Tag Archives: Scale

Announcing Local Build Support for AWS CodeBuild

Post Syndicated from Karthik Thirugnanasambandam original https://aws.amazon.com/blogs/devops/announcing-local-build-support-for-aws-codebuild/

Today, we’re excited to announce local build support in AWS CodeBuild.

AWS CodeBuild is a fully managed build service. There are no servers to provision and scale, or software to install, configure, and operate. You just specify the location of your source code, choose your build settings, and CodeBuild runs build scripts for compiling, testing, and packaging your code.

In this blog post, I’ll show you how to set up CodeBuild locally to build and test a sample Java application.

By building an application on a local machine you can:

  • Test the integrity and contents of a buildspec file locally.
  • Test and build an application locally before committing.
  • Identify and fix errors quickly from your local development environment.

Prerequisites

In this post, I am using AWS Cloud9 IDE as my development environment.

If you would like to use AWS Cloud9 as your IDE, follow the express setup steps in the AWS Cloud9 User Guide.

The AWS Cloud9 IDE comes with Docker and Git already installed. If you are going to use your laptop or desktop machine as your development environment, install Docker and Git before you start.

Steps to build CodeBuild image locally

Run git clone https://github.com/aws/aws-codebuild-docker-images.git to download this repository to your local machine.

$ git clone https://github.com/aws/aws-codebuild-docker-images.git

Lets build a local CodeBuild image for JDK 8 environment. The Dockerfile for JDK 8 is present in /aws-codebuild-docker-images/ubuntu/java/openjdk-8.

Edit the Dockerfile to remove the last line ENTRYPOINT [“dockerd-entrypoint.sh”] and save the file.

Run cd ubuntu/java/openjdk-8 to change the directory in your local workspace.

Run docker build -t aws/codebuild/java:openjdk-8 . to build the Docker image locally. This command will take few minutes to complete.

$ cd aws-codebuild-docker-images
$ cd ubuntu/java/openjdk-8
$ docker build -t aws/codebuild/java:openjdk-8 .

Steps to setup CodeBuild local agent

Run the following Docker pull command to download the local CodeBuild agent.

$ docker pull amazon/aws-codebuild-local:latest --disable-content-trust=false

Now you have the local agent image on your machine and can run a local build.

Run the following git command to download a sample Java project.

$ git clone https://github.com/karthiksambandam/sample-web-app.git

Steps to use the local agent to build a sample project

Let’s build the sample Java project using the local agent.

Execute the following Docker command to run the local agent and build the sample web app repository you cloned earlier.

$ docker run -it -v /var/run/docker.sock:/var/run/docker.sock -e "IMAGE_NAME=aws/codebuild/java:openjdk-8" -e "ARTIFACTS=/home/ec2-user/environment/artifacts" -e "SOURCE=/home/ec2-user/environment/sample-web-app" amazon/aws-codebuild-local

Note: We need to provide three environment variables namely  IMAGE_NAME, SOURCE and ARTIFACTS.

IMAGE_NAME: The name of your build environment image.

SOURCE: The absolute path to your source code directory.

ARTIFACTS: The absolute path to your artifact output folder.

When you run the sample project, you get a runtime error that says the YAML file does not exist. This is because a buildspec.yml file is not included in the sample web project. AWS CodeBuild requires a buildspec.yml to run a build. For more information about buildspec.yml, see Build Spec Example in the AWS CodeBuild User Guide.

Let’s add a buildspec.yml file with the following content to the sample-web-app folder and then rebuild the project.

version: 0.2

phases:
  build:
    commands:
      - echo Build started on `date`
      - mvn install

artifacts:
  files:
    - target/javawebdemo.war

$ docker run -it -v /var/run/docker.sock:/var/run/docker.sock -e "IMAGE_NAME=aws/codebuild/java:openjdk-8" -e "ARTIFACTS=/home/ec2-user/environment/artifacts" -e "SOURCE=/home/ec2-user/environment/sample-web-app" amazon/aws-codebuild-local

This time your build should be successful. Upon successful execution, look in the /artifacts folder for the final built artifacts.zip file to validate.

Conclusion:

In this blog post, I showed you how to quickly set up the CodeBuild local agent to build projects right from your local desktop machine or laptop. As you see, local builds can improve developer productivity by helping you identify and fix errors quickly.

I hope you found this post useful. Feel free to leave your feedback or suggestions in the comments.

Analyze data in Amazon DynamoDB using Amazon SageMaker for real-time prediction

Post Syndicated from YongSeong Lee original https://aws.amazon.com/blogs/big-data/analyze-data-in-amazon-dynamodb-using-amazon-sagemaker-for-real-time-prediction/

Many companies across the globe use Amazon DynamoDB to store and query historical user-interaction data. DynamoDB is a fast NoSQL database used by applications that need consistent, single-digit millisecond latency.

Often, customers want to turn their valuable data in DynamoDB into insights by analyzing a copy of their table stored in Amazon S3. Doing this separates their analytical queries from their low-latency critical paths. This data can be the primary source for understanding customers’ past behavior, predicting future behavior, and generating downstream business value. Customers often turn to DynamoDB because of its great scalability and high availability. After a successful launch, many customers want to use the data in DynamoDB to predict future behaviors or provide personalized recommendations.

DynamoDB is a good fit for low-latency reads and writes, but it’s not practical to scan all data in a DynamoDB database to train a model. In this post, I demonstrate how you can use DynamoDB table data copied to Amazon S3 by AWS Data Pipeline to predict customer behavior. I also demonstrate how you can use this data to provide personalized recommendations for customers using Amazon SageMaker. You can also run ad hoc queries using Amazon Athena against the data. DynamoDB recently released on-demand backups to create full table backups with no performance impact. However, it’s not suitable for our purposes in this post, so I chose AWS Data Pipeline instead to create managed backups are accessible from other services.

To do this, I describe how to read the DynamoDB backup file format in Data Pipeline. I also describe how to convert the objects in S3 to a CSV format that Amazon SageMaker can read. In addition, I show how to schedule regular exports and transformations using Data Pipeline. The sample data used in this post is from Bank Marketing Data Set of UCI.

The solution that I describe provides the following benefits:

  • Separates analytical queries from production traffic on your DynamoDB table, preserving your DynamoDB read capacity units (RCUs) for important production requests
  • Automatically updates your model to get real-time predictions
  • Optimizes for performance (so it doesn’t compete with DynamoDB RCUs after the export) and for cost (using data you already have)
  • Makes it easier for developers of all skill levels to use Amazon SageMaker

All code and data set in this post are available in this .zip file.

Solution architecture

The following diagram shows the overall architecture of the solution.

The steps that data follows through the architecture are as follows:

  1. Data Pipeline regularly copies the full contents of a DynamoDB table as JSON into an S3
  2. Exported JSON files are converted to comma-separated value (CSV) format to use as a data source for Amazon SageMaker.
  3. Amazon SageMaker renews the model artifact and update the endpoint.
  4. The converted CSV is available for ad hoc queries with Amazon Athena.
  5. Data Pipeline controls this flow and repeats the cycle based on the schedule defined by customer requirements.

Building the auto-updating model

This section discusses details about how to read the DynamoDB exported data in Data Pipeline and build automated workflows for real-time prediction with a regularly updated model.

Download sample scripts and data

Before you begin, take the following steps:

  1. Download sample scripts in this .zip file.
  2. Unzip the src.zip file.
  3. Find the automation_script.sh file and edit it for your environment. For example, you need to replace 's3://<your bucket>/<datasource path>/' with your own S3 path to the data source for Amazon ML. In the script, the text enclosed by angle brackets—< and >—should be replaced with your own path.
  4. Upload the json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar file to your S3 path so that the ADD jar command in Apache Hive can refer to it.

For this solution, the banking.csv  should be imported into a DynamoDB table.

Export a DynamoDB table

To export the DynamoDB table to S3, open the Data Pipeline console and choose the Export DynamoDB table to S3 template. In this template, Data Pipeline creates an Amazon EMR cluster and performs an export in the EMRActivity activity. Set proper intervals for backups according to your business requirements.

One core node(m3.xlarge) provides the default capacity for the EMR cluster and should be suitable for the solution in this post. Leave the option to resize the cluster before running enabled in the TableBackupActivity activity to let Data Pipeline scale the cluster to match the table size. The process of converting to CSV format and renewing models happens in this EMR cluster.

For a more in-depth look at how to export data from DynamoDB, see Export Data from DynamoDB in the Data Pipeline documentation.

Add the script to an existing pipeline

After you export your DynamoDB table, you add an additional EMR step to EMRActivity by following these steps:

  1. Open the Data Pipeline console and choose the ID for the pipeline that you want to add the script to.
  2. For Actions, choose Edit.
  3. In the editing console, choose the Activities category and add an EMR step using the custom script downloaded in the previous section, as shown below.

Paste the following command into the new step after the data ­­upload step:

s3://#{myDDBRegion}.elasticmapreduce/libs/script-runner/script-runner.jar,s3://<your bucket name>/automation_script.sh,#{output.directoryPath},#{myDDBRegion}

The element #{output.directoryPath} references the S3 path where the data pipeline exports DynamoDB data as JSON. The path should be passed to the script as an argument.

The bash script has two goals, converting data formats and renewing the Amazon SageMaker model. Subsequent sections discuss the contents of the automation script.

Automation script: Convert JSON data to CSV with Hive

We use Apache Hive to transform the data into a new format. The Hive QL script to create an external table and transform the data is included in the custom script that you added to the Data Pipeline definition.

When you run the Hive scripts, do so with the -e option. Also, define the Hive table with the 'org.openx.data.jsonserde.JsonSerDe' row format to parse and read JSON format. The SQL creates a Hive EXTERNAL table, and it reads the DynamoDB backup data on the S3 path passed to it by Data Pipeline.

Note: You should create the table with the “EXTERNAL” keyword to avoid the backup data being accidentally deleted from S3 if you drop the table.

The full automation script for converting follows. Add your own bucket name and data source path in the highlighted areas.

#!/bin/bash
hive -e "
ADD jar s3://<your bucket name>/json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar ; 
DROP TABLE IF EXISTS blog_backup_data ;
CREATE EXTERNAL TABLE blog_backup_data (
 customer_id map<string,string>,
 age map<string,string>, job map<string,string>, 
 marital map<string,string>,education map<string,string>, 
 default map<string,string>, housing map<string,string>,
 loan map<string,string>, contact map<string,string>, 
 month map<string,string>, day_of_week map<string,string>, 
 duration map<string,string>, campaign map<string,string>,
 pdays map<string,string>, previous map<string,string>, 
 poutcome map<string,string>, emp_var_rate map<string,string>, 
 cons_price_idx map<string,string>, cons_conf_idx map<string,string>,
 euribor3m map<string,string>, nr_employed map<string,string>, 
 y map<string,string> ) 
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe' 
LOCATION '$1/';

INSERT OVERWRITE DIRECTORY 's3://<your bucket name>/<datasource path>/' 
SELECT concat( customer_id['s'],',', 
 age['n'],',', job['s'],',', 
 marital['s'],',', education['s'],',', default['s'],',', 
 housing['s'],',', loan['s'],',', contact['s'],',', 
 month['s'],',', day_of_week['s'],',', duration['n'],',', 
 campaign['n'],',',pdays['n'],',',previous['n'],',', 
 poutcome['s'],',', emp_var_rate['n'],',', cons_price_idx['n'],',',
 cons_conf_idx['n'],',', euribor3m['n'],',', nr_employed['n'],',', y['n'] ) 
FROM blog_backup_data
WHERE customer_id['s'] > 0 ; 

After creating an external table, you need to read data. You then use the INSERT OVERWRITE DIRECTORY ~ SELECT command to write CSV data to the S3 path that you designated as the data source for Amazon SageMaker.

