Tag Archives: SES

Opt-in to the new Amazon SES console experience

Post Syndicated from Simon Poile original https://aws.amazon.com/blogs/messaging-and-targeting/amazon-ses-console-opt-in/

Amazon Web Services (AWS) is pleased to announce the launch of the newly redesigned Amazon Simple Email Service (SES) console. With its streamlined look and feel, the new console makes it even easier for customers to leverage the speed, reliability, and flexibility that Amazon SES has to offer. Customers can access the new console experience via an opt-in link on the classic console.

Amazon SES now offers a new, optimized console to provide customers with a simpler, more intuitive way to create and manage their resources, collect sending activity data, and monitor reputation health. It also has a more robust set of configuration options and new features and functionality not previously available in the classic console.

Here are a few of the improvements customers can find in the new Amazon SES console:

Verified identities

Streamlines how customers manage their sender identities in Amazon SES. This is done by replacing the classic console’s identity management section with verified identities. Verified identities are a centralized place in which customers can view, create, and configure both domain and email address identities on one page. Other notable improvements include:

  • DKIM-based verification
    DKIM-based domain verification replaces the previous verification method which was based on TXT records. DomainKeys Identified Mail (DKIM) is an email authentication mechanism that receiving mail servers use to validate email. This new verification method offers customers the added benefit of enhancing their deliverability with DKIM-compliant email providers, and helping them achieve compliance with DMARC (Domain-based Message Authentication, Reporting and Conformance).
  • Amazon SES mailbox simulator
    The new mailbox simulator makes it significantly easier for customers to test how their applications handle different email sending scenarios. From a dropdown, customers select which scenario they’d like to simulate. Scenario options include bounces, complaints, and automatic out-of-office responses. The mailbox simulator provides customers with a safe environment in which to test their email sending capabilities.

Configuration sets

The new console makes it easier for customers to experience the benefits of using configuration sets. Configuration sets enable customers to capture and publish event data for specific segments of their email sending program. It also isolates IP reputation by segment by assigning dedicated IP pools. With a wider range of configuration options, such as reputation tracking and custom suppression options, customers get even more out of this powerful feature.

  • Default configuration set
    One important feature to highlight is the introduction of the default configuration set. By assigning a default configuration set to an identity, customers ensure that the assigned configuration set is always applied to messages sent from that identity at the time of sending. This enables customers to associate a dedicated IP pool or set up event publishing for an identity without having to modify their email headers.

Account dashboard

There is also an account dashboard for the new SES console. This feature provides customers with fast access to key information about their account, including sending limits and restrictions, and overall account health. A visual representation of the customer’s daily email usage helps them ensure that they aren’t approaching their sending limits. Additionally, customers who use the Amazon SES SMTP interface to send emails can visit the account dashboard to obtain or update their SMTP credentials.

Reputation metrics

The new reputation metrics page provides customers with high-level insight into historic bounce and complaint rates. This is viewed at both the account level and the configuration set level. Bounce and complaint rates are two important metrics that Amazon SES considers when assessing a customer’s sender reputation, as well as the overall health of their account.

The redesigned Amazon SES console, with its easy-to-use workflows, will not only enhance the customers’ on-boarding experience, it will also change the paradigms used for their on-going usage. The Amazon SES team remains committed to investing on behalf of our customers and empowering them to be productive anywhere, anytime. We invite you to opt in to the new Amazon SES console experience and let us know what you think.

[$] A filesystem “change journal” and other topics

Post Syndicated from jake original https://lwn.net/Articles/755277/rss

At the 2017 Linux Storage, Filesystem, and Memory-Management Summit
(LSFMM), Amir Goldstein presented his work
on adding a superblock watch mechanism to provide a scalable way to notify
applications
of changes in a filesystem. At the 2018 edition of LSFMM, he was back to
discuss adding NTFS-like change
journals
to the kernel in support of backup solutions of various
sorts. As a second topic for the session, he also wanted to discuss doing
more performance-regression testing
for filesystems.

AWS Online Tech Talks – June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-june-2018/

AWS Online Tech Talks – June 2018

Join us this month to learn about AWS services and solutions. New this month, we have a fireside chat with the GM of Amazon WorkSpaces and our 2nd episode of the “How to re:Invent” series. We’ll also cover best practices, deep dives, use cases and more! Join us and register today!

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

 

Analytics & Big Data

June 18, 2018 | 11:00 AM – 11:45 AM PTGet Started with Real-Time Streaming Data in Under 5 Minutes – Learn how to use Amazon Kinesis to capture, store, and analyze streaming data in real-time including IoT device data, VPC flow logs, and clickstream data.
June 20, 2018 | 11:00 AM – 11:45 AM PT – Insights For Everyone – Deploying Data across your Organization – Learn how to deploy data at scale using AWS Analytics and QuickSight’s new reader role and usage based pricing.

