The course is part of these learning paths
What is Compute?
Understanding the fundamentals of AWS is critical if you want to deploy services and resources within the AWS Cloud. The Compute category of services are key resources that allow you to carry out computational abilities via a series of instructions used by applications and systems. These resources cover a range of different services and features, these being:
- Amazon Elastic Compute Cloud (EC2)
- Elastic Load Balancing
- Auto Scaling
- Amazon EC2 Container Service (ECS)
- AWS Elastic Beanstalk
- AWS Lambda
- AWS Batch
- Amazon Lightsail
This course will provide the fundamental elements of all of these Compute services and features that will allow you to select the most appropriate service for your project and implementations. Each have their advantages by providing something of value that’s different to the others, which will all be discussed.
Topics covered within this course consist of:
- What is Compute: This lecture explains what 'Compute' is and what is meant by Compute resources and services
- Amazon Elastic Compute Cloud (EC2): This lecture discusses and explains what the EC2 service is and does, and provides a demonstration on how to configure, launch and connect to an EC2 instance
- Elastic Load Balancing & Auto Scaling: This lecture explains the differences between Elastic Load Balancing and Auto Scaling and how they can be used to help manage your fleet of EC2 Compute resources
- Amazon ECS: This lecture explains how the Amazon ECS service allows you to run Docker-enabled applications packaged as containers across a cluster of EC2 instances without requiring you to manage a complex and administratively heavy cluster management system
- AWS Elastic Beanstalk: This lecture provides an overview of the AWS Elastic Beanstalk service which helps to install, distribute and deploy web applications
- AWS Lambda: This lecture explains how AWS Lambda lets your run your own code in response to events in a scalable and highly available serverless environment
- AWS Batch: This lecture looks at AWS Batch and how this service is used to manage and run batch computing workloads within AWS
- Amazon Lightsail: This lecture looks at the Amazon Lightsail service which is essentially a Virtual Private Server (VPS) backed by AWS infrastructure
If you want to learn the differences between the different Compute services, then this course is for you!
With demonstrations provided, along with links to a number of our Labs that allow you to gain hands on experience in using many of these services will give you a solid understanding of the Compute services used within AWS.
If you have thoughts or suggestions for this course, please contact Cloud Academy at firstname.lastname@example.org.
Hello and welcome to this lecture where I will provide a high level overview of AWS Batch.
As the name suggests, this service is used to manage and run batch computing workloads within AWS. Before we go any further I just want to quickly clarify what batch computing is.
Batch computing is primarily used in specialist use cases which require a vast amount of compute power across a cluster of compute resources to complete batch processing executing a series of jobs or tasks.
Outside of a cloud environment, it can be difficult to maintain and manage a batch computing system. It requires specific software and requires the ability to consume the resources required which can be costly. However, with AWS Batch many of these constraints, administration, activities and maintenance tasks, are removed. You can seamlessly create a cluster of compute resources which is highly scalable, taking advantage of the elasticity of AWS, coping with any level of batch processing whilst optimizing the distribution of the workloads.
All provisioning, monitoring, maintenance, and management of the clusters themselves is taken care of by AWS, meaning there is no software to install by you. There are effectively five components that make up the AWS Batch service which will help you to start using the service. These being:
- jobs. A job is classed as a unit of work that is to be run by AWS Batch. For example, this can be a Linux executable file, an application within an ECS Cluster or a shell script. The jobs themselves run on EC2 instances as a containerized application. Each job at any one time can be in a number of different states. For example, submitted, pending, running, failed, among others.
- Job definitions, these are specific parameters for the jobs themselves. They dictate how the job will run and with what configuration. Some example of these may be how many vCPUs to use for the container, which data volume should be used, which IAM Role should be used allowing access for AWS Batch to communicate with other AWS services, and which mount points to use.
- Job queues. Jobs that are scheduled are placed into a job queue until they are run. It is also possible to have multiple queues with different priorities if needed. One queue could be used for On-demand EC2 instances and another queue could be used for Spot Instances. Both On-demand and Spot instances are supported by AWS Batch, allowing to optimize cost and AWS Batch can even bid on your behalf for those Spot instances.
- Job scheduling. The job scheduling takes control of when a job should be run and from which computer environment. Typically it will operate on a first-in-first-out basis. It will look at the different job queues that you have configured, ensuring the higher priority queues are run first assuming all dependencies of that job have been met.
- Compute environments. These are the environments containing the compute resources to carry out the job. The environment can be defined as managed or unmanaged. A managed environment means that the service itself will handle provisioning, scaling and termination of your compute instances based on configuration premises that you would enter regarding instance type and purchase method such as On-demand or Spot instances. This environment is then created as an Amazon ECS Cluster. Unmanaged environments are provisioned, managed and maintained by you which gives greater customization, however, it requires greater administration and maintenance and also requires you to create the necessary Amazon ECS Cluster that the managed environment would have done on your behalf.
If you have a requirement to run multiple jobs in parallel using batch computing. For example, to analyze financial risk models, perform media transcoding, or engineering simulations then AWS Batch will be a perfect solution.
Before I finish this lecture I just want to quickly recap at a higher level the distinction between EC2 Container Service, AWS Elastic Beanstalk, AWS Lambda, and AWS Batch. As they all offer the ability to deploy code in applications for the use of compute resources. And so I think now is a good time to quickly reiterate the key distinction of these four services.
The EC2 Container Service, this service allows you to undock enabled applications packaged as containers across a cluster of EC2 instances without requiring you to manage a complex and administratively heavy cluster management system.
AWS Elastic Beanstalk. AWS Elastic Beanstalk is an AWS manage service that will take your uploaded web application code and automatically provision and deploy the appropriate and necessary resources within AWS to make the web application operational. These resources can include other AWS services and features such as EC2, auto-scaling, application health monitoring, and elastic load balancing.
AWS Lambda. AWS Lambda is a service that lets you run your own code in response to events in a scalable and highly available serverless environment.
AWS Batch, this service is used to managing and run batch computing workloads. Batch computing requires a vast amount of compute power across a cluster of compute resources by processing and executing a series of jobs or tasks.
That brings us to the end of this lecture. Coming up next I will introduce you to the Amazon Lightsail service.
About the Author
Stuart has been working within the IT industry for two decades covering a huge range of topic areas and technologies, from data centre and network infrastructure design, to cloud architecture and implementation.
To date Stuart has created over 40 courses relating to Cloud, most within the AWS category with a heavy focus on security and compliance
He is AWS certified and accredited in addition to being a published author covering topics across the AWS landscape.
In January 2016 Stuart was awarded ‘Expert of the Year Award 2015’ from Experts Exchange for his knowledge share within cloud services to the community.
Stuart enjoys writing about cloud technologies and you will find many of his articles within our blog pages.