Depending on your requirements, you can eliminate or process the columns in the SELECT clause in this step to optimize data analysis. For example, you might remove some columns that have unpredictable correlations with the target value because keeping the wrong columns might expose your model to “overfitting” during the training. In this post, customer_id  columns is removed. Overfitting can make your prediction weak. More information about overfitting can be found in the topic Model Fit: Underfitting vs. Overfitting in the Amazon ML documentation.

Automation script: Renew the Amazon SageMaker model

After the CSV data is replaced and ready to use, create a new model artifact for Amazon SageMaker with the updated dataset on S3.  For renewing model artifact, you must create a new training job.  Training jobs can be run using the AWS SDK ( for example, Amazon SageMaker boto3 ) or the Amazon SageMaker Python SDK that can be installed with “pip install sagemaker” command as well as the AWS CLI for Amazon SageMaker described in this post.

In addition, consider how to smoothly renew your existing model without service impact, because your model is called by applications in real time. To do this, you need to create a new endpoint configuration first and update a current endpoint with the endpoint configuration that is just created.

#!/bin/bash
## Define variable 
REGION=$2
DTTIME=`date +%Y-%m-%d-%H-%M-%S`
ROLE="<your AmazonSageMaker-ExecutionRole>" 


# Select containers image based on region.  
case "$REGION" in
"us-west-2" )
    IMAGE="174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:latest"
    ;;
"us-east-1" )
    IMAGE="382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:latest" 
    ;;
"us-east-2" )
    IMAGE="404615174143.dkr.ecr.us-east-2.amazonaws.com/linear-learner:latest" 
    ;;
"eu-west-1" )
    IMAGE="438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner:latest" 
    ;;
 *)
    echo "Invalid Region Name"
    exit 1 ;  
esac

# Start training job and creating model artifact 
TRAINING_JOB_NAME=TRAIN-${DTTIME} 
S3OUTPUT="s3://<your bucket name>/model/" 
INSTANCETYPE="ml.m4.xlarge"
INSTANCECOUNT=1
VOLUMESIZE=5 
aws sagemaker create-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --algorithm-specification TrainingImage=${IMAGE},TrainingInputMode=File --role-arn ${ROLE}  --input-data-config '[{ "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://<your bucket name>/<datasource path>/", "S3DataDistributionType": "FullyReplicated" } }, "ContentType": "text/csv", "CompressionType": "None" , "RecordWrapperType": "None"  }]'  --output-data-config S3OutputPath=${S3OUTPUT} --resource-config  InstanceType=${INSTANCETYPE},InstanceCount=${INSTANCECOUNT},VolumeSizeInGB=${VOLUMESIZE} --stopping-condition MaxRuntimeInSeconds=120 --hyper-parameters feature_dim=20,predictor_type=binary_classifier  

# Wait until job completed 
aws sagemaker wait training-job-completed-or-stopped --training-job-name ${TRAINING_JOB_NAME}  --region ${REGION}

# Get newly created model artifact and create model
MODELARTIFACT=`aws sagemaker describe-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --query 'ModelArtifacts.S3ModelArtifacts' --output text `
MODELNAME=MODEL-${DTTIME}
aws sagemaker create-model --region ${REGION} --model-name ${MODELNAME}  --primary-container Image=${IMAGE},ModelDataUrl=${MODELARTIFACT}  --execution-role-arn ${ROLE}

# create a new endpoint configuration 
CONFIGNAME=CONFIG-${DTTIME}
aws sagemaker  create-endpoint-config --region ${REGION} --endpoint-config-name ${CONFIGNAME}  --production-variants  VariantName=Users,ModelName=${MODELNAME},InitialInstanceCount=1,InstanceType=ml.m4.xlarge

# create or update the endpoint
STATUS=`aws sagemaker describe-endpoint --endpoint-name  ServiceEndpoint --query 'EndpointStatus' --output text --region ${REGION} `
if [[ $STATUS -ne "InService" ]] ;
then
    aws sagemaker  create-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}    
else
    aws sagemaker  update-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}
fi

Grant permission

Before you execute the script, you must grant proper permission to Data Pipeline. Data Pipeline uses the DataPipelineDefaultResourceRole role by default. I added the following policy to DataPipelineDefaultResourceRole to allow Data Pipeline to create, delete, and update the Amazon SageMaker model and data source in the script.

{
 "Version": "2012-10-17",
 "Statement": [
 {
 "Effect": "Allow",
 "Action": [
 "sagemaker:CreateTrainingJob",
 "sagemaker:DescribeTrainingJob",
 "sagemaker:CreateModel",
 "sagemaker:CreateEndpointConfig",
 "sagemaker:DescribeEndpoint",
 "sagemaker:CreateEndpoint",
 "sagemaker:UpdateEndpoint",
 "iam:PassRole"
 ],
 "Resource": "*"
 }
 ]
}

Use real-time prediction

After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint. This approach is useful for interactive web, mobile, or desktop applications.

Following, I provide a simple Python code example that queries against Amazon SageMaker endpoint URL with its name (“ServiceEndpoint”) and then uses them for real-time prediction.

=== Python sample for real-time prediction ===

#!/usr/bin/env python
import boto3
import json 

client = boto3.client('sagemaker-runtime', region_name ='<your region>' )
new_customer_info = '34,10,2,4,1,2,1,1,6,3,190,1,3,4,3,-1.7,94.055,-39.8,0.715,4991.6'
response = client.invoke_endpoint(
    EndpointName='ServiceEndpoint',
    Body=new_customer_info, 
    ContentType='text/csv'
)
result = json.loads(response['Body'].read().decode())
print(result)
--- output(response) ---
{u'predictions': [{u'score': 0.7528127431869507, u'predicted_label': 1.0}]}

Solution summary

The solution takes the following steps:

  1. Data Pipeline exports DynamoDB table data into S3. The original JSON data should be kept to recover the table in the rare event that this is needed. Data Pipeline then converts JSON to CSV so that Amazon SageMaker can read the data.Note: You should select only meaningful attributes when you convert CSV. For example, if you judge that the “campaign” attribute is not correlated, you can eliminate this attribute from the CSV.
  2. Train the Amazon SageMaker model with the new data source.
  3. When a new customer comes to your site, you can judge how likely it is for this customer to subscribe to your new product based on “predictedScores” provided by Amazon SageMaker.
  4. If the new user subscribes your new product, your application must update the attribute “y” to the value 1 (for yes). This updated data is provided for the next model renewal as a new data source. It serves to improve the accuracy of your prediction. With each new entry, your application can become smarter and deliver better predictions.

Running ad hoc queries using Amazon Athena

Amazon Athena is a serverless query service that makes it easy to analyze large amounts of data stored in Amazon S3 using standard SQL. Athena is useful for examining data and collecting statistics or informative summaries about data. You can also use the powerful analytic functions of Presto, as described in the topic Aggregate Functions of Presto in the Presto documentation.

With the Data Pipeline scheduled activity, recent CSV data is always located in S3 so that you can run ad hoc queries against the data using Amazon Athena. I show this with example SQL statements following. For an in-depth description of this process, see the post Interactive SQL Queries for Data in Amazon S3 on the AWS News Blog. 

Creating an Amazon Athena table and running it

Simply, you can create an EXTERNAL table for the CSV data on S3 in Amazon Athena Management Console.

=== Table Creation ===
CREATE EXTERNAL TABLE datasource (
 age int, 
 job string, 
 marital string , 
 education string, 
 default string, 
 housing string, 
 loan string, 
 contact string, 
 month string, 
 day_of_week string, 
 duration int, 
 campaign int, 
 pdays int , 
 previous int , 
 poutcome string, 
 emp_var_rate double, 
 cons_price_idx double,
 cons_conf_idx double, 
 euribor3m double, 
 nr_employed double, 
 y int 
)
ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' ESCAPED BY '\\' LINES TERMINATED BY '\n' 
LOCATION 's3://<your bucket name>/<datasource path>/';

The following query calculates the correlation coefficient between the target attribute and other attributes using Amazon Athena.

=== Sample Query ===

SELECT corr(age,y) AS correlation_age_and_target, 
 corr(duration,y) AS correlation_duration_and_target, 
 corr(campaign,y) AS correlation_campaign_and_target,
 corr(contact,y) AS correlation_contact_and_target
FROM ( SELECT age , duration , campaign , y , 
 CASE WHEN contact = 'telephone' THEN 1 ELSE 0 END AS contact 
 FROM datasource 
 ) datasource ;

Conclusion

In this post, I introduce an example of how to analyze data in DynamoDB by using table data in Amazon S3 to optimize DynamoDB table read capacity. You can then use the analyzed data as a new data source to train an Amazon SageMaker model for accurate real-time prediction. In addition, you can run ad hoc queries against the data on S3 using Amazon Athena. I also present how to automate these procedures by using Data Pipeline.

You can adapt this example to your specific use case at hand, and hopefully this post helps you accelerate your development. You can find more examples and use cases for Amazon SageMaker in the video AWS 2017: Introducing Amazon SageMaker on the AWS website.

 


Additional Reading

If you found this post useful, be sure to check out Serving Real-Time Machine Learning Predictions on Amazon EMR and Analyzing Data in S3 using Amazon Athena.

 


About the Author

Yong Seong Lee is a Cloud Support Engineer for AWS Big Data Services. He is interested in every technology related to data/databases and helping customers who have difficulties in using AWS services. His motto is “Enjoy life, be curious and have maximum experience.”

 

 

Russia Blocks 50 VPNs & Anonymizers in Telegram Crackdown, Viber Next

Post Syndicated from Andy original https://torrentfreak.com/russia-blocks-50-vpns-anonymizers-in-telegram-crackdown-viber-next-180504/

Any entity operating an encrypted messaging service in Russia needs to register with local authorities. They must also hand over their encryption keys when requested to do so, so that users can be monitored.

Messaging giant Telegram refused to give in to Russian pressure. Founder Pavel Durov said that he would not compromise the privacy of Telegram’s 200m monthly users, despite losing a lawsuit against the Federal Security Service which compelled him to do so. In response, telecoms watchdog Roscomnadzor filed a lawsuit to degrade Telegram via web-blocking.

After a Moscow court gave the go-ahead for Telegram to be banned in Russia last month, chaos broke out. ISPs around the country tried to block the service, which was using Amazon and Google to provide connectivity. Millions of IP addresses belonging to both companies were blocked and countless other companies and individuals had their services blocked too.

But despite the Russian carpet-bombing of Telegram, the service steadfastly remained online. People had problems accessing the service at times, of course, but their determination coupled with that of Telegram and other facilitators largely kept communications flowing.

Part of the huge counter-offensive was mounted by various VPN and anonymizer services that allowed people to bypass ISP blocks. However, they too have found themselves in trouble, with Russian authorities blocking them for facilitating access to Telegram. In an announcement Thursday, the telecoms watchdog revealed the scale of the crackdown.