 

AWS re:Invent
June 13, 2018 | 05:00 PM – 05:30 PM PTEpisode 2: AWS re:Invent Breakout Content Secret Sauce – Hear from one of our own AWS content experts as we dive deep into the re:Invent content strategy and how we maintain a high bar.
Compute

June 25, 2018 | 01:00 PM – 01:45 PM PTAccelerating Containerized Workloads with Amazon EC2 Spot Instances – Learn how to efficiently deploy containerized workloads and easily manage clusters at any scale at a fraction of the cost with Spot Instances.

June 26, 2018 | 01:00 PM – 01:45 PM PTEnsuring Your Windows Server Workloads Are Well-Architected – Get the benefits, best practices and tools on running your Microsoft Workloads on AWS leveraging a well-architected approach.

 

Containers
June 25, 2018 | 09:00 AM – 09:45 AM PTRunning Kubernetes on AWS – Learn about the basics of running Kubernetes on AWS including how setup masters, networking, security, and add auto-scaling to your cluster.

 

Databases

June 18, 2018 | 01:00 PM – 01:45 PM PTOracle to Amazon Aurora Migration, Step by Step – Learn how to migrate your Oracle database to Amazon Aurora.
DevOps

June 20, 2018 | 09:00 AM – 09:45 AM PTSet Up a CI/CD Pipeline for Deploying Containers Using the AWS Developer Tools – Learn how to set up a CI/CD pipeline for deploying containers using the AWS Developer Tools.

 

Enterprise & Hybrid
June 18, 2018 | 09:00 AM – 09:45 AM PTDe-risking Enterprise Migration with AWS Managed Services – Learn how enterprise customers are de-risking cloud adoption with AWS Managed Services.

June 19, 2018 | 11:00 AM – 11:45 AM PTLaunch AWS Faster using Automated Landing Zones – Learn how the AWS Landing Zone can automate the set up of best practice baselines when setting up new

 

AWS Environments

June 21, 2018 | 11:00 AM – 11:45 AM PTLeading Your Team Through a Cloud Transformation – Learn how you can help lead your organization through a cloud transformation.

June 21, 2018 | 01:00 PM – 01:45 PM PTEnabling New Retail Customer Experiences with Big Data – Learn how AWS can help retailers realize actual value from their big data and deliver on differentiated retail customer experiences.

June 28, 2018 | 01:00 PM – 01:45 PM PTFireside Chat: End User Collaboration on AWS – Learn how End User Compute services can help you deliver access to desktops and applications anywhere, anytime, using any device.
IoT

June 27, 2018 | 11:00 AM – 11:45 AM PTAWS IoT in the Connected Home – Learn how to use AWS IoT to build innovative Connected Home products.

 

Machine Learning

June 19, 2018 | 09:00 AM – 09:45 AM PTIntegrating Amazon SageMaker into your Enterprise – Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment.

June 21, 2018 | 09:00 AM – 09:45 AM PTBuilding Text Analytics Applications on AWS using Amazon Comprehend – Learn how you can unlock the value of your unstructured data with NLP-based text analytics.

 

Management Tools

June 20, 2018 | 01:00 PM – 01:45 PM PTOptimizing Application Performance and Costs with Auto Scaling – Learn how selecting the right scaling option can help optimize application performance and costs.

 

Mobile
June 25, 2018 | 11:00 AM – 11:45 AM PTDrive User Engagement with Amazon Pinpoint – Learn how Amazon Pinpoint simplifies and streamlines effective user engagement.

 

Security, Identity & Compliance

June 26, 2018 | 09:00 AM – 09:45 AM PTUnderstanding AWS Secrets Manager – Learn how AWS Secrets Manager helps you rotate and manage access to secrets centrally.
June 28, 2018 | 09:00 AM – 09:45 AM PTUsing Amazon Inspector to Discover Potential Security Issues – See how Amazon Inspector can be used to discover security issues of your instances.

 

Serverless

June 19, 2018 | 01:00 PM – 01:45 PM PTProductionize Serverless Application Building and Deployments with AWS SAM – Learn expert tips and techniques for building and deploying serverless applications at scale with AWS SAM.

 

Storage

June 26, 2018 | 11:00 AM – 11:45 AM PTDeep Dive: Hybrid Cloud Storage with AWS Storage Gateway – Learn how you can reduce your on-premises infrastructure by using the AWS Storage Gateway to connecting your applications to the scalable and reliable AWS storage services.
June 27, 2018 | 01:00 PM – 01:45 PM PTChanging the Game: Extending Compute Capabilities to the Edge – Discover how to change the game for IIoT and edge analytics applications with AWS Snowball Edge plus enhanced Compute instances.
June 28, 2018 | 11:00 AM – 11:45 AM PTBig Data and Analytics Workloads on Amazon EFS – Get best practices and deployment advice for running big data and analytics workloads on Amazon EFS.

