Tag Archives: Virtual Private Cloud

Reactive Microservices Architecture on AWS

Post Syndicated from Sascha Moellering original https://aws.amazon.com/blogs/architecture/reactive-microservices-architecture-on-aws/

Microservice-application requirements have changed dramatically in recent years. These days, applications operate with petabytes of data, need almost 100% uptime, and end users expect sub-second response times. Typical N-tier applications can’t deliver on these requirements.

Reactive Manifesto, published in 2014, describes the essential characteristics of reactive systems including: responsiveness, resiliency, elasticity, and being message driven.

Being message driven is perhaps the most important characteristic of reactive systems. Asynchronous messaging helps in the design of loosely coupled systems, which is a key factor for scalability. In order to build a highly decoupled system, it is important to isolate services from each other. As already described, isolation is an important aspect of the microservices pattern. Indeed, reactive systems and microservices are a natural fit.

Implemented Use Case
This reference architecture illustrates a typical ad-tracking implementation.

Many ad-tracking companies collect massive amounts of data in near-real-time. In many cases, these workloads are very spiky and heavily depend on the success of the ad-tech companies’ customers. Typically, an ad-tracking-data use case can be separated into a real-time part and a non-real-time part. In the real-time part, it is important to collect data as fast as possible and ask several questions including:,  “Is this a valid combination of parameters?,””Does this program exist?,” “Is this program still valid?”

Because response time has a huge impact on conversion rate in advertising, it is important for advertisers to respond as fast as possible. This information should be kept in memory to reduce communication overhead with the caching infrastructure. The tracking application itself should be as lightweight and scalable as possible. For example, the application shouldn’t have any shared mutable state and it should use reactive paradigms. In our implementation, one main application is responsible for this real-time part. It collects and validates data, responds to the client as fast as possible, and asynchronously sends events to backend systems.

The non-real-time part of the application consumes the generated events and persists them in a NoSQL database. In a typical tracking implementation, clicks, cookie information, and transactions are matched asynchronously and persisted in a data store. The matching part is not implemented in this reference architecture. Many ad-tech architectures use frameworks like Hadoop for the matching implementation.

The system can be logically divided into the data collection partand the core data updatepart. The data collection part is responsible for collecting, validating, and persisting the data. In the core data update part, the data that is used for validation gets updated and all subscribers are notified of new data.

Components and Services

Main Application
The main application is implemented using Java 8 and uses Vert.x as the main framework. Vert.x is an event-driven, reactive, non-blocking, polyglot framework to implement microservices. It runs on the Java virtual machine (JVM) by using the low-level IO library Netty. You can write applications in Java, JavaScript, Groovy, Ruby, Kotlin, Scala, and Ceylon. The framework offers a simple and scalable actor-like concurrency model. Vert.x calls handlers by using a thread known as an event loop. To use this model, you have to write code known as “verticles.” Verticles share certain similarities with actors in the actor model. To use them, you have to implement the verticle interface. Verticles communicate with each other by generating messages in  a single event bus. Those messages are sent on the event bus to a specific address, and verticles can register to this address by using handlers.

With only a few exceptions, none of the APIs in Vert.x block the calling thread. Similar to Node.js, Vert.x uses the reactor pattern. However, in contrast to Node.js, Vert.x uses several event loops. Unfortunately, not all APIs in the Java ecosystem are written asynchronously, for example, the JDBC API. Vert.x offers a possibility to run this, blocking APIs without blocking the event loop. These special verticles are called worker verticles. You don’t execute worker verticles by using the standard Vert.x event loops, but by using a dedicated thread from a worker pool. This way, the worker verticles don’t block the event loop.

Our application consists of five different verticles covering different aspects of the business logic. The main entry point for our application is the HttpVerticle, which exposes an HTTP-endpoint to consume HTTP-requests and for proper health checking. Data from HTTP requests such as parameters and user-agent information are collected and transformed into a JSON message. In order to validate the input data (to ensure that the program exists and is still valid), the message is sent to the CacheVerticle.

This verticle implements an LRU-cache with a TTL of 10 minutes and a capacity of 100,000 entries. Instead of adding additional functionality to a standard JDK map implementation, we use Google Guava, which has all the features we need. If the data is not in the L1 cache, the message is sent to the RedisVerticle. This verticle is responsible for data residing in Amazon ElastiCache and uses the Vert.x-redis-client to read data from Redis. In our example, Redis is the central data store. However, in a typical production implementation, Redis would just be the L2 cache with a central data store like Amazon DynamoDB. One of the most important paradigms of a reactive system is to switch from a pull- to a push-based model. To achieve this and reduce network overhead, we’ll use Redis pub/sub to push core data changes to our main application.

Vert.x also supports direct Redis pub/sub-integration, the following code shows our subscriber-implementation:

vertx.eventBus().<JsonObject>consumer(REDIS_PUBSUB_CHANNEL_VERTX, received -> {

JsonObject value = received.body().getJsonObject("value");

String message = value.getString("message");

JsonObject jsonObject = new JsonObject(message);

eb.send(CACHE_REDIS_EVENTBUS_ADDRESS, jsonObject);

});

redis.subscribe(Constants.REDIS_PUBSUB_CHANNEL, res -> {

if (res.succeeded()) {

LOGGER.info("Subscribed to " + Constants.REDIS_PUBSUB_CHANNEL);

} else {

LOGGER.info(res.cause());

}

});

The verticle subscribes to the appropriate Redis pub/sub-channel. If a message is sent over this channel, the payload is extracted and forwarded to the cache-verticle that stores the data in the L1-cache. After storing and enriching data, a response is sent back to the HttpVerticle, which responds to the HTTP request that initially hit this verticle. In addition, the message is converted to ByteBuffer, wrapped in protocol buffers, and send to an Amazon Kinesis Data Stream.

The following example shows a stripped-down version of the KinesisVerticle:

public class KinesisVerticle extends AbstractVerticle {

private static final Logger LOGGER = LoggerFactory.getLogger(KinesisVerticle.class);

private AmazonKinesisAsync kinesisAsyncClient;

private String eventStream = "EventStream";

@Override

public void start() throws Exception {

EventBus eb = vertx.eventBus();

kinesisAsyncClient = createClient();

eventStream = System.getenv(STREAM_NAME) == null ? "EventStream" : System.getenv(STREAM_NAME);

eb.consumer(Constants.KINESIS_EVENTBUS_ADDRESS, message -> {

try {

TrackingMessage trackingMessage = Json.decodeValue((String)message.body(), TrackingMessage.class);

String partitionKey = trackingMessage.getMessageId();

byte [] byteMessage = createMessage(trackingMessage);

ByteBuffer buf = ByteBuffer.wrap(byteMessage);

sendMessageToKinesis(buf, partitionKey);

message.reply("OK");

}

catch (KinesisException exc) {

LOGGER.error(exc);

}

});

}

Kinesis Consumer
This AWS Lambda function consumes data from an Amazon Kinesis Data Stream and persists the data in an Amazon DynamoDB table. In order to improve testability, the invocation code is separated from the business logic. The invocation code is implemented in the class KinesisConsumerHandler and iterates over the Kinesis events pulled from the Kinesis stream by AWS Lambda. Each Kinesis event is unwrapped and transformed from ByteBuffer to protocol buffers and converted into a Java object. Those Java objects are passed to the business logic, which persists the data in a DynamoDB table. In order to improve duration of successive Lambda calls, the DynamoDB-client is instantiated lazily and reused if possible.

Redis Updater
From time to time, it is necessary to update core data in Redis. A very efficient implementation for this requirement is using AWS Lambda and Amazon Kinesis. New core data is sent over the AWS Kinesis stream using JSON as data format and consumed by a Lambda function. This function iterates over the Kinesis events pulled from the Kinesis stream by AWS Lambda. Each Kinesis event is unwrapped and transformed from ByteBuffer to String and converted into a Java object. The Java object is passed to the business logic and stored in Redis. In addition, the new core data is also sent to the main application using Redis pub/sub in order to reduce network overhead and converting from a pull- to a push-based model.

The following example shows the source code to store data in Redis and notify all subscribers:

public void updateRedisData(final TrackingMessage trackingMessage, final Jedis jedis, final LambdaLogger logger) {

try {

ObjectMapper mapper = new ObjectMapper();

String jsonString = mapper.writeValueAsString(trackingMessage);

Map<String, String> map = marshal(jsonString);

String statusCode = jedis.hmset(trackingMessage.getProgramId(), map);

}

catch (Exception exc) {

if (null == logger)

exc.printStackTrace();

else

logger.log(exc.getMessage());

}

}

public void notifySubscribers(final TrackingMessage trackingMessage, final Jedis jedis, final LambdaLogger logger) {

try {

ObjectMapper mapper = new ObjectMapper();

String jsonString = mapper.writeValueAsString(trackingMessage);

jedis.publish(Constants.REDIS_PUBSUB_CHANNEL, jsonString);

}

catch (final IOException e) {

log(e.getMessage(), logger);

}

}

Similarly to our Kinesis Consumer, the Redis-client is instantiated somewhat lazily.

Infrastructure as Code
As already outlined, latency and response time are a very critical part of any ad-tracking solution because response time has a huge impact on conversion rate. In order to reduce latency for customers world-wide, it is common practice to roll out the infrastructure in different AWS Regions in the world to be as close to the end customer as possible. AWS CloudFormation can help you model and set up your AWS resources so that you can spend less time managing those resources and more time focusing on your applications that run in AWS.

You create a template that describes all the AWS resources that you want (for example, Amazon EC2 instances or Amazon RDS DB instances), and AWS CloudFormation takes care of provisioning and configuring those resources for you. Our reference architecture can be rolled out in different Regions using an AWS CloudFormation template, which sets up the complete infrastructure (for example, Amazon Virtual Private Cloud (Amazon VPC), Amazon Elastic Container Service (Amazon ECS) cluster, Lambda functions, DynamoDB table, Amazon ElastiCache cluster, etc.).

Conclusion
In this blog post we described reactive principles and an example architecture with a common use case. We leveraged the capabilities of different frameworks in combination with several AWS services in order to implement reactive principles—not only at the application-level but also at the system-level. I hope I’ve given you ideas for creating your own reactive applications and systems on AWS.

About the Author

Sascha Moellering is a Senior Solution Architect. Sascha is primarily interested in automation, infrastructure as code, distributed computing, containers and JVM. He can be reached at [email protected]

 

 

AWS Direct Connect Update – Ten New Locations Added in Late 2017

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-direct-connect-update-ten-new-locations-added-in-late-2017/

Happy 2018! I am looking forward to getting back to my usual routine, working with our teams to learn about their upcoming launches and then writing blog posts to bring the news to you. Right now I am still catching up on a few launches and announcements from late 2017.

First on the list for today is our most recent round of new cities for AWS Direct Connect. AWS customers all over the world use Direct Connect to create dedicated network connections from their premises to AWS in order to reduce their network costs, increase throughput, and to pursue a more consistent network experience.

We added ten new locations to our Direct Connect roster in December, all of which offer both 1 Gbps and 10 Gbps connectivity, along with partner-supplied options for speeds below 1 Gbps. Here are the newest locations, along withe the data centers and associated AWS Regions:

  • Bangalore, India – NetMagic DC2Asia Pacific (Mumbai).
  • Cape Town, South Africa – Teraco Ct1EU (Ireland).
  • Johannesburg, South Africa – Teraco JB1EU (Ireland).
  • London, UK – Telehouse North TwoEU (London).
  • Miami, Florida, US – Equinix MI1US East (Northern Virginia).
  • Minneapolis, Minnesota, US – Cologix MIN3US East (Ohio)
  • Ningxia, China – Shapotou IDC – China (Ningxia).
  • Ningxia, China – Industrial Park IDC – China (Ningxia).
  • Rio de Janeiro, Brazil – Equinix RJ2South America (São Paulo).
  • Tokyo, Japan – AT Tokyo ChuoAsia Pacific (Tokyo).