Deputy Head of Roskomnadzor told TASS that dozens of VPNs and similar services had been blocked while hinting at yet more to come.

“Fifty for the time being,” Subbotin said.

With VPN providers taking a hit on behalf of Telegram, there could be yet more chaos looming on the horizon. It’s feared that other encrypted services, which have also failed to hand over their keys to the FSB, could be targeted next.

Ministry of Communications chief Nikolai Nikiforov told reporters this week that if Viber doesn’t fall into line, it could suffer the same fate as Telegram.

“This is a matter for the Federal Security Service, because the authority with regard to such specific issues in the execution of the order for the provision of encryption keys is the authority of the FSB,” Nikiforov said.

“If they have problems with the provision of encryption keys, they can also apply to the court and obtain a similar court decision,” the minister said, responding to questions about the Japanese-owned, Luxembourg-based communications app.

With plenty of chaos apparent online, there are also reports of problems from within Roscomnadzor itself. For the past several days, rumors have been circulating in Russian media that Roskomnadzor chief Alexander Zharov has resigned, perhaps in response to the huge over-blocking that took place when Telegram was targeted.

When questioned by reporters this week, Ministry of Communications chief Nikolai Nikiforov refused to provide any further information, stating that such a matter would be for the prime minister to handle.

“I would not like to comment on this. If the chairman of the government takes this decision, I recall that the heads of services are appointed by the decision of the prime minister and personnel decisions are never commented on,” he said.

Whether Prime Minister Dmitry Medvedev will make a statement is yet to be seen, but this week his office has been dealing with a blocking – or rather unblocking – controversy of its own.

In a public post on Facebook May 1, Duma deputy Natalya Kostenko revealed that she was having problems due to the Telegram blockades.

“Dear friends, do not write to me on Telegram, I’m not getting your messages. Use other channels to contact me,” Kostenko wrote.

In response, Dmitry Medvedev’s press secretary, Natalia Timakova, told her colleague to circumvent the blockade so that she could access Telegram once again.

“Use a VPN! It’s simple. And it works almost all the time,” Timakov wrote.

Until those get blocked too, of course…..

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

EC2 Fleet – Manage Thousands of On-Demand and Spot Instances with One Request

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/ec2-fleet-manage-thousands-of-on-demand-and-spot-instances-with-one-request/

EC2 Spot Fleets are really cool. You can launch a fleet of Spot Instances that spans EC2 instance types and Availability Zones without having to write custom code to discover capacity or monitor prices. You can set the target capacity (the size of the fleet) in units that are meaningful to your application and have Spot Fleet create and then maintain the fleet on your behalf. Our customers are creating Spot Fleets of all sizes. For example, one financial service customer runs Monte Carlo simulations across 10 different EC2 instance types. They routinely make requests for hundreds of thousands of vCPUs and count on Spot Fleet to give them access to massive amounts of capacity at the best possible price.

EC2 Fleet
Today we are extending and generalizing the set-it-and-forget-it model that we pioneered in Spot Fleet with EC2 Fleet, a new building block that gives you the ability to create fleets that are composed of a combination of EC2 On-Demand, Reserved, and Spot Instances with a single API call. You tell us what you need, capacity and instance-wise, and we’ll handle all the heavy lifting. We will launch, manage, monitor and scale instances as needed, without the need for scaffolding code.

You can specify the capacity of your fleet in terms of instances, vCPUs, or application-oriented units, and also indicate how much of the capacity should be fulfilled by Spot Instances. The application-oriented units allow you to specify the relative power of each EC2 instance type in a way that directly maps to the needs of your application. All three capacity specification options (instances, vCPUs, and application-oriented units) are known as weights.

I think you’ll find a number ways this feature makes managing a fleet of instances easier, and believe that you will also appreciate the team’s near-term feature roadmap of interest (more on that in a bit).

Using EC2 Fleet
There are a number of ways that you can use this feature, whether you’re running a stateless web service, a big data cluster or a continuous integration pipeline. Today I’m going to describe how you can use EC2 Fleet for genomic processing, but this is similar to workloads like risk analysis, log processing or image rendering. Modern DNA sequencers can produce multiple terabytes of raw data each day, to process that data into meaningful information in a timely fashion you need lots of processing power. I’ll be showing you how to deploy a “grid” of worker nodes that can quickly crunch through secondary analysis tasks in parallel.

Projects in genomics can use the elasticity EC2 provides to experiment and try out new pipelines on hundreds or even thousands of servers. With EC2 you can access as many cores as you need and only pay for what you use. Prior to today, you would need to use the RunInstances API or an Auto Scaling group for the On-Demand & Reserved Instance portion of your grid. To get the best price performance you’d also create and manage a Spot Fleet or multiple Spot Auto Scaling groups with different instance types if you wanted to add Spot Instances to turbo-boost your secondary analysis. Finally, to automate scaling decisions across multiple APIs and Auto Scaling groups you would need to write Lambda functions that periodically assess your grid’s progress & backlog, as well as current Spot prices – modifying your Auto Scaling Groups and Spot Fleets accordingly.

You can now replace all of this with a single EC2 Fleet, analyzing genomes at scale for as little as $1 per analysis. In my grid, each step in in the pipeline requires 1 vCPU and 4 GiB of memory, a perfect match for M4 and M5 instances with 4 GiB of memory per vCPU. I will create a fleet using M4 and M5 instances with weights that correspond to the number of vCPUs on each instance:

  • m4.16xlarge – 64 vCPUs, weight = 64
  • m5.24xlarge – 96 vCPUs, weight = 96

This is expressed in a template that looks like this:

"Overrides": [
{
  "InstanceType": "m4.16xlarge",
  "WeightedCapacity": 64,
},
{
  "InstanceType": "m5.24xlarge",
  "WeightedCapacity": 96,
},
]

By default, EC2 Fleet will select the most cost effective combination of instance types and Availability Zones (both specified in the template) using the current prices for the Spot Instances and public prices for the On-Demand Instances (if you specify instances for which you have matching RIs, your discounts will apply). The default mode takes weights into account to get the instances that have the lowest price per unit. So for my grid, fleet will find the instance that offers the lowest price per vCPU.

Now I can request capacity in terms of vCPUs, knowing EC2 Fleet will select the lowest cost option using only the instance types I’ve defined as acceptable. Also, I can specify how many vCPUs I want to launch using On-Demand or Reserved Instance capacity and how many vCPUs should be launched using Spot Instance capacity:

"TargetCapacitySpecification": {
	"TotalTargetCapacity": 2880,
	"OnDemandTargetCapacity": 960,
	"SpotTargetCapacity": 1920,
	"DefaultTargetCapacityType": "Spot"
}

The above means that I want a total of 2880 vCPUs, with 960 vCPUs fulfilled using On-Demand and 1920 using Spot. The On-Demand price per vCPU is lower for m5.24xlarge than the On-Demand price per vCPU for m4.16xlarge, so EC2 Fleet will launch 10 m5.24xlarge instances to fulfill 960 vCPUs. Based on current Spot pricing (again, on a per-vCPU basis), EC2 Fleet will choose to launch 30 m4.16xlarge instances or 20 m5.24xlarges, delivering 1920 vCPUs either way.

Putting it all together, I have a single file (fl1.json) that describes my fleet:

    "LaunchTemplateConfigs": [
        {
            "LaunchTemplateSpecification": {
                "LaunchTemplateId": "lt-0e8c754449b27161c",
                "Version": "1"
            }
        "Overrides": [
        {
          "InstanceType": "m4.16xlarge",
          "WeightedCapacity": 64,
        },
        {
          "InstanceType": "m5.24xlarge",
          "WeightedCapacity": 96,
        },
      ]
        }
    ],
    "TargetCapacitySpecification": {
        "TotalTargetCapacity": 2880,
        "OnDemandTargetCapacity": 960,
        "SpotTargetCapacity": 1920,
        "DefaultTargetCapacityType": "Spot"
    }
}

I can launch my fleet with a single command:

$ aws ec2 create-fleet --cli-input-json file://home/ec2-user/fl1.json
{
    "FleetId":"fleet-838cf4e5-fded-4f68-acb5-8c47ee1b248a"
}

My entire fleet is created within seconds and was built using 10 m5.24xlarge On-Demand Instances and 30 m4.16xlarge Spot Instances, since the current Spot price was 1.5¢ per vCPU for m4.16xlarge and 1.6¢ per vCPU for m5.24xlarge.

Now lets imagine my grid has crunched through its backlog and no longer needs the additional Spot Instances. I can then modify the size of my fleet by changing the target capacity in my fleet specification, like this:

{         
    "TotalTargetCapacity": 960,
}

Since 960 was equal to the amount of On-Demand vCPUs I had requested, when I describe my fleet I will see all of my capacity being delivered using On-Demand capacity:

"TargetCapacitySpecification": {
	"TotalTargetCapacity": 960,
	"OnDemandTargetCapacity": 960,
	"SpotTargetCapacity": 0,
	"DefaultTargetCapacityType": "Spot"
}

When I no longer need my fleet I can delete it and terminate the instances in it like this:

$ aws ec2 delete-fleets --fleet-id fleet-838cf4e5-fded-4f68-acb5-8c47ee1b248a \
  --terminate-instances   
{
    "UnsuccessfulFleetDletetions": [],
    "SuccessfulFleetDeletions": [
        {
            "CurrentFleetState": "deleted_terminating",
            "PreviousFleetState": "active",
            "FleetId": "fleet-838cf4e5-fded-4f68-acb5-8c47ee1b248a"
        }
    ]
}

Earlier I described how RI discounts apply when EC2 Fleet launches instances for which you have matching RIs, so you might be wondering how else RI customers benefit from EC2 Fleet. Let’s say that I own regional RIs for M4 instances. In my EC2 Fleet I would remove m5.24xlarge and specify m4.10xlarge and m4.16xlarge. Then when EC2 Fleet creates the grid, it will quickly find M4 capacity across the sizes and AZs I’ve specified, and my RI discounts apply automatically to this usage.

In the Works
We plan to connect EC2 Fleet and EC2 Auto Scaling groups. This will let you create a single fleet that mixed instance types and Spot, Reserved and On-Demand, while also taking advantage of EC2 Auto Scaling features such as health checks and lifecycle hooks. This integration will also bring EC2 Fleet functionality to services such as Amazon ECS, Amazon EKS, and AWS Batch that build on and make use of EC2 Auto Scaling for fleet management.

Available Now
You can create and make use of EC2 Fleets today in all public AWS Regions!

Jeff;

CI/CD with Data: Enabling Data Portability in a Software Delivery Pipeline with AWS Developer Tools, Kubernetes, and Portworx

Post Syndicated from Kausalya Rani Krishna Samy original https://aws.amazon.com/blogs/devops/cicd-with-data-enabling-data-portability-in-a-software-delivery-pipeline-with-aws-developer-tools-kubernetes-and-portworx/

This post is written by Eric Han – Vice President of Product Management Portworx and Asif Khan – Solutions Architect

Data is the soul of an application. As containers make it easier to package and deploy applications faster, testing plays an even more important role in the reliable delivery of software. Given that all applications have data, development teams want a way to reliably control, move, and test using real application data or, at times, obfuscated data.