Friday Squid Blogging: Do Cephalopods Contain Alien DNA?

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/06/friday_squid_bl_627.html

Maybe not DNA, but biological somethings.

Cause of Cambrian explosion — Terrestrial or Cosmic?“:

Abstract: We review the salient evidence consistent with or predicted by the Hoyle-Wickramasinghe (H-W) thesis of Cometary (Cosmic) Biology. Much of this physical and biological evidence is multifactorial. One particular focus are the recent studies which date the emergence of the complex retroviruses of vertebrate lines at or just before the Cambrian Explosion of ~500 Ma. Such viruses are known to be plausibly associated with major evolutionary genomic processes. We believe this coincidence is not fortuitous but is consistent with a key prediction of H-W theory whereby major extinction-diversification evolutionary boundaries coincide with virus-bearing cometary-bolide bombardment events. A second focus is the remarkable evolution of intelligent complexity (Cephalopods) culminating in the emergence of the Octopus. A third focus concerns the micro-organism fossil evidence contained within meteorites as well as the detection in the upper atmosphere of apparent incoming life-bearing particles from space. In our view the totality of the multifactorial data and critical analyses assembled by Fred Hoyle, Chandra Wickramasinghe and their many colleagues since the 1960s leads to a very plausible conclusion — life may have been seeded here on Earth by life-bearing comets as soon as conditions on Earth allowed it to flourish (about or just before 4.1 Billion years ago); and living organisms such as space-resistant and space-hardy bacteria, viruses, more complex eukaryotic cells, fertilised ova and seeds have been continuously delivered ever since to Earth so being one important driver of further terrestrial evolution which has resulted in considerable genetic diversity and which has led to the emergence of mankind.

Two commentaries.

This is almost certainly not true.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

Some quick thoughts on the public discussion regarding facial recognition and Amazon Rekognition this past week

Post Syndicated from Dr. Matt Wood original https://aws.amazon.com/blogs/aws/some-quick-thoughts-on-the-public-discussion-regarding-facial-recognition-and-amazon-rekognition-this-past-week/

We have seen a lot of discussion this past week about the role of Amazon Rekognition in facial recognition, surveillance, and civil liberties, and we wanted to share some thoughts.

Amazon Rekognition is a service we announced in 2016. It makes use of new technologies – such as deep learning – and puts them in the hands of developers in an easy-to-use, low-cost way. Since then, we have seen customers use the image and video analysis capabilities of Amazon Rekognition in ways that materially benefit both society (e.g. preventing human trafficking, inhibiting child exploitation, reuniting missing children with their families, and building educational apps for children), and organizations (enhancing security through multi-factor authentication, finding images more easily, or preventing package theft). Amazon Web Services (AWS) is not the only provider of services like these, and we remain excited about how image and video analysis can be a driver for good in the world, including in the public sector and law enforcement.

There have always been and will always be risks with new technology capabilities. Each organization choosing to employ technology must act responsibly or risk legal penalties and public condemnation. AWS takes its responsibilities seriously. But we believe it is the wrong approach to impose a ban on promising new technologies because they might be used by bad actors for nefarious purposes in the future. The world would be a very different place if we had restricted people from buying computers because it was possible to use that computer to do harm. The same can be said of thousands of technologies upon which we all rely each day. Through responsible use, the benefits have far outweighed the risks.

Customers are off to a great start with Amazon Rekognition; the evidence of the positive impact this new technology can provide is strong (and growing by the week), and we’re excited to continue to support our customers in its responsible use.

-Dr. Matt Wood, general manager of artificial intelligence at AWS

DNS over HTTPS in Firefox

Post Syndicated from corbet original https://lwn.net/Articles/756262/rss

The Mozilla blog has an
article
describing the addition of DNS over HTTPS (DoH) as an optional
feature in the Firefox browser. “DoH support has been added to
Firefox 62 to improve the way Firefox interacts with DNS. DoH uses
encrypted networking to obtain DNS information from a server that is
configured within Firefox. This means that DNS requests sent to the DoH
cloud server are encrypted while old style DNS requests are not
protected.
” The configured server is hosted by Cloudflare, which
has posted this
privacy agreement
about the service.

New – Pay-per-Session Pricing for Amazon QuickSight, Another Region, and Lots More

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-pay-per-session-pricing-for-amazon-quicksight-another-region-and-lots-more/

Amazon QuickSight is a fully managed cloud business intelligence system that gives you Fast & Easy to Use Business Analytics for Big Data. QuickSight makes business analytics available to organizations of all shapes and sizes, with the ability to access data that is stored in your Amazon Redshift data warehouse, your Amazon Relational Database Service (RDS) relational databases, flat files in S3, and (via connectors) data stored in on-premises MySQL, PostgreSQL, and SQL Server databases. QuickSight scales to accommodate tens, hundreds, or thousands of users per organization.

