How to Scale End-to-End Observability in AWS Environments

AWS Batch Support Now Available for AWS Fargate

Jan. 5, 2021, 7:12 p.m.

TL;DR

In the first week of the AWS re-Invent 2020, AWS announced AWS Fargate as a computing resource for AWS Batch jobs. AWS Fargate is a serverless compute engine for containers that work with Amazon EKS (Elastic Kubernetes Service) as well as Amazon ECS (Elastic Container Service). AWS Batch enables developers and engineers to efficiently and easily run hundreds of thousands of batch computing jobs on AWS.

With AWS Batch support for AWS Fargate, it will now be possible to run jobs on serverless compute resources. Users can simply submit their analysis, ML inference, map reduce analysis, and other batch workloads, and let Batch and Fargate handle the rest.

AWS Batch is the go-to orchestration layer, especially for those jobs that have high compute requirements or high parallelism

Key Facts

1
AWS Batch was introduced in December 2016, eliminating the need for installation and management of batch computing software or server clusters to run batch jobs. It was a fully managed batch-computing service that simplified batch workload management by creating computing environments, queue management, and launching the right compute resources to run jobs quickly and efficiently.
2
When users select Fargate as a compute resource type in Batch, they can ensure that every job receives the same amount of CPU and memory that it requests. There is no wastage of resource time and users do not need to wait for EC2 instance launches.
3
By specifying Fargate as the resource type in Batch, customers can take advantage of serverless computing without the need for image patching, VM boundary isolation, and calculation of the correct size.
4
AWS Batch support for AWS Fargate is available in all regions that already have AWS Batch and AWS Fargate.

Details

Customers have many orchestration needs when running batch workloads in the cloud. AWS Batch is the go-to orchestration layer, especially for those jobs that have high compute requirements or high parallelism. If your workload uses or needs containers, you still had to do a lot of manual work to create an execution environment for these containers.

With AWS Batch support for AWS Fargate, Amazon takes away the responsibility of all this manual work, leaving customers with a lot more time to attend to business needs. With Fargate integration, customers no longer need to spend time on image maintenance, correcting the size of compute, and monitoring.

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. These customer needs have led us to develop Fargate integration.
Harunobu Kameda
Product Marketing Evangelist, AWS