You can use these new locations in conjunction with the AWS Direct Connect Gateway to set up connectivity that spans Virtual Private Clouds (VPCs) spread across multiple AWS Regions (this does not apply to the AWS Regions in China).

If you are interested in putting Direct Connect to use, be sure to check out our ever-growing list of Direct Connect Partners.

Jeff;

Now Open AWS EU (Paris) Region

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-open-aws-eu-paris-region/

Today we are launching our 18th AWS Region, our fourth in Europe. Located in the Paris area, AWS customers can use this Region to better serve customers in and around France.

The Details
The new EU (Paris) Region provides a broad suite of AWS services including Amazon API Gateway, Amazon Aurora, Amazon CloudFront, Amazon CloudWatch, CloudWatch Events, Amazon CloudWatch Logs, Amazon DynamoDB, Amazon Elastic Compute Cloud (EC2), EC2 Container Registry, Amazon ECS, Amazon Elastic Block Store (EBS), Amazon EMR, Amazon ElastiCache, Amazon Elasticsearch Service, Amazon Glacier, Amazon Kinesis Streams, Polly, Amazon Redshift, Amazon Relational Database Service (RDS), Amazon Route 53, Amazon Simple Notification Service (SNS), Amazon Simple Queue Service (SQS), Amazon Simple Storage Service (S3), Amazon Simple Workflow Service (SWF), Amazon Virtual Private Cloud, Auto Scaling, AWS Certificate Manager (ACM), AWS CloudFormation, AWS CloudTrail, AWS CodeDeploy, AWS Config, AWS Database Migration Service, AWS Direct Connect, AWS Elastic Beanstalk, AWS Identity and Access Management (IAM), AWS Key Management Service (KMS), AWS Lambda, AWS Marketplace, AWS OpsWorks Stacks, AWS Personal Health Dashboard, AWS Server Migration Service, AWS Service Catalog, AWS Shield Standard, AWS Snowball, AWS Snowball Edge, AWS Snowmobile, AWS Storage Gateway, AWS Support (including AWS Trusted Advisor), Elastic Load Balancing, and VM Import.

The Paris Region supports all sizes of C5, M5, R4, T2, D2, I3, and X1 instances.

There are also four edge locations for Amazon Route 53 and Amazon CloudFront: three in Paris and one in Marseille, all with AWS WAF and AWS Shield. Check out the AWS Global Infrastructure page to learn more about current and future AWS Regions.

The Paris Region will benefit from three AWS Direct Connect locations. Telehouse Voltaire is available today. AWS Direct Connect will also become available at Equinix Paris in early 2018, followed by Interxion Paris.

All AWS infrastructure regions around the world are designed, built, and regularly audited to meet the most rigorous compliance standards and to provide high levels of security for all AWS customers. These include ISO 27001, ISO 27017, ISO 27018, SOC 1 (Formerly SAS 70), SOC 2 and SOC 3 Security & Availability, PCI DSS Level 1, and many more. This means customers benefit from all the best practices of AWS policies, architecture, and operational processes built to satisfy the needs of even the most security sensitive customers.

AWS is certified under the EU-US Privacy Shield, and the AWS Data Processing Addendum (DPA) is GDPR-ready and available now to all AWS customers to help them prepare for May 25, 2018 when the GDPR becomes enforceable. The current AWS DPA, as well as the AWS GDPR DPA, allows customers to transfer personal data to countries outside the European Economic Area (EEA) in compliance with European Union (EU) data protection laws. AWS also adheres to the Cloud Infrastructure Service Providers in Europe (CISPE) Code of Conduct. The CISPE Code of Conduct helps customers ensure that AWS is using appropriate data protection standards to protect their data, consistent with the GDPR. In addition, AWS offers a wide range of services and features to help customers meet the requirements of the GDPR, including services for access controls, monitoring, logging, and encryption.

From Our Customers
Many AWS customers are preparing to use this new Region. Here’s a small sample:

Societe Generale, one of the largest banks in France and the world, has accelerated their digital transformation while working with AWS. They developed SG Research, an application that makes reports from Societe Generale’s analysts available to corporate customers in order to improve the decision-making process for investments. The new AWS Region will reduce latency between applications running in the cloud and in their French data centers.

SNCF is the national railway company of France. Their mobile app, powered by AWS, delivers real-time traffic information to 14 million riders. Extreme weather, traffic events, holidays, and engineering works can cause usage to peak at hundreds of thousands of users per second. They are planning to use machine learning and big data to add predictive features to the app.

Radio France, the French public radio broadcaster, offers seven national networks, and uses AWS to accelerate its innovation and stay competitive.

Les Restos du Coeur, a French charity that provides assistance to the needy, delivering food packages and participating in their social and economic integration back into French society. Les Restos du Coeur is using AWS for its CRM system to track the assistance given to each of their beneficiaries and the impact this is having on their lives.

AlloResto by JustEat (a leader in the French FoodTech industry), is using AWS to to scale during traffic peaks and to accelerate their innovation process.

AWS Consulting and Technology Partners
We are already working with a wide variety of consulting, technology, managed service, and Direct Connect partners in France. Here’s a partial list:

AWS Premier Consulting PartnersAccenture, Capgemini, Claranet, CloudReach, DXC, and Edifixio.

AWS Consulting PartnersABC Systemes, Atos International SAS, CoreExpert, Cycloid, Devoteam, LINKBYNET, Oxalide, Ozones, Scaleo Information Systems, and Sopra Steria.

AWS Technology PartnersAxway, Commerce Guys, MicroStrategy, Sage, Software AG, Splunk, Tibco, and Zerolight.

AWS in France
We have been investing in Europe, with a focus on France, for the last 11 years. We have also been developing documentation and training programs to help our customers to improve their skills and to accelerate their journey to the AWS Cloud.

As part of our commitment to AWS customers in France, we plan to train more than 25,000 people in the coming years, helping them develop highly sought after cloud skills. They will have access to AWS training resources in France via AWS Academy, AWSome days, AWS Educate, and webinars, all delivered in French by AWS Technical Trainers and AWS Certified Trainers.

Use it Today
The EU (Paris) Region is open for business now and you can start using it today!

Jeff;

 

Power data ingestion into Splunk using Amazon Kinesis Data Firehose

Post Syndicated from Tarik Makota original https://aws.amazon.com/blogs/big-data/power-data-ingestion-into-splunk-using-amazon-kinesis-data-firehose/

In late September, during the annual Splunk .conf, Splunk and Amazon Web Services (AWS) jointly announced that Amazon Kinesis Data Firehose now supports Splunk Enterprise and Splunk Cloud as a delivery destination. This native integration between Splunk Enterprise, Splunk Cloud, and Amazon Kinesis Data Firehose is designed to make AWS data ingestion setup seamless, while offering a secure and fault-tolerant delivery mechanism. We want to enable customers to monitor and analyze machine data from any source and use it to deliver operational intelligence and optimize IT, security, and business performance.

With Kinesis Data Firehose, customers can use a fully managed, reliable, and scalable data streaming solution to Splunk. In this post, we tell you a bit more about the Kinesis Data Firehose and Splunk integration. We also show you how to ingest large amounts of data into Splunk using Kinesis Data Firehose.

Push vs. Pull data ingestion

Presently, customers use a combination of two ingestion patterns, primarily based on data source and volume, in addition to existing company infrastructure and expertise:

  1. Pull-based approach: Using dedicated pollers running the popular Splunk Add-on for AWS to pull data from various AWS services such as Amazon CloudWatch or Amazon S3.
  2. Push-based approach: Streaming data directly from AWS to Splunk HTTP Event Collector (HEC) by using AWS Lambda. Examples of applicable data sources include CloudWatch Logs and Amazon Kinesis Data Streams.

The pull-based approach offers data delivery guarantees such as retries and checkpointing out of the box. However, it requires more ops to manage and orchestrate the dedicated pollers, which are commonly running on Amazon EC2 instances. With this setup, you pay for the infrastructure even when it’s idle.

On the other hand, the push-based approach offers a low-latency scalable data pipeline made up of serverless resources like AWS Lambda sending directly to Splunk indexers (by using Splunk HEC). This approach translates into lower operational complexity and cost. However, if you need guaranteed data delivery then you have to design your solution to handle issues such as a Splunk connection failure or Lambda execution failure. To do so, you might use, for example, AWS Lambda Dead Letter Queues.

How about getting the best of both worlds?

Let’s go over the new integration’s end-to-end solution and examine how Kinesis Data Firehose and Splunk together expand the push-based approach into a native AWS solution for applicable data sources.

By using a managed service like Kinesis Data Firehose for data ingestion into Splunk, we provide out-of-the-box reliability and scalability. One of the pain points of the old approach was the overhead of managing the data collection nodes (Splunk heavy forwarders). With the new Kinesis Data Firehose to Splunk integration, there are no forwarders to manage or set up. Data producers (1) are configured through the AWS Management Console to drop data into Kinesis Data Firehose.

You can also create your own data producers. For example, you can drop data into a Firehose delivery stream by using Amazon Kinesis Agent, or by using the Firehose API (PutRecord(), PutRecordBatch()), or by writing to a Kinesis Data Stream configured to be the data source of a Firehose delivery stream. For more details, refer to Sending Data to an Amazon Kinesis Data Firehose Delivery Stream.

You might need to transform the data before it goes into Splunk for analysis. For example, you might want to enrich it or filter or anonymize sensitive data. You can do so using AWS Lambda. In this scenario, Kinesis Data Firehose buffers data from the incoming source data, sends it to the specified Lambda function (2), and then rebuffers the transformed data to the Splunk Cluster. Kinesis Data Firehose provides the Lambda blueprints that you can use to create a Lambda function for data transformation.

Systems fail all the time. Let’s see how this integration handles outside failures to guarantee data durability. In cases when Kinesis Data Firehose can’t deliver data to the Splunk Cluster, data is automatically backed up to an S3 bucket. You can configure this feature while creating the Firehose delivery stream (3). You can choose to back up all data or only the data that’s failed during delivery to Splunk.

In addition to using S3 for data backup, this Firehose integration with Splunk supports Splunk Indexer Acknowledgments to guarantee event delivery. This feature is configured on Splunk’s HTTP Event Collector (HEC) (4). It ensures that HEC returns an acknowledgment to Kinesis Data Firehose only after data has been indexed and is available in the Splunk cluster (5).

Now let’s look at a hands-on exercise that shows how to forward VPC flow logs to Splunk.

How-to guide

To process VPC flow logs, we implement the following architecture.

Amazon Virtual Private Cloud (Amazon VPC) delivers flow log files into an Amazon CloudWatch Logs group. Using a CloudWatch Logs subscription filter, we set up real-time delivery of CloudWatch Logs to an Kinesis Data Firehose stream.

Data coming from CloudWatch Logs is compressed with gzip compression. To work with this compression, we need to configure a Lambda-based data transformation in Kinesis Data Firehose to decompress the data and deposit it back into the stream. Firehose then delivers the raw logs to the Splunk Http Event Collector (HEC).

If delivery to the Splunk HEC fails, Firehose deposits the logs into an Amazon S3 bucket. You can then ingest the events from S3 using an alternate mechanism such as a Lambda function.

When data reaches Splunk (Enterprise or Cloud), Splunk parsing configurations (packaged in the Splunk Add-on for Kinesis Data Firehose) extract and parse all fields. They make data ready for querying and visualization using Splunk Enterprise and Splunk Cloud.