For many teams, moving application data through a CI/CD pipeline, while honoring compliance and maintaining separation of concerns, has been a manual task that doesn’t scale. At best, it is limited to a few applications, and is not portable across environments. The goal should be to make running and testing stateful containers (think databases and message buses where operations are tracked) as easy as with stateless (such as with web front ends where they are often not).

Why is state important in testing scenarios? One reason is that many bugs manifest only when code is tested against real data. For example, we might simply want to test a database schema upgrade but a small synthetic dataset does not exercise the critical, finer corner cases in complex business logic. If we want true end-to-end testing, we need to be able to easily manage our data or state.

In this blog post, we define a CI/CD pipeline reference architecture that can automate data movement between applications. We also provide the steps to follow to configure the CI/CD pipeline.

 

Stateful Pipelines: Need for Portable Volumes

As part of continuous integration, testing, and deployment, a team may need to reproduce a bug found in production against a staging setup. Here, the hosting environment is comprised of a cluster with Kubernetes as the scheduler and Portworx for persistent volumes. The testing workflow is then automated by AWS CodeCommit, AWS CodePipeline, and AWS CodeBuild.

Portworx offers Kubernetes storage that can be used to make persistent volumes portable between AWS environments and pipelines. The addition of Portworx to the AWS Developer Tools continuous deployment for Kubernetes reference architecture adds persistent storage and storage orchestration to a Kubernetes cluster. The example uses MongoDB as the demonstration of a stateful application. In practice, the workflow applies to any containerized application such as Cassandra, MySQL, Kafka, and Elasticsearch.

Using the reference architecture, a developer calls CodePipeline to trigger a snapshot of the running production MongoDB database. Portworx then creates a block-based, writable snapshot of the MongoDB volume. Meanwhile, the production MongoDB database continues serving end users and is uninterrupted.

Without the Portworx integrations, a manual process would require an application-level backup of the database instance that is outside of the CI/CD process. For larger databases, this could take hours and impact production. The use of block-based snapshots follows best practices for resilient and non-disruptive backups.

As part of the workflow, CodePipeline deploys a new MongoDB instance for staging onto the Kubernetes cluster and mounts the second Portworx volume that has the data from production. CodePipeline triggers the snapshot of a Portworx volume through an AWS Lambda function, as shown here

 

 

 

AWS Developer Tools with Kubernetes: Integrated Workflow with Portworx

In the following workflow, a developer is testing changes to a containerized application that calls on MongoDB. The tests are performed against a staging instance of MongoDB. The same workflow applies if changes were on the server side. The original production deployment is scheduled as a Kubernetes deployment object and uses Portworx as the storage for the persistent volume.

The continuous deployment pipeline runs as follows:

  • Developers integrate bug fix changes into a main development branch that gets merged into a CodeCommit master branch.
  • Amazon CloudWatch triggers the pipeline when code is merged into a master branch of an AWS CodeCommit repository.
  • AWS CodePipeline sends the new revision to AWS CodeBuild, which builds a Docker container image with the build ID.
  • AWS CodeBuild pushes the new Docker container image tagged with the build ID to an Amazon ECR registry.
  • Kubernetes downloads the new container (for the database client) from Amazon ECR and deploys the application (as a pod) and staging MongoDB instance (as a deployment object).
  • AWS CodePipeline, through a Lambda function, calls Portworx to snapshot the production MongoDB and deploy a staging instance of MongoDB• Portworx provides a snapshot of the production instance as the persistent storage of the staging MongoDB
    • The MongoDB instance mounts the snapshot.

At this point, the staging setup mimics a production environment. Teams can run integration and full end-to-end tests, using partner tooling, without impacting production workloads. The full pipeline is shown here.

 

Summary

This reference architecture showcases how development teams can easily move data between production and staging for the purposes of testing. Instead of taking application-specific manual steps, all operations in this CodePipeline architecture are automated and tracked as part of the CI/CD process.

This integrated experience is part of making stateful containers as easy as stateless. With AWS CodePipeline for CI/CD process, developers can easily deploy stateful containers onto a Kubernetes cluster with Portworx storage and automate data movement within their process.

The reference architecture and code are available on GitHub:

● Reference architecture: https://github.com/portworx/aws-kube-codesuite
● Lambda function source code for Portworx additions: https://github.com/portworx/aws-kube-codesuite/blob/master/src/kube-lambda.py

For more information about persistent storage for containers, visit the Portworx website. For more information about Code Pipeline, see the AWS CodePipeline User Guide.

Danish Traffic to Pirate Sites Increases 67% in Just a Year

Post Syndicated from Andy original https://torrentfreak.com/danish-traffic-to-pirate-sites-increases-67-in-just-a-year-180501/

For close to 20 years, rightsholders have tried to stem the tide of mainstream Internet piracy. Yet despite increasingly powerful enforcement tools, infringement continues on a grand scale.

While the problem is global, rightsholder groups often zoom in on their home turf, to see how the fight is progressing locally. Covering Denmark, the Rights Alliance Data Report 2017 paints a fairly pessimistic picture.

Published this week, the industry study – which uses SimilarWeb and MarkMonitor data – finds that Danes visited 2,000 leading pirate sites 596 million times in 2017. That represents a 67% increase over the 356 million visits to unlicensed platforms made by citizens during 2016.

The report notes that, at least in part, this explosive growth can be attributed to mobile-compatible sites and services, which make it easier than ever to consume illicit content on the move, as well as at home.

In a sea of unauthorized streaming sites, Rights Alliance highlights one platform above all the others as a particularly bad influence in 2017 – 123movies (also known as GoMovies and GoStream, among others).

“The popularity of this service rose sharply in 2017 from 40 million visits in 2016 to 175 million visits in 2017 – an increase of 337 percent, of which most of the traffic originates from mobile devices,” the report notes.

123movies recently announced its closure but before that the platform was subjected to web-blocking in several jurisdictions.

Rights Alliance says that Denmark has one of the most effective blocking systems in the world but that still doesn’t stop huge numbers of people from consuming pirate content from sites that aren’t yet blocked.

“Traffic to infringing sites is overwhelming, and therefore blocking a few sites merely takes the top of the illegal activities,” Rights Alliance chief Maria Fredenslund informs TorrentFreak.

“Blocking is effective by stopping 75% of traffic to blocked sites but certainly, an upscaled effort is necessary.”

Rights Alliance also views the promotion of legal services as crucial to its anti-piracy strategy so when people visit a blocked site, they’re also directed towards legitimate platforms.

“That is why we are working at the moment with Denmark’s Ministry of Culture and ISPs on a campaign ‘Share With Care 2′ which promotes legal services e.g. by offering a search function for legal services which will be placed in combination with the signs that are put on blocked websites,” the anti-piracy group notes.

But even with such measures in place, the thirst for unlicensed content is great. In 2017 alone, 500 of the most popular films and TV shows were downloaded from P2P networks like BitTorrent more than 15 million times from Danish IP addresses, that’s up from 11.9 million in 2016.

Given the dramatic rise in visits to pirate sites overall, the suggestion is that plenty of consumers are still getting through. Rights Alliance says that the number of people being restricted is also hampered by people who don’t use their ISP’s DNS service, which is the method used to block sites in Denmark.

Additionally, interest in VPNs and similar anonymization and bypass-capable technologies is on the increase. Between 3.5% and 5% of Danish Internet users currently use a VPN, a number that’s expected to go up. Furthermore, Rights Alliance reports greater interest in “closed” pirate communities.

“The data is based on closed [BitTorrent] networks. We also address the challenges with private communities on Facebook and other [social media] platforms,” Fredenslund explains.

“Due to the closed doors of these platforms it is not possible for us to say anything precisely about the amount of infringing activities there. However, we receive an increasing number of notices from our members who discover that their products are distributed illegally and also we do an increased monitoring of these platforms.”

But while more established technologies such as torrents and regular web-streaming continue in considerable volumes, newer IPTV-style services accessible via apps and dedicated platforms are also gaining traction.

“The volume of visitors to these services’ websites has been sharply rising in 2017 – an increase of 84 percent from January to December,” Rights Alliance notes.

“Even though the number of visitors does not say anything about actual consumption, as users usually only visit pages one time to download the program, the number gives an indication that the interest in IPTV is increasing.”

To combat this growth market, Rights Alliance says it wants to establish web-blockades against sites hosting the software applications.

Also on the up are visits to platforms offering live sports illegally. In 2017, Danish IP addresses made 2.96 million visits to these services, corresponding to almost 250,000 visits per month and representing an annual increase of 28%.

Rights Alliance informs TF that in future a ‘live’ blocking mechanism similar to the one used by the Premier League in the UK could be deployed in Denmark.

“We already have a dynamic blocking system, and we see an increasing demand for illegal TV products, so this could be a natural next step,” Fredenslund explains.

Another small but perhaps significant detail is how users are accessing pirate sites. According to the report, large volumes of people are now visiting platforms directly, with more than 50% doing so in preference to referrals from search engines such as Google.

In terms of deterrence, the Rights Alliance report sticks to the tried-and-tested approaches seen so often in the anti-piracy arena.

Firstly, the group notes that it’s increasingly encountering people who are paying for legal services such as Netflix and Spotify so believe that allows them to grab something extra from a pirate site. However, in common with similar organizations globally, the group counters that pirate sites can serve malware or have other nefarious business interests behind the scenes, so people should stay away.

Whether significant volumes will heed this advice will remain to be seen but if a 67% increase last year is any predictor of the future, piracy is here to stay – and then some. Rights Alliance says it is ready for the challenge but will need some assistance to achieve its goals.

“As it is evident from the traffic data, criminal activities are not something that we, private companies (right holders in cooperation with ISPs), can handle alone,” Fredenslund says.

“Therefore, we are very pleased that DK Government recently announced that the IP taskforce which was set down as a trial period has now been made permanent. In that regard it is important and necessary that the police will also obtain the authority to handle blocking of massively infringing websites. Police do not have the authority to carry out blocking as it is today.”

The full report is available here (Danish, pdf)

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

Former Judge Accuses IP Court of Using ‘Pirate’ Microsoft Software

Post Syndicated from Andy original https://torrentfreak.com/former-judge-accuses-ip-court-of-using-pirate-microsoft-software-180429/

While piracy of movies, TV shows, and music grabs most of the headlines, software piracy is a huge issue, from both consumer and commercial perspectives.

For many years, software such as Photoshop has been pirated on a grand scale and around the world, millions of computers rely on cracked and unlicensed copies of Microsoft’s Windows software.

One of the key drivers of this kind of piracy is the relative expense of software. Open source variants are nearly always available but big brand names always seem more popular due to their market penetration and perceived ease of use.

While using pirated software very rarely gets individuals into trouble, the same cannot be said of unlicensed commercial operators. That appears to be the case in Russia where somewhat ironically the Court for Intellectual Property Rights stands accused of copyright infringement.

A complaint filed by the Paragon law firm at the Prosecutor General’s Office of the Court for Intellectual Property Rights (CIP) alleges that the Court is illegally using Microsoft software, something which has the potential to affect the outcome of court cases involving the US-based software giant.

Paragon is representing Alexander Shmuratov, who is a former Assistant Judge at the Court for Intellectual Property Rights. Shmuratov worked at the Court for several years and claims that the computers there were being operated with expired licenses.