Today we are launching a new, session-based pricing option for QuickSight, along with additional region support and other important new features. Let’s take a look at each one:

Pay-per-Session Pricing
Our customers are making great use of QuickSight and take full advantage of the power it gives them to connect to data sources, create reports, and and explore visualizations.

However, not everyone in an organization needs or wants such powerful authoring capabilities. Having access to curated data in dashboards and being able to interact with the data by drilling down, filtering, or slicing-and-dicing is more than adequate for their needs. Subscribing them to a monthly or annual plan can be seen as an unwarranted expense, so a lot of such casual users end up not having access to interactive data or BI.

In order to allow customers to provide all of their users with interactive dashboards and reports, the Enterprise Edition of Amazon QuickSight now allows Reader access to dashboards on a Pay-per-Session basis. QuickSight users are now classified as Admins, Authors, or Readers, with distinct capabilities and prices:

Authors have access to the full power of QuickSight; they can establish database connections, upload new data, create ad hoc visualizations, and publish dashboards, all for $9 per month (Standard Edition) or $18 per month (Enterprise Edition).

Readers can view dashboards, slice and dice data using drill downs, filters and on-screen controls, and download data in CSV format, all within the secure QuickSight environment. Readers pay $0.30 for 30 minutes of access, with a monthly maximum of $5 per reader.

Admins have all authoring capabilities, and can manage users and purchase SPICE capacity in the account. The QuickSight admin now has the ability to set the desired option (Author or Reader) when they invite members of their organization to use QuickSight. They can extend Reader invites to their entire user base without incurring any up-front or monthly costs, paying only for the actual usage.

To learn more, visit the QuickSight Pricing page.

A New Region
QuickSight is now available in the Asia Pacific (Tokyo) Region:

The UI is in English, with a localized version in the works.

Hourly Data Refresh
Enterprise Edition SPICE data sets can now be set to refresh as frequently as every hour. In the past, each data set could be refreshed up to 5 times a day. To learn more, read Refreshing Imported Data.

Access to Data in Private VPCs
This feature was launched in preview form late last year, and is now available in production form to users of the Enterprise Edition. As I noted at the time, you can use it to implement secure, private communication with data sources that do not have public connectivity, including on-premises data in Teradata or SQL Server, accessed over an AWS Direct Connect link. To learn more, read Working with AWS VPC.

Parameters with On-Screen Controls
QuickSight dashboards can now include parameters that are set using on-screen dropdown, text box, numeric slider or date picker controls. The default value for each parameter can be set based on the user name (QuickSight calls this a dynamic default). You could, for example, set an appropriate default based on each user’s office location, department, or sales territory. Here’s an example:

To learn more, read about Parameters in QuickSight.

URL Actions for Linked Dashboards
You can now connect your QuickSight dashboards to external applications by defining URL actions on visuals. The actions can include parameters, and become available in the Details menu for the visual. URL actions are defined like this:

You can use this feature to link QuickSight dashboards to third party applications (e.g. Salesforce) or to your own internal applications. Read Custom URL Actions to learn how to use this feature.

Dashboard Sharing
You can now share QuickSight dashboards across every user in an account.

Larger SPICE Tables
The per-data set limit for SPICE tables has been raised from 10 GB to 25 GB.

Upgrade to Enterprise Edition
The QuickSight administrator can now upgrade an account from Standard Edition to Enterprise Edition with a click. This enables provisioning of Readers with pay-per-session pricing, private VPC access, row-level security for dashboards and data sets, and hourly refresh of data sets. Enterprise Edition pricing applies after the upgrade.

Available Now
Everything I listed above is available now and you can start using it today!

You can try QuickSight for 60 days at no charge, and you can also attend our June 20th Webinar.

Jeff;

 

Security updates for Thursday

Post Syndicated from ris original https://lwn.net/Articles/756164/rss

Security updates have been issued by CentOS (389-ds-base, corosync, firefox, java-1.7.0-openjdk, java-1.8.0-openjdk, kernel, librelp, libvirt, libvncserver, libvorbis, PackageKit, patch, pcs, and qemu-kvm), Fedora (asterisk, ca-certificates, gifsicle, ncurses, nodejs-base64-url, nodejs-mixin-deep, and wireshark), Mageia (thunderbird), Red Hat (procps), SUSE (curl, kvm, and libvirt), and Ubuntu (apport, haproxy, and tomcat7, tomcat8).