Walkthrough

Install the Splunk Add-on for Amazon Kinesis Data Firehose

The Splunk Add-on for Amazon Kinesis Data Firehose enables Splunk (be it Splunk Enterprise, Splunk App for AWS, or Splunk Enterprise Security) to use data ingested from Amazon Kinesis Data Firehose. Install the Add-on on all the indexers with an HTTP Event Collector (HEC). The Add-on is available for download from Splunkbase.

HTTP Event Collector (HEC)

Before you can use Kinesis Data Firehose to deliver data to Splunk, set up the Splunk HEC to receive the data. From Splunk web, go to the Setting menu, choose Data Inputs, and choose HTTP Event Collector. Choose Global Settings, ensure All tokens is enabled, and then choose Save. Then choose New Token to create a new HEC endpoint and token. When you create a new token, make sure that Enable indexer acknowledgment is checked.

When prompted to select a source type, select aws:cloudwatch:vpcflow.

Create an S3 backsplash bucket

To provide for situations in which Kinesis Data Firehose can’t deliver data to the Splunk Cluster, we use an S3 bucket to back up the data. You can configure this feature to back up all data or only the data that’s failed during delivery to Splunk.

Note: Bucket names are unique. Thus, you can’t use tmak-backsplash-bucket.

aws s3 create-bucket --bucket tmak-backsplash-bucket --create-bucket-configuration LocationConstraint=ap-northeast-1

Create an IAM role for the Lambda transform function

Firehose triggers an AWS Lambda function that transforms the data in the delivery stream. Let’s first create a role for the Lambda function called LambdaBasicRole.

Note: You can also set this role up when creating your Lambda function.

$ aws iam create-role --role-name LambdaBasicRole --assume-role-policy-document file://TrustPolicyForLambda.json

Here is TrustPolicyForLambda.json.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "lambda.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

 

After the role is created, attach the managed Lambda basic execution policy to it.

$ aws iam attach-role-policy 
  --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole 
  --role-name LambdaBasicRole

 

Create a Firehose Stream

On the AWS console, open the Amazon Kinesis service, go to the Firehose console, and choose Create Delivery Stream.

In the next section, you can specify whether you want to use an inline Lambda function for transformation. Because incoming CloudWatch Logs are gzip compressed, choose Enabled for Record transformation, and then choose Create new.

From the list of the available blueprint functions, choose Kinesis Data Firehose CloudWatch Logs Processor. This function unzips data and place it back into the Firehose stream in compliance with the record transformation output model.

Enter a name for the Lambda function, choose Choose an existing role, and then choose the role you created earlier. Then choose Create Function.

Go back to the Firehose Stream wizard, choose the Lambda function you just created, and then choose Next.

Select Splunk as the destination, and enter your Splunk Http Event Collector information.

Note: Amazon Kinesis Data Firehose requires the Splunk HTTP Event Collector (HEC) endpoint to be terminated with a valid CA-signed certificate matching the DNS hostname used to connect to your HEC endpoint. You receive delivery errors if you are using a self-signed certificate.

In this example, we only back up logs that fail during delivery.

To monitor your Firehose delivery stream, enable error logging. Doing this means that you can monitor record delivery errors.

Create an IAM role for the Firehose stream by choosing Create new, or Choose. Doing this brings you to a new screen. Choose Create a new IAM role, give the role a name, and then choose Allow.

If you look at the policy document, you can see that the role gives Kinesis Data Firehose permission to publish error logs to CloudWatch, execute your Lambda function, and put records into your S3 backup bucket.

You now get a chance to review and adjust the Firehose stream settings. When you are satisfied, choose Create Stream. You get a confirmation once the stream is created and active.

Create a VPC Flow Log

To send events from Amazon VPC, you need to set up a VPC flow log. If you already have a VPC flow log you want to use, you can skip to the “Publish CloudWatch to Kinesis Data Firehose” section.

On the AWS console, open the Amazon VPC service. Then choose VPC, Your VPC, and choose the VPC you want to send flow logs from. Choose Flow Logs, and then choose Create Flow Log. If you don’t have an IAM role that allows your VPC to publish logs to CloudWatch, choose Set Up Permissions and Create new role. Use the defaults when presented with the screen to create the new IAM role.

Once active, your VPC flow log should look like the following.

Publish CloudWatch to Kinesis Data Firehose

When you generate traffic to or from your VPC, the log group is created in Amazon CloudWatch. The new log group has no subscription filter, so set up a subscription filter. Setting this up establishes a real-time data feed from the log group to your Firehose delivery stream.

At present, you have to use the AWS Command Line Interface (AWS CLI) to create a CloudWatch Logs subscription to a Kinesis Data Firehose stream. However, you can use the AWS console to create subscriptions to Lambda and Amazon Elasticsearch Service.

To allow CloudWatch to publish to your Firehose stream, you need to give it permissions.

$ aws iam create-role --role-name CWLtoKinesisFirehoseRole --assume-role-policy-document file://TrustPolicyForCWLToFireHose.json


Here is the content for TrustPolicyForCWLToFireHose.json.

{
  "Statement": {
    "Effect": "Allow",
    "Principal": { "Service": "logs.us-east-1.amazonaws.com" },
    "Action": "sts:AssumeRole"
  }
}

 

Attach the policy to the newly created role.

$ aws iam put-role-policy 
    --role-name CWLtoKinesisFirehoseRole 
    --policy-name Permissions-Policy-For-CWL 
    --policy-document file://PermissionPolicyForCWLToFireHose.json

Here is the content for PermissionPolicyForCWLToFireHose.json.

{
    "Statement":[
      {
        "Effect":"Allow",
        "Action":["firehose:*"],
        "Resource":["arn:aws:firehose:us-east-1:YOUR-AWS-ACCT-NUM:deliverystream/ FirehoseSplunkDeliveryStream"]
      },
      {
        "Effect":"Allow",
        "Action":["iam:PassRole"],
        "Resource":["arn:aws:iam::YOUR-AWS-ACCT-NUM:role/CWLtoKinesisFirehoseRole"]
      }
    ]
}

Finally, create a subscription filter.

$ aws logs put-subscription-filter 
   --log-group-name " /vpc/flowlog/FirehoseSplunkDemo" 
   --filter-name "Destination" 
   --filter-pattern "" 
   --destination-arn "arn:aws:firehose:us-east-1:YOUR-AWS-ACCT-NUM:deliverystream/FirehoseSplunkDeliveryStream" 
   --role-arn "arn:aws:iam::YOUR-AWS-ACCT-NUM:role/CWLtoKinesisFirehoseRole"

When you run the AWS CLI command preceding, you don’t get any acknowledgment. To validate that your CloudWatch Log Group is subscribed to your Firehose stream, check the CloudWatch console.

As soon as the subscription filter is created, the real-time log data from the log group goes into your Firehose delivery stream. Your stream then delivers it to your Splunk Enterprise or Splunk Cloud environment for querying and visualization. The screenshot following is from Splunk Enterprise.

In addition, you can monitor and view metrics associated with your delivery stream using the AWS console.

Conclusion

Although our walkthrough uses VPC Flow Logs, the pattern can be used in many other scenarios. These include ingesting data from AWS IoT, other CloudWatch logs and events, Kinesis Streams or other data sources using the Kinesis Agent or Kinesis Producer Library. We also used Lambda blueprint Kinesis Data Firehose CloudWatch Logs Processor to transform streaming records from Kinesis Data Firehose. However, you might need to use a different Lambda blueprint or disable record transformation entirely depending on your use case. For an additional use case using Kinesis Data Firehose, check out This is My Architecture Video, which discusses how to securely centralize cross-account data analytics using Kinesis and Splunk.

 


Additional Reading

If you found this post useful, be sure to check out Integrating Splunk with Amazon Kinesis Streams and Using Amazon EMR and Hunk for Rapid Response Log Analysis and Review.


About the Authors

Tarik Makota is a solutions architect with the Amazon Web Services Partner Network. He provides technical guidance, design advice and thought leadership to AWS’ most strategic software partners. His career includes work in an extremely broad software development and architecture roles across ERP, financial printing, benefit delivery and administration and financial services. He holds an M.S. in Software Development and Management from Rochester Institute of Technology.

 

 

 

Roy Arsan is a solutions architect in the Splunk Partner Integrations team. He has a background in product development, cloud architecture, and building consumer and enterprise cloud applications. More recently, he has architected Splunk solutions on major cloud providers, including an AWS Quick Start for Splunk that enables AWS users to easily deploy distributed Splunk Enterprise straight from their AWS console. He’s also the co-author of the AWS Lambda blueprints for Splunk. He holds an M.S. in Computer Science Engineering from the University of Michigan.

 

 

 

Introducing the New GDPR Center and “Navigating GDPR Compliance on AWS” Whitepaper

Post Syndicated from Chad Woolf original https://aws.amazon.com/blogs/security/introducing-the-new-gdpr-center-and-navigating-gdpr-compliance-on-aws-whitepaper/

European Union flag

At AWS re:Invent 2017, the AWS Compliance team participated in excellent engagements with AWS customers about the General Data Protection Regulation (GDPR), including discussions that generated helpful input. Today, I am announcing resulting enhancements to our recently launched GDPR Center and the release of a new whitepaper, Navigating GDPR Compliance on AWS. The resources available on the GDPR Center are designed to give you GDPR basics, and provide some ideas as you work out the details of the regulation and find a path to compliance.

In this post, I focus on two of these new GDPR requirements in terms of articles in the GDPR, and explain some of the AWS services and other resources that can help you meet these requirements.

Background about the GDPR

The GDPR is a European privacy law that will become enforceable on May 25, 2018, and is intended to harmonize data protection laws throughout the European Union (EU) by applying a single data protection law that is binding throughout each EU member state. The GDPR not only applies to organizations located within the EU, but also to organizations located outside the EU if they offer goods or services to, or monitor the behavior of, EU data subjects. All AWS services will comply with the GDPR in advance of the May 25, 2018, enforcement date.

We are already seeing customers move personal data to AWS to help solve challenges in complying with the EU’s GDPR because of AWS’s advanced toolset for identifying, securing, and managing all types of data, including personal data. Steve Schmidt, the AWS CISO, has already written about the internal and external work we have been undertaking to help you use AWS services to meet your own GDPR compliance goals.

Article 25 – Data Protection by Design and by Default (Privacy by Design)

Privacy by Design is the integration of data privacy and compliance into the systems development process, enabling applications, systems, and accounts, among other things, to be secure by default. To secure your AWS account, we offer a script to evaluate your AWS account against the full Center for Internet Security (CIS) Amazon Web Services Foundations Benchmark 1.1. You can access this public benchmark on GitHub. Additionally, AWS Trusted Advisor is an online resource to help you improve security by optimizing your AWS environment. Among other things, Trusted Advisor lists a number of security-related controls you should be monitoring. AWS also offers AWS CloudTrail, a logging tool to track usage and API activity. Another example of tooling that enables data protection is Amazon Inspector, which includes a knowledge base of hundreds of rules (regularly updated by AWS security researchers) mapped to common security best practices and vulnerability definitions. Examples of built-in rules include checking for remote root login being enabled or vulnerable software versions installed. These and other tools enable you to design an environment that protects customer data by design.

An accurate inventory of all the GDPR-impacting data is important but sometimes difficult to assess. AWS has some advanced tooling, such as Amazon Macie, to help you determine where customer data is present in your AWS resources. Macie uses advanced machine learning to automatically discover and classify data so that you can protect data, per Article 25.