Shmuratov himself told Kommersant that he “saw the notice of an activation failure every day when using MS Office products” in intellectual property court.

A representative of the Prosecutor General’s Office confirmed that a complaint had been received but said it had been forwarded to the Ministry of Internal Affairs.

In respect of the counterfeit software claims, CIP categorically denies the allegations. CIP says that licenses for all Russian courts were purchased back in 2008 and remained in force until 2011. In 2013, Microsoft agreed to an extension.

Only adding more intrigue to the story, CIP Assistant chairman Catherine Ulyanova said that the initator of the complaint, former judge Alexander Shmuratov, was dismissed from the CIP because he provided false information about income. He later mounted a challenge against his dismissal but was unsuccessful.

Ulyanova said that Microsoft licensed all courts from 2006 for use of Windows and MS Office. The licenses were acquired through a third-party company and more licenses than necessary were purchased, with some licenses being redistributed for use by CIP in later years with the consent of Microsoft.

Kommersant was unable to confirm how licenses were paid for beyond December 2011 but apparently an “official confirmation letter from the Irish headquarters of Microsoft, which does not object to the transfer of CIP licenses” had been sent to the Court.

Responding to Shmuratov’s allegations that software he used hadn’t been activated, Ulyanova said that technical problems had no relationship with the existence of software licenses.

The question of whether the Court is properly licensed will be determined at a later date but observers are already raising questions concerning CIP’s historical dealings with Microsoft not only in terms of licensing, but in cases it handled.

In the period 2014-2017, the Court for Intellectual Property Rights handled around 80 cases involving Microsoft and claims of between 50 thousand ($800) and several million rubles.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

Secure Build with AWS CodeBuild and LayeredInsight

Post Syndicated from Asif Khan original https://aws.amazon.com/blogs/devops/secure-build-with-aws-codebuild-and-layeredinsight/

This post is written by Asif Awan, Chief Technology Officer of Layered InsightSubin Mathew – Software Development Manager for AWS CodeBuild, and Asif Khan – Solutions Architect

Enterprises adopt containers because they recognize the benefits: speed, agility, portability, and high compute density. They understand how accelerating application delivery and deployment pipelines makes it possible to rapidly slipstream new features to customers. Although the benefits are indisputable, this acceleration raises concerns about security and corporate compliance with software governance. In this blog post, I provide a solution that shows how Layered Insight, the pioneer and global leader in container-native application protection, can be used with seamless application build and delivery pipelines like those available in AWS CodeBuild to address these concerns.

Layered Insight solutions

Layered Insight enables organizations to unify DevOps and SecOps by providing complete visibility and control of containerized applications. Using the industry’s first embedded security approach, Layered Insight solves the challenges of container performance and protection by providing accurate insight into container images, adaptive analysis of running containers, and automated enforcement of container behavior.

 

AWS CodeBuild

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy. With CodeBuild, you don’t need to provision, manage, and scale your own build servers. CodeBuild scales continuously and processes multiple builds concurrently, so your builds are not left waiting in a queue. You can get started quickly by using prepackaged build environments, or you can create custom build environments that use your own build tools.

 

Problem Definition

Security and compliance concerns span the lifecycle of application containers. Common concerns include:

Visibility into the container images. You need to verify the software composition information of the container image to determine whether known vulnerabilities associated with any of the software packages and libraries are included in the container image.

Governance of container images is critical because only certain open source packages/libraries, of specific versions, should be included in the container images. You need support for mechanisms for blacklisting all container images that include a certain version of a software package/library, or only allowing open source software that come with a specific type of license (such as Apache, MIT, GPL, and so on). You need to be able to address challenges such as:

·       Defining the process for image compliance policies at the enterprise, department, and group levels.

·       Preventing the images that fail the compliance checks from being deployed in critical environments, such as staging, pre-prod, and production.

Visibility into running container instances is critical, including:

·       CPU and memory utilization.

·       Security of the build environment.

·       All activities (system, network, storage, and application layer) of the application code running in each container instance.

Protection of running container instances that is:

·       Zero-touch to the developers (not an SDK-based approach).

·       Zero touch to the DevOps team and doesn’t limit the portability of the containerized application.

·       This protection must retain the option to switch to a different container stack or orchestration layer, or even to a different Container as a Service (CaaS ).

·       And it must be a fully automated solution to SecOps, so that the SecOps team doesn’t have to manually analyze and define detailed blacklist and whitelist policies.

 

Solution Details

In AWS CodeCommit, we have three projects:
●     “Democode” is a simple Java application, with one buildspec to build the app into a Docker container (run by build-demo-image CodeBuild project), and another to instrument said container (instrument-image CodeBuild project). The resulting container is stored in ECR repo javatestasjavatest:20180415-layered. This instrumented container is running in AWS Fargate cluster demo-java-appand can be seen in the Layered Insight runtime console as the javatestapplication in us-east-1.
●     aws-codebuild-docker-imagesis a clone of the official aws-codebuild-docker-images repo on GitHub . This CodeCommit project is used by the build-python-builder CodeBuild project to build the python 3.3.6 codebuild image and is stored at the codebuild-python ECR repo. We then manually instructed the Layered Insight console to instrument the image.
●     scan-java-imagecontains just a buildspec.yml file. This file is used by the scan-java-image CodeBuild project to instruct Layered Assessment to perform a vulnerability scan of the javatest container image built previously, and then run the scan results through a compliance policy that states there should be no medium vulnerabilities. This build fails — but in this case that is a success: the scan completes successfully, but compliance fails as there are medium-level issues found in the scan.

This build is performed using the instrumented version of the Python 3.3.6 CodeBuild image, so the activity of the processes running within the build are recorded each time within the LI console.

Build container image

Create or use a CodeCommit project with your application. To build this image and store it in Amazon Elastic Container Registry (Amazon ECR), add a buildspec file to the project and build a container image and create a CodeBuild project.

Scan container image

Once the image is built, create a new buildspec in the same project or a new one that looks similar to below (update ECR URL as necessary):

version: 0.2
phases:
  pre_build:
    commands:
      - echo Pulling down LI Scan API client scripts
      - git clone https://github.com/LayeredInsight/scan-api-example-python.git
      - echo Setting up LI Scan API client
      - cd scan-api-example-python
      - pip install layint_scan_api
      - pip install -r requirements.txt
  build:
    commands:
      - echo Scanning container started on `date`
      - IMAGEID=$(./li_add_image --name <aws-region>.amazonaws.com/javatest:20180415)
      - ./li_wait_for_scan -v --imageid $IMAGEID
      - ./li_run_image_compliance -v --imageid $IMAGEID --policyid PB15260f1acb6b2aa5b597e9d22feffb538256a01fbb4e5a95

Add the buildspec file to the git repo, push it, and then build a CodeBuild project using with the instrumented Python 3.3.6 CodeBuild image at <aws-region>.amazonaws.com/codebuild-python:3.3.6-layered. Set the following environment variables in the CodeBuild project:
●     LI_APPLICATIONNAME – name of the build to display
●     LI_LOCATION – location of the build project to display
●     LI_API_KEY – ApiKey:<key-name>:<api-key>
●     LI_API_HOST – location of the Layered Insight API service

Instrument container image

Next, to instrument the new container image:

  1. In the Layered Insight runtime console, ensure that the ECR registry and credentials are defined (click the Setup icon and the ‘+’ sign on the top right of the screen to add a new container registry). Note the name given to the registry in the console, as this needs to be referenced in the li_add_imagecommand in the script, below.
  2. Next, add a new buildspec (with a new name) to the CodeCommit project, such as the one shown below. This code will download the Layered Insight runtime client, and use it to instruct the Layered Insight service to instrument the image that was just built:
    version: 0.2
    phases:
    pre_build:
    commands:
    echo Pulling down LI API Runtime client scripts
    git clone https://github.com/LayeredInsight/runtime-api-example-python
    echo Setting up LI API client
    cd runtime-api-example-python
    pip install layint-runtime-api
    pip install -r requirements.txt
    build:
    commands:
    echo Instrumentation started on `date`
    ./li_add_image --registry "Javatest ECR" --name IMAGE_NAME:TAG --description "IMAGE DESCRIPTION" --policy "Default Policy" --instrument --wait --verbose
  3. Commit and push the new buildspec file.
  4. Going back to CodeBuild, create a new project, with the same CodeCommit repo, but this time select the new buildspec file. Use a Python 3.3.6 builder – either the AWS or LI Instrumented version.
  5. Click Continue
  6. Click Save
  7. Run the build, again on the master branch.
  8. If everything runs successfully, a new image should appear in the ECR registry with a -layered suffix. This is the instrumented image.

Run instrumented container image

When the instrumented container is now run — in ECS, Fargate, or elsewhere — it will log data back to the Layered Insight runtime console. It’s appearance in the console can be modified by setting the LI_APPLICATIONNAME and LI_LOCATION environment variables when running the container.

Conclusion

In the above blog we have provided you steps needed to embed governance and runtime security in your build pipelines running on AWS CodeBuild using Layered Insight.

 

 

 

No, Ray Ozzie hasn’t solved crypto backdoors

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/04/no-ray-ozzie-hasnt-solved-crypto.html

According to this Wired article, Ray Ozzie may have a solution to the crypto backdoor problem. No, he hasn’t. He’s only solving the part we already know how to solve. He’s deliberately ignoring the stuff we don’t know how to solve. We know how to make backdoors, we just don’t know how to secure them.

The vault doesn’t scale

Yes, Apple has a vault where they’ve successfully protected important keys. No, it doesn’t mean this vault scales. The more people and the more often you have to touch the vault, the less secure it becomes. We are talking thousands of requests per day from 100,000 different law enforcement agencies around the world. We are unlikely to protect this against incompetence and mistakes. We are definitely unable to secure this against deliberate attack.

A good analogy to Ozzie’s solution is LetsEncrypt for getting SSL certificates for your website, which is fairly scalable, using a private key locked in a vault for signing hundreds of thousands of certificates. That this scales seems to validate Ozzie’s proposal.

But at the same time, LetsEncrypt is easily subverted. LetsEncrypt uses DNS to verify your identity. But spoofing DNS is easy, as was recently shown in the recent BGP attack against a cryptocurrency. Attackers can create fraudulent SSL certificates with enough effort. We’ve got other protections against this, such as discovering and revoking the SSL bad certificate, so while damaging, it’s not catastrophic.

But with Ozzie’s scheme, equivalent attacks would be catastrophic, as it would lead to unlocking the phone and stealing all of somebody’s secrets.

In particular, consider what would happen if LetsEncrypt’s certificate was stolen (as Matthew Green points out). The consequence is that this would be detected and mass revocations would occur. If Ozzie’s master key were stolen, nothing would happen. Nobody would know, and evildoers would be able to freely decrypt phones. Ozzie claims his scheme can work because SSL works — but then his scheme includes none of the many protections necessary to make SSL work.

What I’m trying to show here is that in a lab, it all looks nice and pretty, but when attacked at scale, things break down — quickly. We have so much experience with failure at scale that we can judge Ozzie’s scheme as woefully incomplete. It’s not even up to the standard of SSL, and we have a long list of SSL problems.

Cryptography is about people more than math

We have a mathematically pure encryption algorithm called the “One Time Pad”. It can’t ever be broken, provably so with mathematics.

