AWS Introduces Batch Support for AWS Fargate

During the first week of the annual re:invent, AWS introduced the ability to specify AWS Fargate as a computing resource for AWS Batch jobs. With the AWS Batch support for AWS Fargate, customers will have a way to run jobs on serverless compute resources, fully-managed from job submission to completion.

AWS first introduced AWS Batch back in December 2016 as a fully managed batch computing service that enables developers, scientists, and engineers to quickly and efficiently run hundreds of thousands of batch computing jobs on AWS. With AWS Batch, customers no longer had to do the heavy lifting of batch workload management by creating compute environments, managing queues, and launching the appropriate compute resources for their jobs.

With the integration of AWS Batch with Fargate, users can run compute-intensive workloads such as ML inference, map-reduce analysis, and other batch workloads without spending time on image maintenance and right-sizing of compute and monitoring. By selecting Fargate or Fargate Spot as a compute resource type in Batch, submitting a Fargate-compatible job definition, users can immediately benefit from the serverless computing engine. With Fargate, every job receives the exact amount of CPU and memory that it requests (within allowed Fargate SKUs); hence, there is no wasted resource time or need to wait for EC2 instance launches.

#amazon #amazon web services #cloud #aws #aws fargate

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AWS Introduces Batch Support for AWS Fargate
Gordon  Matlala

Gordon Matlala

1617350820

New – Fully Serverless Batch Computing with AWS Batch Support for AWS Fargate

We launched  AWS Batch on December 2016 as a fully managed batch computing service that enables developers, scientists and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. With AWS Batch, you no longer need to install and manage batch computing software or server clusters to run your jobs. AWS Batch is designed to remove the heavy lifting of batch workload management by creating compute environments, managing queues, and launching the appropriate compute resources to run your jobs quickly and efficiently.

Today, we are happy to introduce the ability to specify  AWS Fargate as a computing resource for AWS Batch jobs. AWS Fargate is a serverless computing engine for containers that eliminates the need to provision and manage your own servers. With this enhancement, customers will now have a way to run their jobs on serverless computing resources: Simply submit your analysis, ML inference, map reduce analysis, and other batch workloads, and let Batch and Fargate handle the rest.

Basic Concept

Customers running batch workloads in the cloud have a variety of orchestration needs: for example, workloads need to be queued, submitted to a compute resource, given priorities, dependencies and retries need to be handled, compute needs to be scalable and available, and users need to account for utilization and resource management. While AWS Batch simplifies all the queuing, scheduling, and lifecycle management for customers, and even provisions and manages compute in the customer account, customers are looking for even more simplicity where they can get up and running in minutes. Time spent on image maintenance, right-sizing of compute, and monitoring is time not spent on applications. These customer needs have led us to develop Fargate integration, which we are pleased to announce today.

How It Works

Simply specify Fargate or Fargate Spot as the resource type in Batch and submit a Fargate job definition, and customers can now take advantage of the benefits of serverless computing without the need for image patching, isolation of VM boundaries, and calculation of the correct size.

To start, access the  AWS Management Console of AWS Batch. Select Compute environments and Create.
[Getting start

#aws batch #aws fargate #serverless

AWS Introduces Batch Support for AWS Fargate

During the first week of the annual re:invent, AWS introduced the ability to specify AWS Fargate as a computing resource for AWS Batch jobs. With the AWS Batch support for AWS Fargate, customers will have a way to run jobs on serverless compute resources, fully-managed from job submission to completion.

AWS first introduced AWS Batch back in December 2016 as a fully managed batch computing service that enables developers, scientists, and engineers to quickly and efficiently run hundreds of thousands of batch computing jobs on AWS. With AWS Batch, customers no longer had to do the heavy lifting of batch workload management by creating compute environments, managing queues, and launching the appropriate compute resources for their jobs.

With the integration of AWS Batch with Fargate, users can run compute-intensive workloads such as ML inference, map-reduce analysis, and other batch workloads without spending time on image maintenance and right-sizing of compute and monitoring. By selecting Fargate or Fargate Spot as a compute resource type in Batch, submitting a Fargate-compatible job definition, users can immediately benefit from the serverless computing engine. With Fargate, every job receives the exact amount of CPU and memory that it requests (within allowed Fargate SKUs); hence, there is no wasted resource time or need to wait for EC2 instance launches.

#amazon #amazon web services #cloud #aws #aws fargate

Seamus  Quitzon

Seamus Quitzon

1601341562

AWS Cost Allocation Tags and Cost Reduction

Bob had just arrived in the office for his first day of work as the newly hired chief technical officer when he was called into a conference room by the president, Martha, who immediately introduced him to the head of accounting, Amanda. They exchanged pleasantries, and then Martha got right down to business:

“Bob, we have several teams here developing software applications on Amazon and our bill is very high. We think it’s unnecessarily high, and we’d like you to look into it and bring it under control.”