Article 32 – Security of Processing

You can use many AWS services and features to secure the processing of data regulated by the GDPR. Amazon Virtual Private Cloud (Amazon VPC) lets you provision a logically isolated section of the AWS Cloud where you can launch resources in a virtual network that you define. You have complete control over your virtual networking environment, including the selection of your own IP address range, creation of subnets, and configuration of route tables and network gateways. With Amazon VPC, you can make the Amazon Cloud a seamless extension of your existing on-premises resources.

AWS Key Management Service (AWS KMS) is a managed service that makes it easy for you to create and control the encryption keys used to encrypt your data, and uses hardware security modules (HSMs) to help protect your keys. Managing keys with AWS KMS allows you to choose to encrypt data either on the server side or the client side. AWS KMS is integrated with several other AWS services to help you protect the data you store with these services. AWS KMS is also integrated with CloudTrail to provide you with logs of all key usage to help meet your regulatory and compliance needs. You can also use the AWS Encryption SDK to correctly generate and use encryption keys, as well as protect keys after they have been used.

We also recently announced new encryption and security features for Amazon S3, including default encryption and a detailed inventory report. Services of this type as well as additional GDPR enablers will be published regularly on our GDPR Center.

Other resources

As you prepare for GDPR, you may want to visit our AWS Customer Compliance Center or Tools for Amazon Web Services to learn about options for building anything from small scripts that delete data to a full orchestration framework that uses AWS Code services.

-Chad

Now Open – AWS China (Ningxia) Region

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-open-aws-china-ningxia-region/

Today we launched our 17th Region globally, and the second in China. The AWS China (Ningxia) Region, operated by Ningxia Western Cloud Data Technology Co. Ltd. (NWCD), is generally available now and provides customers another option to run applications and store data on AWS in China.

The Details
At launch, the new China (Ningxia) Region, operated by NWCD, supports Auto Scaling, AWS Config, AWS CloudFormation, AWS CloudTrail, Amazon CloudWatch, CloudWatch Events, Amazon CloudWatch Logs, AWS CodeDeploy, AWS Direct Connect, Amazon DynamoDB, Amazon Elastic Compute Cloud (EC2), Amazon Elastic Block Store (EBS), Amazon EC2 Systems Manager, AWS Elastic Beanstalk, Amazon ElastiCache, Amazon Elasticsearch Service, Elastic Load Balancing, Amazon EMR, Amazon Glacier, AWS Identity and Access Management (IAM), Amazon Kinesis Streams, Amazon Redshift, Amazon Relational Database Service (RDS), Amazon Simple Storage Service (S3), Amazon Simple Notification Service (SNS), Amazon Simple Queue Service (SQS), AWS Support API, AWS Trusted Advisor, Amazon Simple Workflow Service (SWF), Amazon Virtual Private Cloud, and VM Import. Visit the AWS China Products page for additional information on these services.

The Region supports all sizes of C4, D2, M4, T2, R4, I3, and X1 instances.

Check out the AWS Global Infrastructure page to learn more about current and future AWS Regions.

Operating Partner
To comply with China’s legal and regulatory requirements, AWS has formed a strategic technology collaboration with NWCD to operate and provide services from the AWS China (Ningxia) Region. Founded in 2015, NWCD is a licensed datacenter and cloud services provider, based in Ningxia, China. NWCD joins Sinnet, the operator of the AWS China China (Beijing) Region, as an AWS operating partner in China. Through these relationships, AWS provides its industry-leading technology, guidance, and expertise to NWCD and Sinnet, while NWCD and Sinnet operate and provide AWS cloud services to local customers. While the cloud services offered in both AWS China Regions are the same as those available in other AWS Regions, the AWS China Regions are different in that they are isolated from all other AWS Regions and operated by AWS’s Chinese partners separately from all other AWS Regions. Customers using the AWS China Regions enter into customer agreements with Sinnet and NWCD, rather than with AWS.

Use it Today
The AWS China (Ningxia) Region, operated by NWCD, is open for business, and you can start using it now! Starting today, Chinese developers, startups, and enterprises, as well as government, education, and non-profit organizations, can leverage AWS to run their applications and store their data in the new AWS China (Ningxia) Region, operated by NWCD. Customers already using the AWS China (Beijing) Region, operated by Sinnet, can select the AWS China (Ningxia) Region directly from the AWS Management Console, while new customers can request an account at www.amazonaws.cn to begin using both AWS China Regions.

Jeff;

 

 

Glenn’s Take on re:Invent Part 2

Post Syndicated from Glenn Gore original https://aws.amazon.com/blogs/architecture/glenns-take-on-reinvent-part-2/

Glenn Gore here, Chief Architect for AWS. I’m in Las Vegas this week — with 43K others — for re:Invent 2017. We’ve got a lot of exciting announcements this week. I’m going to check in to the Architecture blog with my take on what’s interesting about some of the announcements from an cloud architectural perspective. My first post can be found here.

The Media and Entertainment industry has been a rapid adopter of AWS due to the scale, reliability, and low costs of our services. This has enabled customers to create new, online, digital experiences for their viewers ranging from broadcast to streaming to Over-the-Top (OTT) services that can be a combination of live, scheduled, or ad-hoc viewing, while supporting devices ranging from high-def TVs to mobile devices. Creating an end-to-end video service requires many different components often sourced from different vendors with different licensing models, which creates a complex architecture and a complex environment to support operationally.

AWS Media Services
Based on customer feedback, we have developed AWS Media Services to help simplify distribution of video content. AWS Media Services is comprised of five individual services that can either be used together to provide an end-to-end service or individually to work within existing deployments: AWS Elemental MediaConvert, AWS Elemental MediaLive, AWS Elemental MediaPackage, AWS Elemental MediaStore and AWS Elemental MediaTailor. These services can help you with everything from storing content safely and durably to setting up a live-streaming event in minutes without having to be concerned about the underlying infrastructure and scalability of the stream itself.

In my role, I participate in many AWS and industry events and often work with the production and event teams that put these shows together. With all the logistical tasks they have to deal with, the biggest question is often: “Will the live stream work?” Compounding this fear is the reality that, as users, we are also quick to jump on social media and make noise when a live stream drops while we are following along remotely. Worse is when I see event organizers actively selecting not to live stream content because of the risk of failure and and exposure — leading them to decide to take the safe option and not stream at all.

With AWS Media Services addressing many of the issues around putting together a high-quality media service, live streaming, and providing access to a library of content through a variety of mechanisms, I can’t wait to see more event teams use live streaming without the concern and worry I’ve seen in the past. I am excited for what this also means for non-media companies, as video becomes an increasingly common way of sharing information and adding a more personalized touch to internally- and externally-facing content.

AWS Media Services will allow you to focus more on the content and not worry about the platform. Awesome!

Amazon Neptune
As a civilization, we have been developing new ways to record and store information and model the relationships between sets of information for more than a thousand years. Government census data, tax records, births, deaths, and marriages were all recorded on medium ranging from knotted cords in the Inca civilization, clay tablets in ancient Babylon, to written texts in Western Europe during the late Middle Ages.

One of the first challenges of computing was figuring out how to store and work with vast amounts of information in a programmatic way, especially as the volume of information was increasing at a faster rate than ever before. We have seen different generations of how to organize this information in some form of database, ranging from flat files to the Information Management System (IMS) used in the 1960s for the Apollo space program, to the rise of the relational database management system (RDBMS) in the 1970s. These innovations drove a lot of subsequent innovations in information management and application development as we were able to move from thousands of records to millions and billions.

Today, as architects and developers, we have a vast variety of database technologies to select from, which have different characteristics that are optimized for different use cases:

  • Relational databases are well understood after decades of use in the majority of companies who required a database to store information. Amazon Relational Database (Amazon RDS) supports many popular relational database engines such as MySQL, Microsoft SQL Server, PostgreSQL, MariaDB, and Oracle. We have even brought the traditional RDBMS into the cloud world through Amazon Aurora, which provides MySQL and PostgreSQL support with the performance and reliability of commercial-grade databases at 1/10th the cost.
  • Non-relational databases (NoSQL) provided a simpler method of storing and retrieving information that was often faster and more scalable than traditional RDBMS technology. The concept of non-relational databases has existed since the 1960s but really took off in the early 2000s with the rise of web-based applications that required performance and scalability that relational databases struggled with at the time. AWS published this Dynamo whitepaper in 2007, with DynamoDB launching as a service in 2012. DynamoDB has quickly become one of the critical design elements for many of our customers who are building highly-scalable applications on AWS. We continue to innovate with DynamoDB, and this week launched global tables and on-demand backup at re:Invent 2017. DynamoDB excels in a variety of use cases, such as tracking of session information for popular websites, shopping cart information on e-commerce sites, and keeping track of gamers’ high scores in mobile gaming applications, for example.
  • Graph databases focus on the relationship between data items in the store. With a graph database, we work with nodes, edges, and properties to represent data, relationships, and information. Graph databases are designed to make it easy and fast to traverse and retrieve complex hierarchical data models. Graph databases share some concepts from the NoSQL family of databases such as key-value pairs (properties) and the use of a non-SQL query language such as Gremlin. Graph databases are commonly used for social networking, recommendation engines, fraud detection, and knowledge graphs. We released Amazon Neptune to help simplify the provisioning and management of graph databases as we believe that graph databases are going to enable the next generation of smart applications.

A common use case I am hearing every week as I talk to customers is how to incorporate chatbots within their organizations. Amazon Lex and Amazon Polly have made it easy for customers to experiment and build chatbots for a wide range of scenarios, but one of the missing pieces of the puzzle was how to model decision trees and and knowledge graphs so the chatbot could guide the conversation in an intelligent manner.

Graph databases are ideal for this particular use case, and having Amazon Neptune simplifies the deployment of a graph database while providing high performance, scalability, availability, and durability as a managed service. Security of your graph database is critical. To help ensure this, you can store your encrypted data by running AWS in Amazon Neptune within your Amazon Virtual Private Cloud (Amazon VPC) and using encryption at rest integrated with AWS Key Management Service (AWS KMS). Neptune also supports Amazon VPC and AWS Identity and Access Management (AWS IAM) to help further protect and restrict access.

Our customers now have the choice of many different database technologies to ensure that they can optimize each application and service for their specific needs. Just as DynamoDB has unlocked and enabled many new workloads that weren’t possible in relational databases, I can’t wait to see what new innovations and capabilities are enabled from graph databases as they become easier to use through Amazon Neptune.

Look for more on DynamoDB and Amazon S3 from me on Monday.

 

Glenn at Tour de Mont Blanc

 

 

AWS Systems Manager – A Unified Interface for Managing Your Cloud and Hybrid Resources

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/aws-systems-manager/

AWS Systems Manager is a new way to manage your cloud and hybrid IT environments. AWS Systems Manager provides a unified user interface that simplifies resource and application management, shortens the time to detect and resolve operational problems, and makes it easy to operate and manage your infrastructure securely at scale. This service is absolutely packed full of features. It defines a new experience around grouping, visualizing, and reacting to problems using features from products like Amazon EC2 Systems Manager (SSM) to enable rich operations across your resources.

As I said above, there are a lot of powerful features in this service and we won’t be able to dive deep on all of them but it’s easy to go to the console and get started with any of the tools.

Resource Groupings

Resource Groups allow you to create logical groupings of most resources that support tagging like: Amazon Elastic Compute Cloud (EC2) instances, Amazon Simple Storage Service (S3) buckets, Elastic Load Balancing balancers, Amazon Relational Database Service (RDS) instances, Amazon Virtual Private Cloud, Amazon Kinesis streams, Amazon Route 53 zones, and more. Previously, you could use the AWS Console to define resource groupings but AWS Systems Manager provides this new resource group experience via a new console and API. These groupings are a fundamental building block of Systems Manager in that they are frequently the target of various operations you may want to perform like: compliance management, software inventories, patching, and other automations.

