Our last post in this series has provided you with an overview of our example architecture on AWS. In this post, we are going into some more detail in focusing on elasticity using AWS EC2 (Elastic Compute Cloud), and in particular, we will see how to use AutoScaling to make your computing infrastructure elastic and highly available.
But what is that elasticity thing that people keep on going on about? According to Wikipedia elasticity is defined as “the degree to which a system is able to adapt to workload changes by provisioning and de-provisioning resources in an autonomic manner, such that at each point in time the available resources match the current demand as closely as possible.”
This is different from scalability, or, if you like, a specialization of scalability. Scalability provides the ability to increase (or decrease) the number of resources in scaling up (more powerful instances) or out (additional instances), which is usually done through manual intervention. Elasticity does the same but in an autonomic manner, independent from human interaction.
But what does that mean for EC2? Sometimes EC2 instances only tend to be considered as virtual machines that are hosted in the cloud. However, this doesn’t take into account the auxiliary services that come as part of EC2. Therefore it is missing one key enabler to elasticity as defined above: AutoScaling.
How AutoScaling works
AutoScaling has two components: Launch Configurations and Auto Scaling Groups.
- Launch Configurations hold the instructions for the creation of new instances. The instructions describe what type of instance AutoScaling needs to launch (e.g. t2.medium, m3.large), what Amazon Machine Image (AMI) the new instance is going to be based on, what roles or what storage is going to be associated with the instance, and so on.
- Scaling Groups, on the other hand, manage the scaling rules and logic, which are defined in policies. Those could be based on schedule or CloudWatch metrics. The CloudWatch service allows you to monitor all resources and applications that you have deployed on AWS. CloudWatch allows you to define alarms on metrics, which the AutoScaling policies subscribe to. Through the use of metrics you can for example implement rules that elastic scale your environment based on the performance of your deployed instances or traffic volumes on the network.
This doesn’t have to be the limit though. Since CloudWatch is collecting metrics from each and every resource deployed within your environment you can choose a variety of different sources as inputs to your scaling events. Assume you have deployed an application on EC2 that is processing requests from a queue like the Simple Queuing Service. With CloudWatch you can monitor the length of the queues and scale your computing environment in or out based on the number of items in the queue at the time. And since CloudWatch also supports the creation of custom metrics through the API, you can actually use any of your application logging outputs as a trigger for utility compute scaling events.
How to use AutoScaling to achieve elastic computing
Ignoring CloudWatch you can also use the AutoScaling APIs to amend your scaling configuration, trigger scaling events or define the health of an instance. Defining the health status of your instances allows you to go beyond the internal health checking that is done by AutoScaling, which is basically just confirming whether an instance is still running or not. As part of your internal application logic, you could set the health status as a result of certain error conditions. Once set to unhealthy, AutoScaling will take the instance out of service and spin up a fresh new instance instead.
Auto Scaling can also have use outside of the traditional elasticity needs. Auto Scaling is commonly used in a smaller environment to ensure that no less than a certain amount of instances are running at any point in time. So if you are just starting up with that flash new application that no one knows about just yet, or you are deploying an internal facing business application, it is still good practice to make those instances part of an Auto Scaling group. This brings a number of advantages with it.
Firstly and most importantly: you are forcing yourself to design your application in a way that lends itself to the paradigm of disposable infrastructure. Therefore you will ensure that no state or data is ever going to be stored on the instance.
Secondly, you ensure that the launch of a new instance is fully automated. While you may not yet start to use configuration management tools like Chef, Puppet or PowerShell DSC, you will set yourself on the right path in either maintaining a ‘master’ AMI image or make use of the default AMIs in combination with bootstrapping through the instance’ user data.
Finally, with the first two strategies implemented, you are ready to scale your environment in case that your idea becomes the hype of the month.
In summary, we have provided you with a variety of examples that allow you to understand the use of elasticity and scalability in relation to EC2 and provided you with a summary of the services involved.
For scaling, particular using elastic scaling you need to be conscious about the other services in your environment that form part of your solution. For example, you may need to consider whether your relational database can continue to respond to the increase in demand from the additional web or application servers. If you are utilizing the Elastic Load Balancer (ELB) to distribute the load between your instances, you need to be aware that the ELB is also designed as an elastic service, which is based on EC2. For huge spikes in demand, unfortunately, you don’t quite get the elasticity you would wish for. As you are ‘warming-up’ your own environment in spinning up new instances in anticipation for an expected increase in demand (e.g. through the launch of a marketing campaign), you are best to also contact the AWS support in advance of the expected spike to ensure that the ELB is ready to respond to the demand immediately.
You can learn more how to design a scalable and elastic infrastructure on AWS using Cloud Academy’s AWS training library. In particular, you might benefit from watching our course How to Architect with a Design for Failure Approach, where AutoScaling is used to help to achieve high availability and fault-tolerance in a common architecture.
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