Martha placed a screenshot of the Amazon Web Services (AWS) billing report on the table and pointed to it.

“This is a problem for us: We don’t know what we’re spending this money on, and we need to see more detail.”

Amanda chimed in, “Bob, look, we have financial dimensions that we use for reporting purposes, and I can provide you with some guidance regarding some information we’d really like to see such that the reports that are ultimately produced mirror these dimensions — if you can do this, it would really help us internally.”

“Bob, we can’t stress how important this is right now. These projects are becoming very expensive for our business,” Martha reiterated.

“How many projects do we have?” Bob inquired.

“We have four projects in total: two in the aviation division and two in the energy division. If it matters, the aviation division has 75 developers and the energy division has 25 developers,” the CEO responded.

Bob understood the problem and responded, “I’ll see what I can do and have some ideas. I might not be able to give you retrospective insight, but going forward, we should be able to get a better idea of what’s going on and start to bring the cost down.”

The meeting ended with Bob heading to find his desk. Cost allocation tags should help us, he thought to himself as he looked for someone who might know where his office is.

#aws #aws cloud #node js #cost optimization #aws cli #well architected framework #aws cost report #cost control #aws cost #aws tags

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Christa  Stehr

Christa Stehr

1598408880

How To Unite AWS KMS with Serverless Application Model (SAM)

The Basics

AWS KMS is a Key Management Service that let you create Cryptographic keys that you can use to encrypt and decrypt data and also other keys. You can read more about it here.

Important points about Keys

Please note that the customer master keys(CMK) generated can only be used to encrypt small amount of data like passwords, RSA key. You can use AWS KMS CMKs to generate, encrypt, and decrypt data keys. However, AWS KMS does not store, manage, or track your data keys, or perform cryptographic operations with data keys.

You must use and manage data keys outside of AWS KMS. KMS API uses AWS KMS CMK in the encryption operations and they cannot accept more than 4 KB (4096 bytes) of data. To encrypt application data, use the server-side encryption features of an AWS service, or a client-side encryption library, such as the AWS Encryption SDK or the Amazon S3 encryption client.

Scenario

We want to create signup and login forms for a website.

Passwords should be encrypted and stored in DynamoDB database.

What do we need?

  1. KMS key to encrypt and decrypt data
  2. DynamoDB table to store password.
  3. Lambda functions & APIs to process Login and Sign up forms.
  4. Sign up/ Login forms in HTML.

Lets Implement it as Serverless Application Model (SAM)!

Lets first create the Key that we will use to encrypt and decrypt password.

KmsKey:
    Type: AWS::KMS::Key
    Properties: 
      Description: CMK for encrypting and decrypting
      KeyPolicy:
        Version: '2012-10-17'
        Id: key-default-1
        Statement:
        - Sid: Enable IAM User Permissions
          Effect: Allow
          Principal:
            AWS: !Sub arn:aws:iam::${AWS::AccountId}:root
          Action: kms:*
          Resource: '*'
        - Sid: Allow administration of the key
          Effect: Allow
          Principal:
            AWS: !Sub arn:aws:iam::${AWS::AccountId}:user/${KeyAdmin}
          Action:
          - kms:Create*
          - kms:Describe*
          - kms:Enable*
          - kms:List*
          - kms:Put*
          - kms:Update*
          - kms:Revoke*
          - kms:Disable*
          - kms:Get*
          - kms:Delete*
          - kms:ScheduleKeyDeletion
          - kms:CancelKeyDeletion
          Resource: '*'
        - Sid: Allow use of the key
          Effect: Allow
          Principal:
            AWS: !Sub arn:aws:iam::${AWS::AccountId}:user/${KeyUser}
          Action:
          - kms:DescribeKey
          - kms:Encrypt
          - kms:Decrypt
          - kms:ReEncrypt*
          - kms:GenerateDataKey
          - kms:GenerateDataKeyWithoutPlaintext
          Resource: '*'

The important thing in above snippet is the KeyPolicy. KMS requires a Key Administrator and Key User. As a best practice your Key Administrator and Key User should be 2 separate user in your Organisation. We are allowing all permissions to the root users.

So if your key Administrator leaves the organisation, the root user will be able to delete this key. As you can see **KeyAdmin **can manage the key but not use it and KeyUser can only use the key. ${KeyAdmin} and **${KeyUser} **are parameters in the SAM template.

You would be asked to provide values for these parameters during SAM Deploy.

#aws #serverless #aws-sam #aws-key-management-service #aws-certification #aws-api-gateway #tutorial-for-beginners #aws-blogs