You start by defining a group based on tag filters. From there you can view all of the resources in a centralized console. You would typically use these groupings to differentiate between applications, application layers, and environments like production or dev – but you can make your own rules about how to use them as well. If you imagine a typical 3 tier web-app you might have a few EC2 instances, an ELB, a few S3 buckets, and an RDS instance. You can define a grouping for that application and with all of those different resources simultaneously.

Insights

AWS Systems Manager automatically aggregates and displays operational data for each resource group through a dashboard. You no longer need to navigate through multiple AWS consoles to view all of your operational data. You can easily integrate your exiting Amazon CloudWatch dashboards, AWS Config rules, AWS CloudTrail trails, AWS Trusted Advisor notifications, and AWS Personal Health Dashboard performance and availability alerts. You can also easily view your software inventories across your fleet. AWS Systems Manager also provides a compliance dashboard allowing you to see the state of various security controls and patching operations across your fleets.

Acting on Insights

Building on the success of EC2 Systems Manager (SSM), AWS Systems Manager takes all of the features of SSM and provides a central place to access them. These are all the same experiences you would have through SSM with a more accesible console and centralized interface. You can use the resource groups you’ve defined in Systems Manager to visualize and act on groups of resources.

Automation


Automations allow you to define common IT tasks as a JSON document that specify a list of tasks. You can also use community published documents. These documents can be executed through the Console, CLIs, SDKs, scheduled maintenance windows, or triggered based on changes in your infrastructure through CloudWatch events. You can track and log the execution of each step in the documents and prompt for additional approvals. It also allows you to incrementally roll out changes and automatically halt when errors occur. You can start executing an automation directly on a resource group and it will be able to apply itself to the resources that it understands within the group.

Run Command

Run Command is a superior alternative to enabling SSH on your instances. It provides safe, secure remote management of your instances at scale without logging into your servers, replacing the need for SSH bastions or remote powershell. It has granular IAM permissions that allow you to restrict which roles or users can run certain commands.

Patch Manager, Maintenance Windows, and State Manager

I’ve written about Patch Manager before and if you manage fleets of Windows and Linux instances it’s a great way to maintain a common baseline of security across your fleet.

Maintenance windows allow you to schedule instance maintenance and other disruptive tasks for a specific time window.

State Manager allows you to control various server configuration details like anti-virus definitions, firewall settings, and more. You can define policies in the console or run existing scripts, PowerShell modules, or even Ansible playbooks directly from S3 or GitHub. You can query State Manager at any time to view the status of your instance configurations.

Things To Know

There’s some interesting terminology here. We haven’t done the best job of naming things in the past so let’s take a moment to clarify. EC2 Systems Manager (sometimes called SSM) is what you used before today. You can still invoke aws ssm commands. However, AWS Systems Manager builds on and enhances many of the tools provided by EC2 Systems Manager and allows those same tools to be applied to more than just EC2. When you see the phrase “Systems Manager” in the future you should think of AWS Systems Manager and not EC2 Systems Manager.

AWS Systems Manager with all of this useful functionality is provided at no additional charge. It is immediately available in all public AWS regions.

The best part about these services is that even with their tight integrations each one is designed to be used in isolation as well. If you only need one component of these services it’s simple to get started with only that component.

There’s a lot more than I could ever document in this post so I encourage you all to jump into the console and documentation to figure out where you can start using AWS Systems Manager.

Randall

The 10 Most Viewed Security-Related AWS Knowledge Center Articles and Videos for November 2017

Post Syndicated from Maggie Burke original https://aws.amazon.com/blogs/security/the-10-most-viewed-security-related-aws-knowledge-center-articles-and-videos-for-november-2017/

AWS Knowledge Center image

The AWS Knowledge Center helps answer the questions most frequently asked by AWS Support customers. The following 10 Knowledge Center security articles and videos have been the most viewed this month. It’s likely you’ve wondered about a few of these topics yourself, so here’s a chance to learn the answers!

  1. How do I create an AWS Identity and Access Management (IAM) policy to restrict access for an IAM user, group, or role to a particular Amazon Virtual Private Cloud (VPC)?
    Learn how to apply a custom IAM policy to restrict IAM user, group, or role permissions for creating and managing Amazon EC2 instances in a specified VPC.
  2. How do I use an MFA token to authenticate access to my AWS resources through the AWS CLI?
    One IAM best practice is to protect your account and its resources by using a multi-factor authentication (MFA) device. If you plan use the AWS Command Line Interface (CLI) while using an MFA device, you must create a temporary session token.
  3. Can I restrict an IAM user’s EC2 access to specific resources?
    This article demonstrates how to link multiple AWS accounts through AWS Organizations and isolate IAM user groups in their own accounts.
  4. I didn’t receive a validation email for the SSL certificate I requested through AWS Certificate Manager (ACM)—where is it?
    Can’t find your ACM validation emails? Be sure to check the email address to which you requested that ACM send validation emails.
  5. How do I create an IAM policy that has a source IP restriction but still allows users to switch roles in the AWS Management Console?
    Learn how to write an IAM policy that not only includes a source IP restriction but also lets your users switch roles in the console.
  6. How do I allow users from another account to access resources in my account through IAM?
    If you have the 12-digit account number and permissions to create and edit IAM roles and users for both accounts, you can permit specific IAM users to access resources in your account.
  7. What are the differences between a service control policy (SCP) and an IAM policy?
    Learn how to distinguish an SCP from an IAM policy.
  8. How do I share my customer master keys (CMKs) across multiple AWS accounts?
    To grant another account access to your CMKs, create an IAM policy on the secondary account that grants access to use your CMKs.
  9. How do I set up AWS Trusted Advisor notifications?
    Learn how to receive free weekly email notifications from Trusted Advisor.
  10. How do I use AWS Key Management Service (AWS KMS) encryption context to protect the integrity of encrypted data?
    Encryption context name-value pairs used with AWS KMS encryption and decryption operations provide a method for checking ciphertext authenticity. Learn how to use encryption context to help protect your encrypted data.

The AWS Security Blog will publish an updated version of this list regularly going forward. You also can subscribe to the AWS Knowledge Center Videos playlist on YouTube.

– Maggie

How to Patch, Inspect, and Protect Microsoft Windows Workloads on AWS—Part 1

Post Syndicated from Koen van Blijderveen original https://aws.amazon.com/blogs/security/how-to-patch-inspect-and-protect-microsoft-windows-workloads-on-aws-part-1/

Most malware tries to compromise your systems by using a known vulnerability that the maker of the operating system has already patched. To help prevent malware from affecting your systems, two security best practices are to apply all operating system patches to your systems and actively monitor your systems for missing patches. In case you do need to recover from a malware attack, you should make regular backups of your data.

In today’s blog post (Part 1 of a two-part post), I show how to keep your Amazon EC2 instances that run Microsoft Windows up to date with the latest security patches by using Amazon EC2 Systems Manager. Tomorrow in Part 2, I show how to take regular snapshots of your data by using Amazon EBS Snapshot Scheduler and how to use Amazon Inspector to check if your EC2 instances running Microsoft Windows contain any common vulnerabilities and exposures (CVEs).

What you should know first

To follow along with the solution in this post, you need one or more EC2 instances. You may use existing instances or create new instances. For the blog post, I assume this is an EC2 for Microsoft Windows Server 2012 R2 instance installed from the Amazon Machine Images (AMIs). If you are not familiar with how to launch an EC2 instance, see Launching an Instance. I also assume you launched or will launch your instance in a private subnet. A private subnet is not directly accessible via the internet, and access to it requires either a VPN connection to your on-premises network or a jump host in a public subnet (a subnet with access to the internet). You must make sure that the EC2 instance can connect to the internet using a network address translation (NAT) instance or NAT gateway to communicate with Systems Manager and Amazon Inspector. The following diagram shows how you should structure your Amazon Virtual Private Cloud (VPC). You should also be familiar with Restoring an Amazon EBS Volume from a Snapshot and Attaching an Amazon EBS Volume to an Instance.

Later on, you will assign tasks to a maintenance window to patch your instances with Systems Manager. To do this, the AWS Identity and Access Management (IAM) user you are using for this post must have the iam:PassRole permission. This permission allows this IAM user to assign tasks to pass their own IAM permissions to the AWS service. In this example, when you assign a task to a maintenance window, IAM passes your credentials to Systems Manager. This safeguard ensures that the user cannot use the creation of tasks to elevate their IAM privileges because their own IAM privileges limit which tasks they can run against an EC2 instance. You should also authorize your IAM user to use EC2, Amazon Inspector, Amazon CloudWatch, and Systems Manager. You can achieve this by attaching the following AWS managed policies to the IAM user you are using for this example: AmazonInspectorFullAccess, AmazonEC2FullAccess, and AmazonSSMFullAccess.

Architectural overview

The following diagram illustrates the components of this solution’s architecture.

Diagram showing the components of this solution's architecture

For this blog post, Microsoft Windows EC2 is Amazon EC2 for Microsoft Windows Server 2012 R2 instances with attached Amazon Elastic Block Store (Amazon EBS) volumes, which are running in your VPC. These instances may be standalone Windows instances running your Windows workloads, or you may have joined them to an Active Directory domain controller. For instances joined to a domain, you can be using Active Directory running on an EC2 for Windows instance, or you can use AWS Directory Service for Microsoft Active Directory.

Amazon EC2 Systems Manager is a scalable tool for remote management of your EC2 instances. You will use the Systems Manager Run Command to install the Amazon Inspector agent. The agent enables EC2 instances to communicate with the Amazon Inspector service and run assessments, which I explain in detail later in this blog post. You also will create a Systems Manager association to keep your EC2 instances up to date with the latest security patches.

You can use the EBS Snapshot Scheduler to schedule automated snapshots at regular intervals. You will use it to set up regular snapshots of your Amazon EBS volumes. EBS Snapshot Scheduler is a prebuilt solution by AWS that you will deploy in your AWS account. With Amazon EBS snapshots, you pay only for the actual data you store. Snapshots save only the data that has changed since the previous snapshot, which minimizes your cost.

You will use Amazon Inspector to run security assessments on your EC2 for Windows Server instance. In this post, I show how to assess if your EC2 for Windows Server instance is vulnerable to any of the more than 50,000 CVEs registered with Amazon Inspector.

In today’s and tomorrow’s posts, I show you how to:

  1. Launch an EC2 instance with an IAM role, Amazon EBS volume, and tags that Systems Manager and Amazon Inspector will use.
  2. Configure Systems Manager to install the Amazon Inspector agent and patch your EC2 instances.
  3. Take EBS snapshots by using EBS Snapshot Scheduler to automate snapshots based on instance tags.
  4. Use Amazon Inspector to check if your EC2 instances running Microsoft Windows contain any common vulnerabilities and exposures (CVEs).

Step 1: Launch an EC2 instance

In this section, I show you how to launch your EC2 instances so that you can use Systems Manager with the instances and use instance tags with EBS Snapshot Scheduler to automate snapshots. This requires three things:

  • Create an IAM role for Systems Manager before launching your EC2 instance.
  • Launch your EC2 instance with Amazon EBS and the IAM role for Systems Manager.
  • Add tags to instances so that you can automate policies for which instances you take snapshots of and when.

Create an IAM role for Systems Manager

Before launching your EC2 instance, I recommend that you first create an IAM role for Systems Manager, which you will use to update the EC2 instance you will launch. AWS already provides a preconfigured policy that you can use for your new role, and it is called AmazonEC2RoleforSSM.