It’s also perfectly useless, as it’s not something humans can use. That’s why we use AES, which is vastly less secure (anything you encrypt today can probably be decrypted in 100 years). AES can be used by humans whereas One Time Pads cannot be. (I learned the fallacy of One Time Pad’s on my grandfather’s knee — he was a WW II codebreaker who broke German messages trying to futz with One Time Pads).

The same is true with Ozzie’s scheme. It focuses on the mathematical model but ignores the human element. We already know how to solve the mathematical problem in a hundred different ways. The part we don’t know how to secure is the human element.

How do we know the law enforcement person is who they say they are? How do we know the “trusted Apple employee” can’t be bribed? How can the law enforcement agent communicate securely with the Apple employee?

You think these things are theoretical, but they aren’t. Consider financial transactions. It used to be common that you could just email your bank/broker to wire funds into an account for such things as buying a house. Hackers have subverted that, intercepting messages, changing account numbers, and stealing millions. Most banks/brokers require additional verification before doing such transfers.

Let me repeat: Ozzie has only solved the part we already know how to solve. He hasn’t addressed these issues that confound us.

We still can’t secure security, much less secure backdoors

We already know how to decrypt iPhones: just wait a year or two for somebody to discover a vulnerability. FBI claims it’s “going dark”, but that’s only for timely decryption of phones. If they are willing to wait a year or two a vulnerability will eventually be found that allows decryption.

That’s what’s happened with the “GrayKey” device that’s been all over the news lately. Apple is fixing it so that it won’t work on new phones, but it works on old phones.

Ozzie’s solution is based on the assumption that iPhones are already secure against things like GrayKey. Like his assumption “if Apple already has a vault for private keys, then we have such vaults for backdoor keys”, Ozzie is saying “if Apple already had secure hardware/software to secure the phone, then we can use the same stuff to secure the backdoors”. But we don’t really have secure vaults and we don’t really have secure hardware/software to secure the phone.

Again, to stress this point, Ozzie is solving the part we already know how to solve, but ignoring the stuff we don’t know how to solve. His solution is insecure for the same reason phones are already insecure.

Locked phones aren’t the problem

Phones are general purpose computers. That means anybody can install an encryption app on the phone regardless of whatever other security the phone might provide. The police are powerless to stop this. Even if they make such encryption crime, then criminals will still use encryption.

That leads to a strange situation that the only data the FBI will be able to decrypt is that of people who believe they are innocent. Those who know they are guilty will install encryption apps like Signal that have no backdoors.

In the past this was rare, as people found learning new apps a barrier. These days, apps like Signal are so easy even drug dealers can figure out how to use them.

We know how to get Apple to give us a backdoor, just pass a law forcing them to. It may look like Ozzie’s scheme, it may be something more secure designed by Apple’s engineers. Sure, it will weaken security on the phone for everyone, but those who truly care will just install Signal. But again we are back to the problem that Ozzie’s solving the problem we know how to solve while ignoring the much larger problem, that of preventing people from installing their own encryption.

The FBI isn’t necessarily the problem

Ozzie phrases his solution in terms of U.S. law enforcement. Well, what about Europe? What about Russia? What about China? What about North Korea?

Technology is borderless. A solution in the United States that allows “legitimate” law enforcement requests will inevitably be used by repressive states for what we believe would be “illegitimate” law enforcement requests.

Ozzie sees himself as the hero helping law enforcement protect 300 million American citizens. He doesn’t see himself what he really is, the villain helping oppress 1.4 billion Chinese, 144 million Russians, and another couple billion living in oppressive governments around the world.

Conclusion

Ozzie pretends the problem is political, that he’s created a solution that appeases both sides. He hasn’t. He’s solved the problem we already know how to solve. He’s ignored all the problems we struggle with, the problems we claim make secure backdoors essentially impossible. I’ve listed some in this post, but there are many more. Any famous person can create a solution that convinces fawning editors at Wired Magazine, but if Ozzie wants to move forward he’s going to have to work harder to appease doubting cryptographers.

Serverless Architectures with AWS Lambda: Overview and Best Practices

Post Syndicated from Andrew Baird original https://aws.amazon.com/blogs/architecture/serverless-architectures-with-aws-lambda-overview-and-best-practices/

For some organizations, the idea of “going serverless” can be daunting. But with an understanding of best practices – and the right tools — many serverless applications can be fully functional with only a few lines of code and little else.

Examples of fully-serverless-application use cases include:

  • Web or mobile backends – Create fully-serverless, mobile applications or websites by creating user-facing content in a native mobile application or static web content in an S3 bucket. Then have your front-end content integrate with Amazon API Gateway as a backend service API. Lambda functions will then execute the business logic you’ve written for each of the API Gateway methods in your backend API.
  • Chatbots and virtual assistants – Build new serverless ways to interact with your customers, like customer support assistants and bots ready to engage customers on your company-run social media pages. The Amazon Alexa Skills Kit (ASK) and Amazon Lex have the ability to apply natural-language understanding to user-voice and freeform-text input so that a Lambda function you write can intelligently respond and engage with them.
  • Internet of Things (IoT) backends – AWS IoT has direct-integration for device messages to be routed to and processed by Lambda functions. That means you can implement serverless backends for highly secure, scalable IoT applications for uses like connected consumer appliances and intelligent manufacturing facilities.

Using AWS Lambda as the logic layer of a serverless application can enable faster development speed and greater experimentation – and innovation — than in a traditional, server-based environment.

We recently published the “Serverless Architectures with AWS Lambda: Overview and Best Practices” whitepaper to provide the guidance and best practices you need to write better Lambda functions and build better serverless architectures.

Once you’ve finished reading the whitepaper, below are a couple additional resources I recommend as your next step:

  1. If you would like to better understand some of the architecture pattern possibilities for serverless applications: Thirty Serverless Architectures in 30 Minutes (re:Invent 2017 video)
  2. If you’re ready to get hands-on and build a sample serverless application: AWS Serverless Workshops (GitHub Repository)
  3. If you’ve already built a serverless application and you’d like to ensure your application has been Well Architected: The Serverless Application Lens: AWS Well Architected Framework (Whitepaper)

About the Author

 

Andrew Baird is a Sr. Solutions Architect for AWS. Prior to becoming a Solutions Architect, Andrew was a developer, including time as an SDE with Amazon.com. He has worked on large-scale distributed systems, public-facing APIs, and operations automation.

Implement continuous integration and delivery of serverless AWS Glue ETL applications using AWS Developer Tools

Post Syndicated from Prasad Alle original https://aws.amazon.com/blogs/big-data/implement-continuous-integration-and-delivery-of-serverless-aws-glue-etl-applications-using-aws-developer-tools/

AWS Glue is an increasingly popular way to develop serverless ETL (extract, transform, and load) applications for big data and data lake workloads. Organizations that transform their ETL applications to cloud-based, serverless ETL architectures need a seamless, end-to-end continuous integration and continuous delivery (CI/CD) pipeline: from source code, to build, to deployment, to product delivery. Having a good CI/CD pipeline can help your organization discover bugs before they reach production and deliver updates more frequently. It can also help developers write quality code and automate the ETL job release management process, mitigate risk, and more.

AWS Glue is a fully managed data catalog and ETL service. It simplifies and automates the difficult and time-consuming tasks of data discovery, conversion, and job scheduling. AWS Glue crawls your data sources and constructs a data catalog using pre-built classifiers for popular data formats and data types, including CSV, Apache Parquet, JSON, and more.

When you are developing ETL applications using AWS Glue, you might come across some of the following CI/CD challenges:

  • Iterative development with unit tests
  • Continuous integration and build
  • Pushing the ETL pipeline to a test environment
  • Pushing the ETL pipeline to a production environment
  • Testing ETL applications using real data (live test)
  • Exploring and validating data

In this post, I walk you through a solution that implements a CI/CD pipeline for serverless AWS Glue ETL applications supported by AWS Developer Tools (including AWS CodePipeline, AWS CodeCommit, and AWS CodeBuild) and AWS CloudFormation.

Solution overview

The following diagram shows the pipeline workflow:

This solution uses AWS CodePipeline, which lets you orchestrate and automate the test and deploy stages for ETL application source code. The solution consists of a pipeline that contains the following stages:

1.) Source Control: In this stage, the AWS Glue ETL job source code and the AWS CloudFormation template file for deploying the ETL jobs are both committed to version control. I chose to use AWS CodeCommit for version control.

To get the ETL job source code and AWS CloudFormation template, download the gluedemoetl.zip file. This solution is developed based on a previous post, Build a Data Lake Foundation with AWS Glue and Amazon S3.

2.) LiveTest: In this stage, all resources—including AWS Glue crawlers, jobs, S3 buckets, roles, and other resources that are required for the solution—are provisioned, deployed, live tested, and cleaned up.

The LiveTest stage includes the following actions:

  • Deploy: In this action, all the resources that are required for this solution (crawlers, jobs, buckets, roles, and so on) are provisioned and deployed using an AWS CloudFormation template.
  • AutomatedLiveTest: In this action, all the AWS Glue crawlers and jobs are executed and data exploration and validation tests are performed. These validation tests include, but are not limited to, record counts in both raw tables and transformed tables in the data lake and any other business validations. I used AWS CodeBuild for this action.
  • LiveTestApproval: This action is included for the cases in which a pipeline administrator approval is required to deploy/promote the ETL applications to the next stage. The pipeline pauses in this action until an administrator manually approves the release.
  • LiveTestCleanup: In this action, all the LiveTest stage resources, including test crawlers, jobs, roles, and so on, are deleted using the AWS CloudFormation template. This action helps minimize cost by ensuring that the test resources exist only for the duration of the AutomatedLiveTest and LiveTestApproval

3.) DeployToProduction: In this stage, all the resources are deployed using the AWS CloudFormation template to the production environment.

Try it out

This code pipeline takes approximately 20 minutes to complete the LiveTest test stage (up to the LiveTest approval stage, in which manual approval is required).

To get started with this solution, choose Launch Stack:

This creates the CI/CD pipeline with all of its stages, as described earlier. It performs an initial commit of the sample AWS Glue ETL job source code to trigger the first release change.

In the AWS CloudFormation console, choose Create. After the template finishes creating resources, you see the pipeline name on the stack Outputs tab.

After that, open the CodePipeline console and select the newly created pipeline. Initially, your pipeline’s CodeCommit stage shows that the source action failed.

Allow a few minutes for your new pipeline to detect the initial commit applied by the CloudFormation stack creation. As soon as the commit is detected, your pipeline starts. You will see the successful stage completion status as soon as the CodeCommit source stage runs.

In the CodeCommit console, choose Code in the navigation pane to view the solution files.

Next, you can watch how the pipeline goes through the LiveTest stage of the deploy and AutomatedLiveTest actions, until it finally reaches the LiveTestApproval action.

At this point, if you check the AWS CloudFormation console, you can see that a new template has been deployed as part of the LiveTest deploy action.

At this point, make sure that the AWS Glue crawlers and the AWS Glue job ran successfully. Also check whether the corresponding databases and external tables have been created in the AWS Glue Data Catalog. Then verify that the data is validated using Amazon Athena, as shown following.