Amazon Neptune Generally Available

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/amazon-neptune-generally-available/

Amazon Neptune is now Generally Available in US East (N. Virginia), US East (Ohio), US West (Oregon), and EU (Ireland). Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. At the core of Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with millisecond latencies. Neptune supports two popular graph models, Property Graph and RDF, through Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune can be used to power everything from recommendation engines and knowledge graphs to drug discovery and network security. Neptune is fully-managed with automatic minor version upgrades, backups, encryption, and fail-over. I wrote about Neptune in detail for AWS re:Invent last year and customers have been using the preview and providing great feedback that the team has used to prepare the service for GA.

Now that Amazon Neptune is generally available there are a few changes from the preview:

Launching an Amazon Neptune Cluster

Launching a Neptune cluster is as easy as navigating to the AWS Management Console and clicking create cluster. Of course you can also launch with CloudFormation, the CLI, or the SDKs.

You can monitor your cluster health and the health of individual instances through Amazon CloudWatch and the console.

Additional Resources

We’ve created two repos with some additional tools and examples here. You can expect continuous development on these repos as we add additional tools and examples.

  • Amazon Neptune Tools Repo
    This repo has a useful tool for converting GraphML files into Neptune compatible CSVs for bulk loading from S3.
  • Amazon Neptune Samples Repo
    This repo has a really cool example of building a collaborative filtering recommendation engine for video game preferences.

Purpose Built Databases

There’s an industry trend where we’re moving more and more onto purpose-built databases. Developers and businesses want to access their data in the format that makes the most sense for their applications. As cloud resources make transforming large datasets easier with tools like AWS Glue, we have a lot more options than we used to for accessing our data. With tools like Amazon Redshift, Amazon Athena, Amazon Aurora, Amazon DynamoDB, and more we get to choose the best database for the job or even enable entirely new use-cases. Amazon Neptune is perfect for workloads where the data is highly connected across data rich edges.

I’m really excited about graph databases and I see a huge number of applications. Looking for ideas of cool things to build? I’d love to build a web crawler in AWS Lambda that uses Neptune as the backing store. You could further enrich it by running Amazon Comprehend or Amazon Rekognition on the text and images found and creating a search engine on top of Neptune.

As always, feel free to reach out in the comments or on twitter to provide any feedback!

Randall

Monitoring your Amazon SNS message filtering activity with Amazon CloudWatch

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/monitoring-your-amazon-sns-message-filtering-activity-with-amazon-cloudwatch/

This post is courtesy of Otavio Ferreira, Manager, Amazon SNS, AWS Messaging.

Amazon SNS message filtering provides a set of string and numeric matching operators that allow each subscription to receive only the messages of interest. Hence, SNS message filtering can simplify your pub/sub messaging architecture by offloading the message filtering logic from your subscriber systems, as well as the message routing logic from your publisher systems.

After you set the subscription attribute that defines a filter policy, the subscribing endpoint receives only the messages that carry attributes matching this filter policy. Other messages published to the topic are filtered out for this subscription. In this way, the native integration between SNS and Amazon CloudWatch provides visibility into the number of messages delivered, as well as the number of messages filtered out.

CloudWatch metrics are captured automatically for you. To get started with SNS message filtering, see Filtering Messages with Amazon SNS.

Message Filtering Metrics

The following six CloudWatch metrics are relevant to understanding your SNS message filtering activity:

  • NumberOfMessagesPublished – Inbound traffic to SNS. This metric tracks all the messages that have been published to the topic.
  • NumberOfNotificationsDelivered – Outbound traffic from SNS. This metric tracks all the messages that have been successfully delivered to endpoints subscribed to the topic. A delivery takes place either when the incoming message attributes match a subscription filter policy, or when the subscription has no filter policy at all, which results in a catch-all behavior.
  • NumberOfNotificationsFilteredOut – This metric tracks all the messages that were filtered out because they carried attributes that didn’t match the subscription filter policy.
  • NumberOfNotificationsFilteredOut-NoMessageAttributes – This metric tracks all the messages that were filtered out because they didn’t carry any attributes at all and, consequently, didn’t match the subscription filter policy.
  • NumberOfNotificationsFilteredOut-InvalidAttributes – This metric keeps track of messages that were filtered out because they carried invalid or malformed attributes and, thus, didn’t match the subscription filter policy.
  • NumberOfNotificationsFailed – This last metric tracks all the messages that failed to be delivered to subscribing endpoints, regardless of whether a filter policy had been set for the endpoint. This metric is emitted after the message delivery retry policy is exhausted, and SNS stops attempting to deliver the message. At that moment, the subscribing endpoint is likely no longer reachable. For example, the subscribing SQS queue or Lambda function has been deleted by its owner. You may want to closely monitor this metric to address message delivery issues quickly.

Message filtering graphs

Through the AWS Management Console, you can compose graphs to display your SNS message filtering activity. The graph shows the number of messages published, delivered, and filtered out within the timeframe you specify (1h, 3h, 12h, 1d, 3d, 1w, or custom).