  1. Sign in to the IAM console and choose Roles in the navigation pane. Choose Create new role.
    Screenshot of choosing "Create role"
  2. In the role-creation workflow, choose AWS service > EC2 > EC2 to create a role for an EC2 instance.
    Screenshot of creating a role for an EC2 instance
  3. Choose the AmazonEC2RoleforSSM policy to attach it to the new role you are creating.
    Screenshot of attaching the AmazonEC2RoleforSSM policy to the new role you are creating
  4. Give the role a meaningful name (I chose EC2SSM) and description, and choose Create role.
    Screenshot of giving the role a name and description

Launch your EC2 instance

To follow along, you need an EC2 instance that is running Microsoft Windows Server 2012 R2 and that has an Amazon EBS volume attached. You can use any existing instance you may have or create a new instance.

When launching your new EC2 instance, be sure that:

  • The operating system is Microsoft Windows Server 2012 R2.
  • You attach at least one Amazon EBS volume to the EC2 instance.
  • You attach the newly created IAM role (EC2SSM).
  • The EC2 instance can connect to the internet through a network address translation (NAT) gateway or a NAT instance.
  • You create the tags shown in the following screenshot (you will use them later).

If you are using an already launched EC2 instance, you can attach the newly created role as described in Easily Replace or Attach an IAM Role to an Existing EC2 Instance by Using the EC2 Console.

Add tags

The final step of configuring your EC2 instances is to add tags. You will use these tags to configure Systems Manager in Step 2 of this blog post and to configure Amazon Inspector in Part 2. For this example, I add a tag key, Patch Group, and set the value to Windows Servers. I could have other groups of EC2 instances that I treat differently by having the same tag key but a different tag value. For example, I might have a collection of other servers with the Patch Group tag key with a value of IAS Servers.

Screenshot of adding tags

Note: You must wait a few minutes until the EC2 instance becomes available before you can proceed to the next section.

At this point, you now have at least one EC2 instance you can use to configure Systems Manager, use EBS Snapshot Scheduler, and use Amazon Inspector.

Note: If you have a large number of EC2 instances to tag, you may want to use the EC2 CreateTags API rather than manually apply tags to each instance.

Step 2: Configure Systems Manager

In this section, I show you how to use Systems Manager to apply operating system patches to your EC2 instances, and how to manage patch compliance.

To start, I will provide some background information about Systems Manager. Then, I will cover how to:

  • Create the Systems Manager IAM role so that Systems Manager is able to perform patch operations.
  • Associate a Systems Manager patch baseline with your instance to define which patches Systems Manager should apply.
  • Define a maintenance window to make sure Systems Manager patches your instance when you tell it to.
  • Monitor patch compliance to verify the patch state of your instances.

Systems Manager is a collection of capabilities that helps you automate management tasks for AWS-hosted instances on EC2 and your on-premises servers. In this post, I use Systems Manager for two purposes: to run remote commands and apply operating system patches. To learn about the full capabilities of Systems Manager, see What Is Amazon EC2 Systems Manager?

Patch management is an important measure to prevent malware from infecting your systems. Most malware attacks look for vulnerabilities that are publicly known and in most cases are already patched by the maker of the operating system. These publicly known vulnerabilities are well documented and therefore easier for an attacker to exploit than having to discover a new vulnerability.

Patches for these new vulnerabilities are available through Systems Manager within hours after Microsoft releases them. There are two prerequisites to use Systems Manager to apply operating system patches. First, you must attach the IAM role you created in the previous section, EC2SSM, to your EC2 instance. Second, you must install the Systems Manager agent on your EC2 instance. If you have used a recent Microsoft Windows Server 2012 R2 AMI published by AWS, Amazon has already installed the Systems Manager agent on your EC2 instance. You can confirm this by logging in to an EC2 instance and looking for Amazon SSM Agent under Programs and Features in Windows. To install the Systems Manager agent on an instance that does not have the agent preinstalled or if you want to use the Systems Manager agent on your on-premises servers, see the documentation about installing the Systems Manager agent. If you forgot to attach the newly created role when launching your EC2 instance or if you want to attach the role to already running EC2 instances, see Attach an AWS IAM Role to an Existing Amazon EC2 Instance by Using the AWS CLI or use the AWS Management Console.

To make sure your EC2 instance receives operating system patches from Systems Manager, you will use the default patch baseline provided and maintained by AWS, and you will define a maintenance window so that you control when your EC2 instances should receive patches. For the maintenance window to be able to run any tasks, you also must create a new role for Systems Manager. This role is a different kind of role than the one you created earlier: Systems Manager will use this role instead of EC2. Earlier we created the EC2SSM role with the AmazonEC2RoleforSSM policy, which allowed the Systems Manager agent on our instance to communicate with the Systems Manager service. Here we need a new role with the policy AmazonSSMMaintenanceWindowRole to make sure the Systems Manager service is able to execute commands on our instance.

Create the Systems Manager IAM role

To create the new IAM role for Systems Manager, follow the same procedure as in the previous section, but in Step 3, choose the AmazonSSMMaintenanceWindowRole policy instead of the previously selected AmazonEC2RoleforSSM policy.

Screenshot of creating the new IAM role for Systems Manager

Finish the wizard and give your new role a recognizable name. For example, I named my role MaintenanceWindowRole.

Screenshot of finishing the wizard and giving your new role a recognizable name

By default, only EC2 instances can assume this new role. You must update the trust policy to enable Systems Manager to assume this role.

To update the trust policy associated with this new role:

  1. Navigate to the IAM console and choose Roles in the navigation pane.
  2. Choose MaintenanceWindowRole and choose the Trust relationships tab. Then choose Edit trust relationship.
  3. Update the policy document by copying the following policy and pasting it in the Policy Document box. As you can see, I have added the ssm.amazonaws.com service to the list of allowed Principals that can assume this role. Choose Update Trust Policy.
    {
       "Version":"2012-10-17",
       "Statement":[
          {
             "Sid":"",
             "Effect":"Allow",
             "Principal":{
                "Service":[
                   "ec2.amazonaws.com",
                   "ssm.amazonaws.com"
               ]
             },
             "Action":"sts:AssumeRole"
          }
       ]
    }

Associate a Systems Manager patch baseline with your instance

Next, you are going to associate a Systems Manager patch baseline with your EC2 instance. A patch baseline defines which patches Systems Manager should apply. You will use the default patch baseline that AWS manages and maintains. Before you can associate the patch baseline with your instance, though, you must determine if Systems Manager recognizes your EC2 instance.

Navigate to the EC2 console, scroll down to Systems Manager Shared Resources in the navigation pane, and choose Managed Instances. Your new EC2 instance should be available there. If your instance is missing from the list, verify the following:

  1. Go to the EC2 console and verify your instance is running.
  2. Select your instance and confirm you attached the Systems Manager IAM role, EC2SSM.
  3. Make sure that you deployed a NAT gateway in your public subnet to ensure your VPC reflects the diagram at the start of this post so that the Systems Manager agent can connect to the Systems Manager internet endpoint.
  4. Check the Systems Manager Agent logs for any errors.

Now that you have confirmed that Systems Manager can manage your EC2 instance, it is time to associate the AWS maintained patch baseline with your EC2 instance:

  1. Choose Patch Baselines under Systems Manager Services in the navigation pane of the EC2 console.
  2. Choose the default patch baseline as highlighted in the following screenshot, and choose Modify Patch Groups in the Actions drop-down.
    Screenshot of choosing Modify Patch Groups in the Actions drop-down
  3. In the Patch group box, enter the same value you entered under the Patch Group tag of your EC2 instance in “Step 1: Configure your EC2 instance.” In this example, the value I enter is Windows Servers. Choose the check mark icon next to the patch group and choose Close.Screenshot of modifying the patch group

Define a maintenance window

Now that you have successfully set up a role and have associated a patch baseline with your EC2 instance, you will define a maintenance window so that you can control when your EC2 instances should receive patches. By creating multiple maintenance windows and assigning them to different patch groups, you can make sure your EC2 instances do not all reboot at the same time. The Patch Group resource tag you defined earlier will determine to which patch group an instance belongs.

To define a maintenance window:

  1. Navigate to the EC2 console, scroll down to Systems Manager Shared Resources in the navigation pane, and choose Maintenance Windows. Choose Create a Maintenance Window.
    Screenshot of starting to create a maintenance window in the Systems Manager console
  2. Select the Cron schedule builder to define the schedule for the maintenance window. In the example in the following screenshot, the maintenance window will start every Saturday at 10:00 P.M. UTC.
  3. To specify when your maintenance window will end, specify the duration. In this example, the four-hour maintenance window will end on the following Sunday morning at 2:00 A.M. UTC (in other words, four hours after it started).
  4. Systems manager completes all tasks that are in process, even if the maintenance window ends. In my example, I am choosing to prevent new tasks from starting within one hour of the end of my maintenance window because I estimated my patch operations might take longer than one hour to complete. Confirm the creation of the maintenance window by choosing Create maintenance window.
    Screenshot of completing all boxes in the maintenance window creation process
  5. After creating the maintenance window, you must register the EC2 instance to the maintenance window so that Systems Manager knows which EC2 instance it should patch in this maintenance window. To do so, choose Register new targets on the Targets tab of your newly created maintenance window. You can register your targets by using the same Patch Group tag you used before to associate the EC2 instance with the AWS-provided patch baseline.
    Screenshot of registering new targets
  6. Assign a task to the maintenance window that will install the operating system patches on your EC2 instance:
    1. Open Maintenance Windows in the EC2 console, select your previously created maintenance window, choose the Tasks tab, and choose Register run command task from the Register new task drop-down.
    2. Choose the AWS-RunPatchBaseline document from the list of available documents.
    3. For Parameters:
      1. For Role, choose the role you created previously (called MaintenanceWindowRole).
      2. For Execute on, specify how many EC2 instances Systems Manager should patch at the same time. If you have a large number of EC2 instances and want to patch all EC2 instances within the defined time, make sure this number is not too low. For example, if you have 1,000 EC2 instances, a maintenance window of 4 hours, and 2 hours’ time for patching, make this number at least 500.
      3. For Stop after, specify after how many errors Systems Manager should stop.
      4. For Operation, choose Install to make sure to install the patches.
        Screenshot of stipulating maintenance window parameters

Now, you must wait for the maintenance window to run at least once according to the schedule you defined earlier. Note that if you don’t want to wait, you can adjust the schedule to run sooner by choosing Edit maintenance window on the Maintenance Windows page of Systems Manager. If your maintenance window has expired, you can check the status of any maintenance tasks Systems Manager has performed on the Maintenance Windows page of Systems Manager and select your maintenance window.

Screenshot of the maintenance window successfully created

Monitor patch compliance

You also can see the overall patch compliance of all EC2 instances that are part of defined patch groups by choosing Patch Compliance under Systems Manager Services in the navigation pane of the EC2 console. You can filter by Patch Group to see how many EC2 instances within the selected patch group are up to date, how many EC2 instances are missing updates, and how many EC2 instances are in an error state.

Screenshot of monitoring patch compliance

In this section, you have set everything up for patch management on your instance. Now you know how to patch your EC2 instance in a controlled manner and how to check if your EC2 instance is compliant with the patch baseline you have defined. Of course, I recommend that you apply these steps to all EC2 instances you manage.

Summary

In Part 1 of this blog post, I have shown how to configure EC2 instances for use with Systems Manager, EBS Snapshot Scheduler, and Amazon Inspector. I also have shown how to use Systems Manager to keep your Microsoft Windows–based EC2 instances up to date. In Part 2 of this blog post tomorrow, I will show how to take regular snapshots of your data by using EBS Snapshot Scheduler and how to use Amazon Inspector to check if your EC2 instances running Microsoft Windows contain any CVEs.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about or issues implementing this solution, start a new thread on the EC2 forum or the Amazon Inspector forum, or contact AWS Support.