Open the AWS Glue console, and choose Databases in the navigation pane. You will see the following databases in the Data Catalog:

Open the Amazon Athena console, and run the following queries. Verify that the record counts are matching.

SELECT count(*) FROM "nycitytaxi_gluedemocicdtest"."data";
SELECT count(*) FROM "nytaxiparquet_gluedemocicdtest"."datalake";

The following shows the raw data:

The following shows the transformed data:

The pipeline pauses the action until the release is approved. After validating the data, manually approve the revision on the LiveTestApproval action on the CodePipeline console.

Add comments as needed, and choose Approve.

The LiveTestApproval stage now appears as Approved on the console.

After the revision is approved, the pipeline proceeds to use the AWS CloudFormation template to destroy the resources that were deployed in the LiveTest deploy action. This helps reduce cost and ensures a clean test environment on every deployment.

Production deployment is the final stage. In this stage, all the resources—AWS Glue crawlers, AWS Glue jobs, Amazon S3 buckets, roles, and so on—are provisioned and deployed to the production environment using the AWS CloudFormation template.

After successfully running the whole pipeline, feel free to experiment with it by changing the source code stored on AWS CodeCommit. For example, if you modify the AWS Glue ETL job to generate an error, it should make the AutomatedLiveTest action fail. Or if you change the AWS CloudFormation template to make its creation fail, it should affect the LiveTest deploy action. The objective of the pipeline is to guarantee that all changes that are deployed to production are guaranteed to work as expected.

Conclusion

In this post, you learned how easy it is to implement CI/CD for serverless AWS Glue ETL solutions with AWS developer tools like AWS CodePipeline and AWS CodeBuild at scale. Implementing such solutions can help you accelerate ETL development and testing at your organization.

If you have questions or suggestions, please comment below.

 


Additional Reading

If you found this post useful, be sure to check out Implement Continuous Integration and Delivery of Apache Spark Applications using AWS and Build a Data Lake Foundation with AWS Glue and Amazon S3.

 


About the Authors

Prasad Alle is a Senior Big Data Consultant with AWS Professional Services. He spends his time leading and building scalable, reliable Big data, Machine learning, Artificial Intelligence and IoT solutions for AWS Enterprise and Strategic customers. His interests extend to various technologies such as Advanced Edge Computing, Machine learning at Edge. In his spare time, he enjoys spending time with his family.

 
Luis Caro is a Big Data Consultant for AWS Professional Services. He works with our customers to provide guidance and technical assistance on big data projects, helping them improving the value of their solutions when using AWS.

 

 

 

Securing Elections

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/04/securing_electi_1.html

Elections serve two purposes. The first, and obvious, purpose is to accurately choose the winner. But the second is equally important: to convince the loser. To the extent that an election system is not transparently and auditably accurate, it fails in that second purpose. Our election systems are failing, and we need to fix them.

Today, we conduct our elections on computers. Our registration lists are in computer databases. We vote on computerized voting machines. And our tabulation and reporting is done on computers. We do this for a lot of good reasons, but a side effect is that elections now have all the insecurities inherent in computers. The only way to reliably protect elections from both malice and accident is to use something that is not hackable or unreliable at scale; the best way to do that is to back up as much of the system as possible with paper.

Recently, there have been two graphic demonstrations of how bad our computerized voting system is. In 2007, the states of California and Ohio conducted audits of their electronic voting machines. Expert review teams found exploitable vulnerabilities in almost every component they examined. The researchers were able to undetectably alter vote tallies, erase audit logs, and load malware on to the systems. Some of their attacks could be implemented by a single individual with no greater access than a normal poll worker; others could be done remotely.

Last year, the Defcon hackers’ conference sponsored a Voting Village. Organizers collected 25 pieces of voting equipment, including voting machines and electronic poll books. By the end of the weekend, conference attendees had found ways to compromise every piece of test equipment: to load malicious software, compromise vote tallies and audit logs, or cause equipment to fail.

It’s important to understand that these were not well-funded nation-state attackers. These were not even academics who had been studying the problem for weeks. These were bored hackers, with no experience with voting machines, playing around between parties one weekend.

It shouldn’t be any surprise that voting equipment, including voting machines, voter registration databases, and vote tabulation systems, are that hackable. They’re computers — often ancient computers running operating systems no longer supported by the manufacturers — and they don’t have any magical security technology that the rest of the industry isn’t privy to. If anything, they’re less secure than the computers we generally use, because their manufacturers hide any flaws behind the proprietary nature of their equipment.

We’re not just worried about altering the vote. Sometimes causing widespread failures, or even just sowing mistrust in the system, is enough. And an election whose results are not trusted or believed is a failed election.

Voting systems have another requirement that makes security even harder to achieve: the requirement for a secret ballot. Because we have to securely separate the election-roll system that determines who can vote from the system that collects and tabulates the votes, we can’t use the security systems available to banking and other high-value applications.

We can securely bank online, but can’t securely vote online. If we could do away with anonymity — if everyone could check that their vote was counted correctly — then it would be easy to secure the vote. But that would lead to other problems. Before the US had the secret ballot, voter coercion and vote-buying were widespread.

We can’t, so we need to accept that our voting systems are insecure. We need an election system that is resilient to the threats. And for many parts of the system, that means paper.

Let’s start with the voter rolls. We know they’ve already been targeted. In 2016, someone changed the party affiliation of hundreds of voters before the Republican primary. That’s just one possibility. A well-executed attack that deletes, for example, one in five voters at random — or changes their addresses — would cause chaos on election day.

Yes, we need to shore up the security of these systems. We need better computer, network, and database security for the various state voter organizations. We also need to better secure the voter registration websites, with better design and better internet security. We need better security for the companies that build and sell all this equipment.

Multiple, unchangeable backups are essential. A record of every addition, deletion, and change needs to be stored on a separate system, on write-only media like a DVD. Copies of that DVD, or — even better — a paper printout of the voter rolls, should be available at every polling place on election day. We need to be ready for anything.

Next, the voting machines themselves. Security researchers agree that the gold standard is a voter-verified paper ballot. The easiest (and cheapest) way to achieve this is through optical-scan voting. Voters mark paper ballots by hand; they are fed into a machine and counted automatically. That paper ballot is saved, and serves as a final true record in a recount in case of problems. Touch-screen machines that print a paper ballot to drop in a ballot box can also work for voters with disabilities, as long as the ballot can be easily read and verified by the voter.

Finally, the tabulation and reporting systems. Here again we need more security in the process, but we must always use those paper ballots as checks on the computers. A manual, post-election, risk-limiting audit varies the number of ballots examined according to the margin of victory. Conducting this audit after every election, before the results are certified, gives us confidence that the election outcome is correct, even if the voting machines and tabulation computers have been tampered with. Additionally, we need better coordination and communications when incidents occur.

It’s vital to agree on these procedures and policies before an election. Before the fact, when anyone can win and no one knows whose votes might be changed, it’s easy to agree on strong security. But after the vote, someone is the presumptive winner — and then everything changes. Half of the country wants the result to stand, and half wants it reversed. At that point, it’s too late to agree on anything.

The politicians running in the election shouldn’t have to argue their challenges in court. Getting elections right is in the interest of all citizens. Many countries have independent election commissions that are charged with conducting elections and ensuring their security. We don’t do that in the US.

Instead, we have representatives from each of our two parties in the room, keeping an eye on each other. That provided acceptable security against 20th-century threats, but is totally inadequate to secure our elections in the 21st century. And the belief that the diversity of voting systems in the US provides a measure of security is a dangerous myth, because few districts can be decisive and there are so few voting-machine vendors.

We can do better. In 2017, the Department of Homeland Security declared elections to be critical infrastructure, allowing the department to focus on securing them. On 23 March, Congress allocated $380m to states to upgrade election security.

These are good starts, but don’t go nearly far enough. The constitution delegates elections to the states but allows Congress to “make or alter such Regulations”. In 1845, Congress set a nationwide election day. Today, we need it to set uniform and strict election standards.

This essay originally appeared in the Guardian.

Hackspace magazine 6: Paper Engineering

Post Syndicated from Andrew Gregory original https://www.raspberrypi.org/blog/hackspace-magazine-6/

HackSpace magazine is back with our brand-new issue 6, available for you on shop shelves, in your inbox, and on our website right now.

Inside Hackspace magazine 6

Paper is probably the first thing you ever used for making, and for good reason: in no other medium can you iterate through 20 designs at the cost of only a few pennies. We’ve roped in Rob Ives to show us how to make a barking paper dog with moveable parts and a cam mechanism. Even better, the magazine includes this free paper automaton for you to make yourself. That’s right: free!

At the other end of the scale, there’s the forge, where heat, light, and noise combine to create immutable steel. We speak to Alec Steele, YouTuber, blacksmith, and philosopher, about his amazingly beautiful Damascus steel creations, and about why there’s no difference between grinding a knife and blowing holes in a mountain to build a road through it.

HackSpace magazine 6 Alec Steele

Do it yourself

You’ve heard of reading glasses — how about glasses that read for you? Using a camera, optical character recognition software, and a text-to-speech engine (and of course a Raspberry Pi to hold it all together), reader Andrew Lewis has hacked together his own system to help deal with age-related macular degeneration.

It’s the definition of hacking: here’s a problem, there’s no solution in the shops, so you go and build it yourself!

Radio

60 years ago, the cutting edge of home hacking was the transistor radio. Before the internet was dreamt of, the transistor radio made the world smaller and brought people together. Nowadays, the components you need to build a radio are cheap and easily available, so if you’re in any way electronically inclined, building a radio is an ideal excuse to dust off your soldering iron.

Tutorials

If you’re a 12-month subscriber (if you’re not, you really should be), you’ve no doubt been thinking of all sorts of things to do with the Adafruit Circuit Playground Express we gave you for free. How about a sewable circuit for a canvas bag? Use the accelerometer to detect patterns of movement — walking, for example — and flash a series of lights in response. It’s clever, fun, and an easy way to add some programmable fun to your shopping trips.


We’re also making gin, hacking a children’s toy car to unlock more features, and getting started with robot sumo to fill the void left by the cancellation of Robot Wars.

HackSpace magazine 6

All this, plus an 11-metre tall mechanical miner, in HackSpace magazine issue 6 — subscribe here from just £4 an issue or get the PDF version for free. You can also find HackSpace magazine in WHSmith, Tesco, Sainsbury’s, and independent newsagents in the UK. If you live in the US, check out your local Barnes & Noble, Fry’s, or Micro Center next week. We’re also shipping to stores in Australia, Hong Kong, Canada, Singapore, Belgium, and Brazil, so be sure to ask your local newsagent whether they’ll be getting HackSpace magazine.

The post Hackspace magazine 6: Paper Engineering appeared first on Raspberry Pi.

The End of Google Cloud Messaging, and What it Means for Your Apps

Post Syndicated from Zach Barbitta original https://aws.amazon.com/blogs/messaging-and-targeting/the-end-of-google-cloud-messaging-and-what-it-means-for-your-apps/

On April 10, 2018, Google announced the deprecation of its Google Cloud Messaging (GCM) platform. Specifically, the GCM server and client APIs are deprecated and will be removed as soon as April 11, 2019.  What does this mean for you and your applications that use Amazon Simple Notification Service (Amazon SNS) or Amazon Pinpoint?