SNS message filtering for CloudWatch Metrics

To compose an SNS message filtering graph with CloudWatch:

  1. Open the CloudWatch console.
  2. Choose Metrics, SNS, All Metrics, and Topic Metrics.
  3. Select all metrics to add to the graph, such as:
    • NumberOfMessagesPublished
    • NumberOfNotificationsDelivered
    • NumberOfNotificationsFilteredOut
  4. Choose Graphed metrics.
  5. In the Statistic column, switch from Average to Sum.
  6. Title your graph with a descriptive name, such as “SNS Message Filtering”

After you have your graph set up, you may want to copy the graph link for bookmarking, emailing, or sharing with co-workers. You may also want to add your graph to a CloudWatch dashboard for easy access in the future. Both actions are available to you on the Actions menu, which is found above the graph.

Summary

SNS message filtering defines how SNS topics behave in terms of message delivery. By using CloudWatch metrics, you gain visibility into the number of messages published, delivered, and filtered out. This enables you to validate the operation of filter policies and more easily troubleshoot during development phases.

SNS message filtering can be implemented easily with existing AWS SDKs by applying message and subscription attributes across all SNS supported protocols (Amazon SQS, AWS Lambda, HTTP, SMS, email, and mobile push). CloudWatch metrics for SNS message filtering is available now, in all AWS Regions.

For information about pricing, see the CloudWatch pricing page.

For more information, see:

A set of Git security releases

Post Syndicated from corbet original https://lwn.net/Articles/755935/rss

Git versions v2.17.1, v2.13.7, v2.14.4, v2.15.2 and v2.16.4 have all been
released with fixes to a couple of security issues. The nastier of the two
(CVE-2018-11235) enables arbitrary code execution controlled by a hostile
repository. See this
Microsoft blog entry
for more details — after updating.

[$] Stratis: Easy local storage management for Linux

Post Syndicated from jake original https://lwn.net/Articles/755454/rss

Stratis is a new local
storage-management solution for Linux. It can be compared to
ZFS, Btrfs, or LVM. Its focus is on simplicity of concepts and ease of use,
while giving users access to advanced storage features. Internally,
Stratis’s implementation favors tight integration of existing
components instead of the fully-integrated, in-kernel approach that ZFS and
Btrfs use. This has benefits and drawbacks for Stratis, but also greatly
decreases the overall time needed to develop a useful and stable initial
version, which can then be a base for further improvement in later
versions. Subscribers can read on for an introduction to Stratis, by guest
author (and Stratis team lead at Red Hat) Andy Grover.

Kidnapping Fraud

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/kidnapping_frau.html

Fake kidnapping fraud:

“Most commonly we have unsolicited calls to potential victims in Australia, purporting to represent the people in authority in China and suggesting to intending victims here they have been involved in some sort of offence in China or elsewhere, for which they’re being held responsible,” Commander McLean said.

The scammers threaten the students with deportation from Australia or some kind of criminal punishment.

The victims are then coerced into providing their identification details or money to get out of the supposed trouble they’re in.

Commander McLean said there are also cases where the student is told they have to hide in a hotel room, provide compromising photos of themselves and cut off all contact.

This simulates a kidnapping.

“So having tricked the victims in Australia into providing the photographs, and money and documents and other things, they then present the information back to the unknowing families in China to suggest that their children who are abroad are in trouble,” Commander McLean said.

“So quite circular in a sense…very skilled, very cunning.”

Recording lost seconds with the Augenblick blink camera

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/augenblick-camera/

Warning: a GIF used in today’s blog contains flashing images.

Students at the University of Bremen, Germany, have built a wearable camera that records the seconds of vision lost when you blink. Augenblick uses a Raspberry Pi Zero and Camera Module alongside muscle sensors to record footage whenever you close your eyes, producing a rather disjointed film of the sights you miss out on.

Augenblick blink camera recording using a Raspberry Pi Zero

Blink and you’ll miss it

The average person blinks up to five times a minute, with each blink lasting 0.5 to 0.8 seconds. These half-seconds add up to about 30 minutes a day. What sights are we losing during these minutes? That is the question asked by students Manasse Pinsuwan and René Henrich when they set out to design Augenblick.

Blinking is a highly invasive mechanism for our eyesight. Every day we close our eyes thousands of times without noticing it. Our mind manages to never let us wonder what exactly happens in the moments that we miss.

Capturing lost moments

For Augenblick, the wearer sticks MyoWare Muscle Sensor pads to their face, and these detect the electrical impulses that trigger blinking.

Augenblick blink camera recording using a Raspberry Pi Zero

Two pads are applied over the orbicularis oculi muscle that forms a ring around the eye socket, while the third pad is attached to the cheek as a neutral point.

Biology fact: there are two muscles responsible for blinking. The orbicularis oculi muscle closes the eye, while the levator palpebrae superioris muscle opens it — and yes, they both sound like the names of Harry Potter spells.