– Koen

AWS Achieves FedRAMP JAB Moderate Provisional Authorization for 20 Services in the AWS US East/West Region

Post Syndicated from Chris Gile original https://aws.amazon.com/blogs/security/aws-achieves-fedramp-jab-moderate-authorization-for-20-services-in-us-eastwest/

The AWS US East/West Region has received a Provisional Authority to Operate (P-ATO) from the Joint Authorization Board (JAB) at the Federal Risk and Authorization Management Program (FedRAMP) Moderate baseline.

Though AWS has maintained an AWS US East/West Region Agency-ATO since early 2013, this announcement represents AWS’s carefully deliberated move to the JAB for the centralized maintenance of our P-ATO for 10 services already authorized. This also includes the addition of 10 new services to our FedRAMP program (see the complete list of services below). This doubles the number of FedRAMP Moderate services available to our customers to enable increased use of the cloud and support modernized IT missions. Our public sector customers now can leverage this FedRAMP P-ATO as a baseline for their own authorizations and look to the JAB for centralized Continuous Monitoring reporting and updates. In a significant enhancement for our partners that build their solutions on the AWS US East/West Region, they can now achieve FedRAMP JAB P-ATOs of their own for their Platform as a Service (PaaS) and Software as a Service (SaaS) offerings.

In line with FedRAMP security requirements, our independent FedRAMP assessment was completed in partnership with a FedRAMP accredited Third Party Assessment Organization (3PAO) on our technical, management, and operational security controls to validate that they meet or exceed FedRAMP’s Moderate baseline requirements. Effective immediately, you can begin leveraging this P-ATO for the following 20 services in the AWS US East/West Region:

  • Amazon Aurora (MySQL)*
  • Amazon CloudWatch Logs*
  • Amazon DynamoDB
  • Amazon Elastic Block Store
  • Amazon Elastic Compute Cloud
  • Amazon EMR*
  • Amazon Glacier*
  • Amazon Kinesis Streams*
  • Amazon RDS (MySQL, Oracle, Postgres*)
  • Amazon Redshift
  • Amazon Simple Notification Service*
  • Amazon Simple Queue Service*
  • Amazon Simple Storage Service
  • Amazon Simple Workflow Service*
  • Amazon Virtual Private Cloud
  • AWS CloudFormation*
  • AWS CloudTrail*
  • AWS Identity and Access Management
  • AWS Key Management Service
  • Elastic Load Balancing

* Services with first-time FedRAMP Moderate authorizations

We continue to work with the FedRAMP Project Management Office (PMO), other regulatory and compliance bodies, and our customers and partners to ensure that we are raising the bar on our customers’ security and compliance needs.

To learn more about how AWS helps customers meet their security and compliance requirements, see the AWS Compliance website. To learn about what other public sector customers are doing on AWS, see our Government, Education, and Nonprofits Case Studies and Customer Success Stories. To review the public posting of our FedRAMP authorizations, see the FedRAMP Marketplace.

– Chris Gile, Senior Manager, AWS Public Sector Risk and Compliance

How AWS Managed Microsoft AD Helps to Simplify the Deployment and Improve the Security of Active Directory–Integrated .NET Applications

Post Syndicated from Peter Pereira original https://aws.amazon.com/blogs/security/how-aws-managed-microsoft-ad-helps-to-simplify-the-deployment-and-improve-the-security-of-active-directory-integrated-net-applications/

Companies using .NET applications to access sensitive user information, such as employee salary, Social Security Number, and credit card information, need an easy and secure way to manage access for users and applications.

For example, let’s say that your company has a .NET payroll application. You want your Human Resources (HR) team to manage and update the payroll data for all the employees in your company. You also want your employees to be able to see their own payroll information in the application. To meet these requirements in a user-friendly and secure way, you want to manage access to the .NET application by using your existing Microsoft Active Directory identities. This enables you to provide users with single sign-on (SSO) access to the .NET application and to manage permissions using Active Directory groups. You also want the .NET application to authenticate itself to access the database, and to limit access to the data in the database based on the identity of the application user.

Microsoft Active Directory supports these requirements through group Managed Service Accounts (gMSAs) and Kerberos constrained delegation (KCD). AWS Directory Service for Microsoft Active Directory, also known as AWS Managed Microsoft AD, enables you to manage gMSAs and KCD through your administrative account, helping you to migrate and develop .NET applications that need these native Active Directory features.

In this blog post, I give an overview of how to use AWS Managed Microsoft AD to manage gMSAs and KCD and demonstrate how you can configure a gMSA and KCD in six steps for a .NET application:

  1. Create your AWS Managed Microsoft AD.
  2. Create your Amazon RDS for SQL Server database.
  3. Create a gMSA for your .NET application.
  4. Deploy your .NET application.
  5. Configure your .NET application to use the gMSA.
  6. Configure KCD for your .NET application.

Solution overview

The following diagram shows the components of a .NET application that uses Amazon RDS for SQL Server with a gMSA and KCD. The diagram also illustrates authentication and access and is numbered to show the six key steps required to use a gMSA and KCD. To deploy this solution, the AWS Managed Microsoft AD directory must be in the same Amazon Virtual Private Cloud (VPC) as RDS for SQL Server. For this example, my company name is Example Corp., and my directory uses the domain name, example.com.

Diagram showing the components of a .NET application that uses Amazon RDS for SQL Server with a gMSA and KCD

Deploy the solution

The following six steps (numbered to correlate with the preceding diagram) walk you through configuring and using a gMSA and KCD.

1. Create your AWS Managed Microsoft AD directory

Using the Directory Service console, create your AWS Managed Microsoft AD directory in your Amazon VPC. In my example, my domain name is example.com.

Image of creating an AWS Managed Microsoft AD directory in an Amazon VPC

2. Create your Amazon RDS for SQL Server database

Using the RDS console, create your Amazon RDS for SQL Server database instance in the same Amazon VPC where your directory is running, and enable Windows Authentication. To enable Windows Authentication, select your directory in the Microsoft SQL Server Windows Authentication section in the Configure Advanced Settings step of the database creation workflow (see the following screenshot).

In my example, I create my Amazon RDS for SQL Server db-example database, and enable Windows Authentication to allow my db-example database to authenticate against my example.com directory.

Screenshot of configuring advanced settings

3. Create a gMSA for your .NET application

Now that you have deployed your directory, database, and application, you can create a gMSA for your .NET application.

To perform the next steps, you must install the Active Directory administration tools on a Windows server that is joined to your AWS Managed Microsoft AD directory domain. If you do not have a Windows server joined to your directory domain, you can deploy a new Amazon EC2 for Microsoft Windows Server instance and join it to your directory domain.

To create a gMSA for your .NET application:

  1. Log on to the instance on which you installed the Active Directory administration tools by using a user that is a member of the Admins security group or the Managed Service Accounts Admins security group in your organizational unit (OU). For my example, I use the Admin user in the example OU.

Screenshot of logging on to the instance on which you installed the Active Directory administration tools

  1. Identify which .NET application servers (hosts) will run your .NET application. Create a new security group in your OU and add your .NET application servers as members of this new group. This allows a group of application servers to use a single gMSA, instead of creating one gMSA for each server. In my example, I create a group, App_server_grp, in my example OU. I also add Appserver1, which is my .NET application server computer name, as a member of this new group.

Screenshot of creating a new security group

  1. Create a gMSA in your directory by running Windows PowerShell from the Start menu. The basic syntax to create the gMSA at the Windows PowerShell command prompt follows.
    PS C:\Users\admin> New-ADServiceAccount -name [gMSAname] -DNSHostName [domainname] -PrincipalsAllowedToRetrieveManagedPassword [AppServersSecurityGroup] -TrustedForDelegation $truedn <Enter>

    In my example, the gMSAname is gMSAexample, the DNSHostName is example.com, and the PrincipalsAllowedToRetrieveManagedPassword is the recently created security group, App_server_grp.

    PS C:\Users\admin> New-ADServiceAccount -name gMSAexample -DNSHostName example.com -PrincipalsAllowedToRetrieveManagedPassword App_server_grp -TrustedForDelegation $truedn <Enter>

    To confirm you created the gMSA, you can run the Get-ADServiceAccount command from the PowerShell command prompt.

    PS C:\Users\admin> Get-ADServiceAccount gMSAexample <Enter>
    
    DistinguishedName : CN=gMSAexample,CN=Managed Service Accounts,DC=example,DC=com
    Enabled           : True
    Name              : gMSAexample
    ObjectClass       : msDS-GroupManagedServiceAccount
    ObjectGUID        : 24d8b68d-36d5-4dc3-b0a9-edbbb5dc8a5b
    SamAccountName    : gMSAexample$
    SID               : S-1-5-21-2100421304-991410377-951759617-1603
    UserPrincipalName :

    You also can confirm you created the gMSA by opening the Active Directory Users and Computers utility located in your Administrative Tools folder, expand the domain (example.com in my case), and expand the Managed Service Accounts folder.
    Screenshot of confirming the creation of the gMSA

4. Deploy your .NET application

Deploy your .NET application on IIS on Amazon EC2 for Windows Server instances. For this step, I assume you are the application’s expert and already know how to deploy it. Make sure that all of your instances are joined to your directory.

5. Configure your .NET application to use the gMSA

You can configure your .NET application to use the gMSA to enforce strong password security policy and ensure password rotation of your service account. This helps to improve the security and simplify the management of your .NET application. Configure your .NET application in two steps:

  1. Grant to gMSA the required permissions to run your .NET application in the respective application folders. This is a critical step because when you change the application pool identity account to use gMSA, downtime can occur if the gMSA does not have the application’s required permissions. Therefore, make sure you first test the configurations in your development and test environments.
  2. Configure your application pool identity on IIS to use the gMSA as the service account. When you configure a gMSA as the service account, you include the $ at the end of the gMSA name. You do not need to provide a password because AWS Managed Microsoft AD automatically creates and rotates the password. In my example, my service account is gMSAexample$, as shown in the following screenshot.

Screenshot of configuring application pool identity

You have completed all the steps to use gMSA to create and rotate your .NET application service account password! Now, you will configure KCD for your .NET application.

6. Configure KCD for your .NET application

You now are ready to allow your .NET application to have access to other services by using the user identity’s permissions instead of the application service account’s permissions. Note that KCD and gMSA are independent features, which means you do not have to create a gMSA to use KCD. For this example, I am using both features to show how you can use them together. To configure a regular service account such as a user or local built-in account, see the Kerberos constrained delegation with ASP.NET blog post on MSDN.

In my example, my goal is to delegate to the gMSAexample account the ability to enforce the user’s permissions to my db-example SQL Server database, instead of the gMSAexample account’s permissions. For this, I have to update the msDS-AllowedToDelegateTo gMSA attribute. The value for this attribute is the service principal name (SPN) of the service instance that you are targeting, which in this case is the db-example Amazon RDS for SQL Server database.

The SPN format for the msDS-AllowedToDelegateTo attribute is a combination of the service class, the Kerberos authentication endpoint, and the port number. The Amazon RDS for SQL Server Kerberos authentication endpoint format is [database_name].[domain_name]. The value for my msDS-AllowedToDelegateTo attribute is MSSQLSvc/db-example.example.com:1433, where MSSQLSvc and 1433 are the SQL Server Database service class and port number standards, respectively.