First, nothing will break now or after April 11, 2019. GCM device tokens are completely interchangeable with the newer Firebase Cloud Messaging (FCM) device tokens. If you have existing GCM tokens, you’ll still be able to use them to send notifications. This statement is also true for GCM tokens that you generate in the future.

On the back end, we’ve already migrated Amazon SNS and Amazon Pinpoint to the server endpoint for FCM (https://fcm.googleapis.com/fcm/send). As a developer, you don’t need to make any changes as a result of this deprecation.

We created the following mini-FAQ to address some of the questions you may have as a developer who uses Amazon SNS or Amazon Pinpoint.

If I migrate to FCM from GCM, can I still use Amazon Pinpoint and Amazon SNS?

Yes. Your ability to connect to your applications and send messages through both Amazon SNS and Amazon Pinpoint doesn’t change. We’ll update the documentation for Amazon SNS and Amazon Pinpoint soon to reflect these changes.

If I don’t migrate to FCM from GCM, can I still use Amazon Pinpoint and Amazon SNS?

Yes. If you do nothing, your existing credentials and GCM tokens will still be valid. All applications that you previously set up to use Amazon Pinpoint or Amazon SNS will continue to work normally. When you call the API for Amazon Pinpoint or Amazon SNS, we initiate a request to the FCM server endpoint directly.

What are the differences between Amazon SNS and Amazon Pinpoint?

Amazon SNS makes it easy for developers to set up, operate, and send notifications at scale, affordably and with a high degree of flexibility. Amazon Pinpoint has many of the same messaging capabilities as Amazon SNS, with the same levels of scalability and flexibility.

The main difference between the two services is that Amazon Pinpoint provides both transactional and targeted messaging capabilities. By using Amazon Pinpoint, marketers and developers can not only send transactional messages to their customers, but can also segment their audiences, create campaigns, and analyze both application and message metrics.

How do I migrate from GCM to FCM?

For more information about migrating from GCM to FCM, see Migrate a GCM Client App for Android to Firebase Cloud Messaging on the Google Developers site.

If you have any questions, please post them in the comments section, or in the Amazon Pinpoint or Amazon SNS forums.

Confused About the Hybrid Cloud? You’re Not Alone

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/confused-about-the-hybrid-cloud-youre-not-alone/

Hybrid Cloud. What is it?

Do you have a clear understanding of the hybrid cloud? If you don’t, it’s not surprising.

Hybrid cloud has been applied to a greater and more varied number of IT solutions than almost any other recent data management term. About the only thing that’s clear about the hybrid cloud is that the term hybrid cloud wasn’t invented by customers, but by vendors who wanted to hawk whatever solution du jour they happened to be pushing.

Let’s be honest. We’re in an industry that loves hype. We can’t resist grafting hyper, multi, ultra, and super and other prefixes onto the beginnings of words to entice customers with something new and shiny. The alphabet soup of cloud-related terms can include various options for where the cloud is located (on-premises, off-premises), whether the resources are private or shared in some degree (private, community, public), what type of services are offered (storage, computing), and what type of orchestrating software is used to manage the workflow and the resources. With so many moving parts, it’s no wonder potential users are confused.

Let’s take a step back, try to clear up the misconceptions, and come up with a basic understanding of what the hybrid cloud is. To be clear, this is our viewpoint. Others are free to do what they like, so bear that in mind.

So, What is the Hybrid Cloud?

The hybrid cloud refers to a cloud environment made up of a mixture of on-premises private cloud resources combined with third-party public cloud resources that use some kind of orchestration between them.

To get beyond the hype, let’s start with Forrester Research‘s idea of the hybrid cloud: “One or more public clouds connected to something in my data center. That thing could be a private cloud; that thing could just be traditional data center infrastructure.”

To put it simply, a hybrid cloud is a mash-up of on-premises and off-premises IT resources.

To expand on that a bit, we can say that the hybrid cloud refers to a cloud environment made up of a mixture of on-premises private cloud[1] resources combined with third-party public cloud resources that use some kind of orchestration[2] between them. The advantage of the hybrid cloud model is that it allows workloads and data to move between private and public clouds in a flexible way as demands, needs, and costs change, giving businesses greater flexibility and more options for data deployment and use.

In other words, if you have some IT resources in-house that you are replicating or augmenting with an external vendor, congrats, you have a hybrid cloud!

Private Cloud vs. Public Cloud

The cloud is really just a collection of purpose built servers. In a private cloud, the servers are dedicated to a single tenant or a group of related tenants. In a public cloud, the servers are shared between multiple unrelated tenants (customers). A public cloud is off-site, while a private cloud can be on-site or off-site — or on-prem or off-prem.

As an example, let’s look at a hybrid cloud meant for data storage, a hybrid data cloud. A company might set up a rule that says all accounting files that have not been touched in the last year are automatically moved off-prem to cloud storage to save cost and reduce the amount of storage needed on-site. The files are still available; they are just no longer stored on your local systems. The rules can be defined to fit an organization’s workflow and data retention policies.

The hybrid cloud concept also contains cloud computing. For example, at the end of the quarter, order processing application instances can be spun up off-premises in a hybrid computing cloud as needed to add to on-premises capacity.

Hybrid Cloud Benefits

If we accept that the hybrid cloud combines the best elements of private and public clouds, then the benefits of hybrid cloud solutions are clear, and we can identify the primary two benefits that result from the blending of private and public clouds.

Diagram of the Components of the Hybrid Cloud

Benefit 1: Flexibility and Scalability

Undoubtedly, the primary advantage of the hybrid cloud is its flexibility. It takes time and money to manage in-house IT infrastructure and adding capacity requires advance planning.

The cloud is ready and able to provide IT resources whenever needed on short notice. The term cloud bursting refers to the on-demand and temporary use of the public cloud when demand exceeds resources available in the private cloud. For example, some businesses experience seasonal spikes that can put an extra burden on private clouds. These spikes can be taken up by a public cloud. Demand also can vary with geographic location, events, or other variables. The public cloud provides the elasticity to deal with these and other anticipated and unanticipated IT loads. The alternative would be fixed cost investments in on-premises IT resources that might not be efficiently utilized.

For a data storage user, the on-premises private cloud storage provides, among other benefits, the highest speed access. For data that is not frequently accessed, or needed with the absolute lowest levels of latency, it makes sense for the organization to move it to a location that is secure, but less expensive. The data is still readily available, and the public cloud provides a better platform for sharing the data with specific clients, users, or with the general public.

Benefit 2: Cost Savings

The public cloud component of the hybrid cloud provides cost-effective IT resources without incurring capital expenses and labor costs. IT professionals can determine the best configuration, service provider, and location for each service, thereby cutting costs by matching the resource with the task best suited to it. Services can be easily scaled, redeployed, or reduced when necessary, saving costs through increased efficiency and avoiding unnecessary expenses.

Comparing Private vs Hybrid Cloud Storage Costs

To get an idea of the difference in storage costs between a purely on-premises solutions and one that uses a hybrid of private and public storage, we’ll present two scenarios. For each scenario we’ll use data storage amounts of 100 terabytes, 1 petabyte, and 2 petabytes. Each table is the same format, all we’ve done is change how the data is distributed: private (on-premises) cloud or public (off-premises) cloud. We are using the costs for our own B2 Cloud Storage in this example. The math can be adapted for any set of numbers you wish to use.

Scenario 1    100% of data on-premises storage

Data Stored
Data stored On-Premises: 100%100 TB1,000 TB2,000 TB
On-premises cost rangeMonthly Cost
Low — $12/TB/Month$1,200$12,000$24,000
High — $20/TB/Month$2,000$20,000$40,000

Scenario 2    20% of data on-premises with 80% public cloud storage (B2)

Data Stored
Data stored On-Premises: 20%20 TB200 TB400 TB
Data stored in Cloud: 80%80 TB800 TB1,600 TB
On-premises cost rangeMonthly Cost
Low — $12/TB/Month$240$2,400$4,800
High — $20/TB/Month$400$4,000$8,000
Public cloud cost rangeMonthly Cost
Low — $5/TB/Month (B2)$400$4,000$8,000
High — $20/TB/Month$1,600$16,000$32,000
On-premises + public cloud cost rangeMonthly Cost
Low$640$6,400$12,800
High$2,000$20,000$40,000

As can be seen in the numbers above, using a hybrid cloud solution and storing 80% of the data in the cloud with a provider such as Backblaze B2 can result in significant savings over storing only on-premises. For other cost scenarios, see the B2 Cost Calculator.

When Hybrid Might Not Always Be the Right Fit

There are circumstances where the hybrid cloud might not be the best solution. Smaller organizations operating on a tight IT budget might best be served by a purely public cloud solution. The cost of setting up and running private servers is substantial.

An application that requires the highest possible speed might not be suitable for hybrid, depending on the specific cloud implementation. While latency does play a factor in data storage for some users, it is less of a factor for uploading and downloading data than it is for organizations using the hybrid cloud for computing. Because Backblaze recognized the importance of speed and low-latency for customers wishing to use computing on data stored in B2, we directly connected our data centers with those of our computing partners, ensuring that latency would not be an issue even for a hybrid cloud computing solution.

It is essential to have a good understanding of workloads and their essential characteristics in order to make the hybrid cloud work well for you. Each application needs to be examined for the right mix of private cloud, public cloud, and traditional IT resources that fit the particular workload in order to benefit most from a hybrid cloud architecture.

The Hybrid Cloud Can Be a Win-Win Solution

From the high altitude perspective, any solution that enables an organization to respond in a flexible manner to IT demands is a win. Avoiding big upfront capital expenses for in-house IT infrastructure will appeal to the CFO. Being able to quickly spin up IT resources as they’re needed will appeal to the CTO and VP of Operations.

Should You Go Hybrid?

We’ve arrived at the bottom line and the question is, should you or your organization embrace hybrid cloud infrastructures?

According to 451 Research, by 2019, 69% of companies will operate in hybrid cloud environments, and 60% of workloads will be running in some form of hosted cloud service (up from 45% in 2017). That indicates that the benefits of the hybrid cloud appeal to a broad range of companies.

In Two Years, More Than Half of Workloads Will Run in Cloud

Clearly, depending on an organization’s needs, there are advantages to a hybrid solution. While it might have been possible to dismiss the hybrid cloud in the early days of the cloud as nothing more than a buzzword, that’s no longer true. The hybrid cloud has evolved beyond the marketing hype to offer real solutions for an increasingly complex and challenging IT environment.

If an organization approaches the hybrid cloud with sufficient planning and a structured approach, a hybrid cloud can deliver on-demand flexibility, empower legacy systems and applications with new capabilities, and become a catalyst for digital transformation. The result can be an elastic and responsive infrastructure that has the ability to quickly respond to changing demands of the business.

As data management professionals increasingly recognize the advantages of the hybrid cloud, we can expect more and more of them to embrace it as an essential part of their IT strategy.

Tell Us What You’re Doing with the Hybrid Cloud

Are you currently embracing the hybrid cloud, or are you still uncertain or hanging back because you’re satisfied with how things are currently? Maybe you’ve gone totally hybrid. We’d love to hear your comments below on how you’re dealing with the hybrid cloud.


[1] Private cloud can be on-premises or a dedicated off-premises facility.

[2] Hybrid cloud orchestration solutions are often proprietary, vertical, and task dependent.

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