The sensor is read 25 times a second. Whenever it detects that the orbicularis oculi is active, the Camera Module records video footage.

Augenblick blink recording using a Raspberry Pi Zero

Pressing a button on the side of the Augenblick glasses set the code running. An LED lights up whenever the camera is recording and also serves to confirm the correct placement of the sensor pads.

Augenblick blink camera recording using a Raspberry Pi Zero

The Pi Zero saves the footage so that it can be stitched together later to form a continuous, if disjointed, film.

Learn more about the Augenblick blink camera

You can find more information on the conception, design, and build process of Augenblick here in German, with a shorter explanation including lots of photos here in English.

And if you’re keen to recreate this project, our free project resource for a wearable Pi Zero time-lapse camera will come in handy as a starting point.

The post Recording lost seconds with the Augenblick blink camera appeared first on Raspberry Pi.

Measuring the throughput for Amazon MQ using the JMS Benchmark

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/measuring-the-throughput-for-amazon-mq-using-the-jms-benchmark/

This post is courtesy of Alan Protasio, Software Development Engineer, Amazon Web Services

Just like compute and storage, messaging is a fundamental building block of enterprise applications. Message brokers (aka “message-oriented middleware”) enable different software systems, often written in different languages, on different platforms, running in different locations, to communicate and exchange information. Mission-critical applications, such as CRM and ERP, rely on message brokers to work.

A common performance consideration for customers deploying a message broker in a production environment is the throughput of the system, measured as messages per second. This is important to know so that application environments (hosts, threads, memory, etc.) can be configured correctly.

In this post, we demonstrate how to measure the throughput for Amazon MQ, a new managed message broker service for ActiveMQ, using JMS Benchmark. It should take between 15–20 minutes to set up the environment and an hour to run the benchmark. We also provide some tips on how to configure Amazon MQ for optimal throughput.

Benchmarking throughput for Amazon MQ

ActiveMQ can be used for a number of use cases. These use cases can range from simple fire and forget tasks (that is, asynchronous processing), low-latency request-reply patterns, to buffering requests before they are persisted to a database.

The throughput of Amazon MQ is largely dependent on the use case. For example, if you have non-critical workloads such as gathering click events for a non-business-critical portal, you can use ActiveMQ in a non-persistent mode and get extremely high throughput with Amazon MQ.

On the flip side, if you have a critical workload where durability is extremely important (meaning that you can’t lose a message), then you are bound by the I/O capacity of your underlying persistence store. We recommend using mq.m4.large for the best results. The mq.t2.micro instance type is intended for product evaluation. Performance is limited, due to the lower memory and burstable CPU performance.

Tip: To improve your throughput with Amazon MQ, make sure that you have consumers processing messaging as fast as (or faster than) your producers are pushing messages.

Because it’s impossible to talk about how the broker (ActiveMQ) behaves for each and every use case, we walk through how to set up your own benchmark for Amazon MQ using our favorite open-source benchmarking tool: JMS Benchmark. We are fans of the JMS Benchmark suite because it’s easy to set up and deploy, and comes with a built-in visualizer of the results.

Non-Persistent Scenarios – Queue latency as you scale producer throughput

JMS Benchmark nonpersistent scenarios

Getting started

At the time of publication, you can create an mq.m4.large single-instance broker for testing for $0.30 per hour (US pricing).

This walkthrough covers the following tasks:

  1.  Create and configure the broker.
  2. Create an EC2 instance to run your benchmark
  3. Configure the security groups
  4.  Run the benchmark.

Step 1 – Create and configure the broker
Create and configure the broker using Tutorial: Creating and Configuring an Amazon MQ Broker.

Step 2 – Create an EC2 instance to run your benchmark
Launch the EC2 instance using Step 1: Launch an Instance. We recommend choosing the m5.large instance type.

Step 3 – Configure the security groups
Make sure that all the security groups are correctly configured to let the traffic flow between the EC2 instance and your broker.

  1. Sign in to the Amazon MQ console.
  2. From the broker list, choose the name of your broker (for example, MyBroker)
  3. In the Details section, under Security and network, choose the name of your security group or choose the expand icon ( ).
  4. From the security group list, choose your security group.
  5. At the bottom of the page, choose Inbound, Edit.
  6. In the Edit inbound rules dialog box, add a role to allow traffic between your instance and the broker:
    • Choose Add Rule.
    • For Type, choose Custom TCP.
    • For Port Range, type the ActiveMQ SSL port (61617).
    • For Source, leave Custom selected and then type the security group of your EC2 instance.
    • Choose Save.

Your broker can now accept the connection from your EC2 instance.