Follow these steps to perform the msDS-AllowedToDelegateTo gMSA attribute configuration:

  1. Log on to your Active Directory management instance with a user identity that is a member of the Kerberos Delegation Admins security group. In this case, I will use admin.
  2. Open the Active Directory Users and Groups utility located in your Administrative Tools folder, choose View, and then choose Advanced Features.
  3. Expand your domain name (example.com in this example), and then choose the Managed Service Accounts security group. Right-click the gMSA account for the application pool you want to enable for Kerberos delegation, choose Properties, and choose the Attribute Editor tab.
  4. Search for the msDS-AllowedToDelegateTo attribute on the Attribute Editor tab and choose Edit.
  5. Enter the MSSQLSvc/db-example.example.com:1433 value and choose Add.
    Screenshot of entering the value of the multi-valued string
  6. Choose OK and Apply, and your KCD configuration is complete.

Congratulations! At this point, your application is using a gMSA rather than an embedded static user identity and password, and the application is able to access SQL Server using the identity of the application user. The gMSA eliminates the need for you to rotate the application’s password manually, and it allows you to better scope permissions for the application. When you use KCD, you can enforce access to your database consistently based on user identities at the database level, which prevents improper access that might otherwise occur because of an application error.

Summary

In this blog post, I demonstrated how to simplify the deployment and improve the security of your .NET application by using a group Managed Service Account and Kerberos constrained delegation with your AWS Managed Microsoft AD directory. I also outlined the main steps to get your .NET environment up and running on a managed Active Directory and SQL Server infrastructure. This approach will make it easier for you to build new .NET applications in the AWS Cloud or migrate existing ones in a more secure way.

For additional information about using group Managed Service Accounts and Kerberos constrained delegation with your AWS Managed Microsoft AD directory, see the AWS Directory Service documentation.

To learn more about AWS Directory Service, see the AWS Directory Service home page. If you have questions about this post or its solution, start a new thread on the Directory Service forum.

– Peter

New – AWS PrivateLink for AWS Services: Kinesis, Service Catalog, EC2 Systems Manager, Amazon EC2 APIs, and ELB APIs in your VPC

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/new-aws-privatelink-endpoints-kinesis-ec2-systems-manager-and-elb-apis-in-your-vpc/

This guest post is by Colm MacCárthaigh, Senior Engineer for Amazon Virtual Private Cloud.


Since VPC Endpoints launched in 2015, creating Endpoints has been a popular way to securely access S3 and DynamoDB from an Amazon Virtual Private Cloud (VPC) without the need for an Internet gateway, a NAT gateway, or firewall proxies. With VPC Endpoints, the routing between the VPC and the AWS service is handled by the AWS network, and IAM policies can be used to control access to service resources.

Today we are announcing AWS PrivateLink, the newest generation of VPC Endpoints which is designed for customers to access AWS services in a highly available and scalable manner, while keeping all the traffic within the AWS network. Kinesis, Service Catalog, Amazon EC2, EC2 Systems Manager (SSM), and Elastic Load Balancing (ELB) APIs are now available to use inside your VPC, with support for more services coming soon such as Key Management Service (KMS) and Amazon Cloudwatch.

With traditional endpoints, it’s very much like connecting a virtual cable between your VPC and the AWS service. Connectivity to the AWS service does not require an Internet or NAT gateway, but the endpoint remains outside of your VPC. With PrivateLink, endpoints are instead created directly inside of your VPC, using Elastic Network Interfaces (ENIs) and IP addresses in your VPC’s subnets. The service is now in your VPC, enabling connectivity to AWS services via private IP addresses. That means that VPC Security Groups can be used to manage access to the endpoints and that PrivateLink endpoints can also be accessed from your premises via AWS Direct Connect.

Using the services powered by PrivateLink, customers can now manage fleets of instances, create and manage catalogs of IT services as well as store and process data, without requiring the traffic to traverse the Internet.

Creating a PrivateLink Endpoint
To create a PrivateLink endpoint, I navigate to the VPC Console, select Endpoints, and choose Create Endpoint.

I then choose which service I’d like to access. New PrivateLink endpoints have an “interface” type. In this case I’d like to use the Kinesis service directly from my VPC and I choose the kinesis-streams service.

At this point I can choose which of my VPCs I’d like to launch my new endpoint in, and select the subnets that the ENIs and IP addresses will be placed in. I can also associate the endpoint with a new or existing Security Group, allowing me to control which of my instances can access the Endpoint.

Because PrivateLink endpoints will use IP addresses from my VPC, I have the option to over-ride DNS for the AWS service DNS name by using VPC Private DNS. By leaving Enable Private DNS Name checked, lookups from within my VPC for “kinesis.us-east-1.amazonaws.com” will resolve to the IP addresses for the endpoint that I’m creating. This makes the transition to the endpoint seamless without requiring any changes to my applications. If I’d prefer to test or configure the endpoint before handling traffic by default, I can leave this disabled and then change it at any time by editing the endpoint.

Once I’m ready and happy with the VPC, subnets and DNS settings, I click Create Endpoint to complete the process.

Using a PrivateLink Endpoint

By default, with the Private DNS Name enabled, using a PrivateLink endpoint is as straight-forward as using the SDK, AWS CLI or other software that accesses the service API from within your VPC. There’s no need to change any code or configurations.

To support testing and advanced configurations, every endpoint also gets a set of DNS names that are unique and dedicated to your endpoint. There’s a primary name for the endpoint and zonal names.

The primary name is particularly useful for accessing your endpoint via Direct Connect, without having to use any DNS over-rides on-premises. Naturally, the primary name can also be used inside of your VPC.
The primary name, and the main service name – since I chose to over-ride it – include zonal fault-tolerance and will balance traffic between the Availability Zones. If I had an architecture that uses zonal isolation techniques, either for fault containment and compartmentalization, low latency, or for minimizing regional data transfer I could also use the zonal names to explicitly control whether my traffic flows between or stays within zones.

Pricing & Availability
AWS PrivateLink is available today in all AWS commercial regions except China (Beijing). For the region availability of individual services, please check our documentation.

Pricing starts at $0.01 / hour plus a data processing charge at $0.01 / GB. Data transferred between availability zones, or between your Endpoint and your premises via Direct Connect will also incur the usual EC2 Regional and Direct Connect data transfer charges. For more information, see VPC Pricing.

Colm MacCárthaigh

 

Now Available – Compute-Intensive C5 Instances for Amazon EC2

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-available-compute-intensive-c5-instances-for-amazon-ec2/

I’m thrilled to announce that the new compute-intensive C5 instances are available today in six sizes for launch in three AWS regions!

These instances designed for compute-heavy applications like batch processing, distributed analytics, high-performance computing (HPC), ad serving, highly scalable multiplayer gaming, and video encoding. The new instances offer a 25% price/performance improvement over the C4 instances, with over 50% for some workloads. They also have additional memory per vCPU, and (for code that can make use of the new AVX-512 instructions), twice the performance for vector and floating point workloads.

Over the years we have been working non-stop to provide our customers with the best possible networking, storage, and compute performance, with a long-term focus on offloading many types of work to dedicated hardware designed and built by AWS. The C5 instance type incorporates the latest generation of our hardware offloads, and also takes another big step forward with the addition of a new hypervisor that runs hand-in-glove with our hardware. The new hypervisor allows us to give you access to all of the processing power provided by the host hardware, while also making performance even more consistent and further raising the bar on security. We’ll be sharing many technical details about it at AWS re:Invent.

The New Instances
The C5 instances are available in six sizes:

Instance Name vCPUs
RAM
EBS Bandwidth Network Bandwidth
c5.large 2 4 GiB Up to 2.25 Gbps Up to 10 Gbps
c5.xlarge 4 8 GiB Up to 2.25 Gbps Up to 10 Gbps
c5.2xlarge 8 16 GiB Up to 2.25 Gbps Up to 10 Gbps
c5.4xlarge 16 32 GiB 2.25 Gbps Up to 10 Gbps
c5.9xlarge 36 72 GiB 4.5 Gbps 10 Gbps
c5.18xlarge 72 144 GiB 9 Gbps 25 Gbps

Each vCPU is a hardware hyperthread on a 3.0 GHz Intel Xeon Platinum 8000-series processor. This custom processor, optimized for EC2, allows you have full control over the C-states on the two largest sizes, allowing you to run a single core at up to 3.5 GHz using Intel Turbo Boost Technology.

As you can see from the table, the four smallest instance sizes offer substantially more EBS and network bandwidth than the previous generation of compute-intensive instances.

Because all networking and storage functionality is implemented in hardware, C5 instances require HVM AMIs that include drivers for the Elastic Network Adapter (ENA) and NVMe. The latest Amazon Linux, Microsoft Windows, Ubuntu, RHEL, CentOS, SLES, Debian, and FreeBSD AMIs all support C5 instances. If you are doing machine learning inferencing, or other compute-intensive work, be sure to check out the most recent version of the Intel Math Kernel Library. It has been optimized for the Intel® Xeon® Platinum processor and has the potential to greatly accelerate your work.

In order to remain compatible with instances that use the Xen hypervisor, the device names for EBS volumes will continue to use the existing /dev/sd and /dev/xvd prefixes. The device name that you provide when you attach a volume to an instance is not used because the NVMe driver assigns its own device name (read Amazon EBS and NVMe to learn more):

The nvme command displays additional information about each volume (install it using sudo yum -y install nvme-cli if necessary):

The SN field in the output can be mapped to an EBS volume ID by inserting a “-” after the “vol” prefix (sadly, the NVMe SN field is not long enough to store the entire ID). Here’s a simple script that uses this information to create an EBS snapshot of each attached volume:

$ sudo nvme list | \
  awk '/dev/ {print(gensub("vol", "vol-", 1, $2))}' | \
  xargs -n 1 aws ec2 create-snapshot --volume-id

With a little more work (and a lot of testing), you could create a script that expands EBS volumes that are getting full.

Getting to C5
As I mentioned earlier, our effort to offload work to hardware accelerators has been underway for quite some time. Here’s a recap:

CC1 – Launched in 2010, the CC1 was designed to support scale-out HPC applications. It was the first EC2 instance to support 10 Gbps networking and one of the first to support HVM virtualization. The network fabric that we designed for the CC1 (based on our own switch hardware) has become the standard for all AWS data centers.

C3 – Launched in 2013, the C3 introduced Enhanced Networking and uses dedicated hardware accelerators to support the software defined network inside of each Virtual Private Cloud (VPC). Hardware virtualization removes the I/O stack from the hypervisor in favor of direct access by the guest OS, resulting in higher performance and reduced variability.

C4 – Launched in 2015, the C4 instances are EBS Optimized by default via a dedicated network connection, and also offload EBS processing (including CPU-intensive crypto operations for encrypted EBS volumes) to a hardware accelerator.

C5 – Launched today, the hypervisor that powers the C5 instances allow practically all of the resources of the host CPU to be devoted to customer instances. The ENA networking and the NVMe interface to EBS are both powered by hardware accelerators. The instances do not require (or support) the Xen paravirtual networking or block device drivers, both of which have been removed in order to increase efficiency.

Going forward, we’ll use this hypervisor to power other instance types and plan to share additional technical details in a set of AWS re:Invent sessions.

Launch a C5 Today
You can launch C5 instances today in the US East (Northern Virginia), US West (Oregon), and EU (Ireland) Regions in On-Demand and Spot form (Reserved Instances are also available), with additional Regions in the works.

One quick note before I go: The current NVMe driver is not optimized for high-performance sequential workloads and we don’t recommend the use of C5 instances in conjunction with sc1 or st1 volumes. We are aware of this issue and have been working to optimize the driver for this important use case.

Jeff;