Step 4 – Run the benchmark
Connect to your EC2 instance using SSH and run the following commands:

$ cd ~
$ curl -L https://github.com/alanprot/jms-benchmark/archive/master.zip -o master.zip
$ unzip master.zip
$ cd jms-benchmark-master
$ chmod a+x bin/*
$ env \
  SERVER_SETUP=false \
  SERVER_ADDRESS={activemq-endpoint} \
  ACTIVEMQ_TRANSPORT=ssl\
  ACTIVEMQ_PORT=61617 \
  ACTIVEMQ_USERNAME={activemq-user} \
  ACTIVEMQ_PASSWORD={activemq-password} \
  ./bin/benchmark-activemq

After the benchmark finishes, you can find the results in the ~/reports directory. As you may notice, the performance of ActiveMQ varies based on the number of consumers, producers, destinations, and message size.

Amazon MQ architecture

The last bit that’s important to know so that you can better understand the results of the benchmark is how Amazon MQ is architected.

Amazon MQ is architected to be highly available (HA) and durable. For HA, we recommend using the multi-AZ option. After a message is sent to Amazon MQ in persistent mode, the message is written to the highly durable message store that replicates the data across multiple nodes in multiple Availability Zones. Because of this replication, for some use cases you may see a reduction in throughput as you migrate to Amazon MQ. Customers have told us they appreciate the benefits of message replication as it helps protect durability even in the face of the loss of an Availability Zone.

Conclusion

We hope this gives you an idea of how Amazon MQ performs. We encourage you to run tests to simulate your own use cases.

To learn more, see the Amazon MQ website. You can try Amazon MQ for free with the AWS Free Tier, which includes up to 750 hours of a single-instance mq.t2.micro broker and up to 1 GB of storage per month for one year.

[$] Killing processes that don’t want to die

Post Syndicated from jake original https://lwn.net/Articles/754980/rss

Suppose you have a program running on your system that you don’t quite
trust. Maybe it’s a program submitted by a student to an automated
grading system. Or maybe it’s a QEMU device model running in a Xen
control domain ("domain 0" or “dom0”), and you want to make sure
that even
if an attacker from a rogue virtual machine manages to take over the QEMU
process,
they can’t do any further harm. There are many things you want to do as far
as restricting its ability
to do mischief. But one thing in particular you probably want to do
is to be able to reliably kill the process once you think it should be
done. This turns out to be quite a bit more tricky than you’d think.

Project Floofball and more: Pi pet stuff

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/project-floofball-pi-pet-stuff/

It’s a public holiday here today (yes, again). So, while we indulge in the traditional pastime of barbecuing stuff (ourselves, mainly), here’s a little trove of Pi projects that cater for our various furry friends.

Project Floofball

Nicole Horward created Project Floofball for her hamster, Harold. It’s an IoT hamster wheel that uses a Raspberry Pi and a magnetic door sensor to log how far Harold runs.

Project Floofball: an IoT hamster wheel

An IoT Hamsterwheel using a Raspberry Pi and a magnetic door sensor, to see how far my hamster runs.

You can follow Harold’s runs in real time on his ThingSpeak channel, and you’ll find photos of the build on imgur. Nicole’s Python code, as well as her template for the laser-cut enclosure that houses the wiring and LCD display, are available on the hamster wheel’s GitHub repo.

A live-streaming pet feeder

JaganK3 used to work long hours that meant he couldn’t be there to feed his dog on time. He found that he couldn’t buy an automated feeder in India without paying a lot to import one, so he made one himself. It uses a Raspberry Pi to control a motor that turns a dispensing valve in a hopper full of dry food, giving his dog a portion of food at set times.

A transparent cylindrical hopper of dry dog food, with a motor that can turn a dispensing valve at the lower end. The motor is connected to a Raspberry Pi in a plastic case. Hopper, motor, Pi, and wiring are all mounted on a board on the wall.

He also added a web cam for live video streaming, because he could. Find out more in JaganK3’s Instructable for his pet feeder.

Shark laser cat toy

Sam Storino, meanwhile, is using a Raspberry Pi to control a laser-pointer cat toy with a goshdarned SHARK (which is kind of what I’d expect from the guy who made the steampunk-looking cat feeder a few weeks ago). The idea is to keep his cats interested and active within the confines of a compact city apartment.

Raspberry Pi Automatic Cat Laser Pointer Toy

Post with 52 votes and 7004 views. Tagged with cat, shark, lasers, austin powers, raspberry pi; Shared by JeorgeLeatherly. Raspberry Pi Automatic Cat Laser Pointer Toy

If I were a cat, I would definitely be entirely happy with this. Find out more on Sam’s website.

And there’s more

Michel Parreno has written a series of articles to help you monitor and feed your pet with Raspberry Pi.

All of these makers are generous in acknowledging the tutorials and build logs that helped them with their projects. It’s lovely to see the Raspberry Pi and maker community working like this, and I bet their projects will inspire others too.

Now, if you’ll excuse me. I’m late for a barbecue.

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