Optimizing AWS Performance Over the Long Term

Those tools are certainly part of an effective AWS performance optimization strategy. On their own, however, they are not enough to guarantee performance and reliability over the long haul.

That’s because tools and processes such as autoscaling and rightsizing only help you optimize the performance of applications that are running currently, or are about to be deployed. They do little to ensure that performance remains optimal—especially as cloud services, cost structures and architectures evolve.

With that challenge in mind, let’s discuss how to get the most out of AWS performance over the long term.

What is AWS Performance?

Before delving into long-term AWS performance strategies, let’s make clear what we mean by performance. A high-performing application is one that:

  • Meets or exceeds user expectations regarding responsiveness and speed.
  • Meets or exceeds SLA requirements for availability.
  • Is able to scale seamlessly as performance requirements change.
  • Does all of the above in a cost-efficient manner.

These aspects of a high-performing AWS application are important to emphasize because they reflect a holistic approach to performance. Sometimes performance is defined narrowly, in terms of application responsiveness and/or availability. Those are part of the performance equation, but in order to maximize performance over the long term, you need to think about other dimensions of performance, like the role played by scalability and cloud spend.

Long-Term AWS Performance Considerations

To optimize AWS performance over the long term, you must take a variety of factors into consideration.

Handling AWS Service Changes and Upgrades

AWS is constantly evolving. A performance strategy that works well today may not be as effective tomorrow if AWS introduces a new service or changes the features of an existing service.

The challenge here lies not just in updating your app and your cloud configuration as AWS changes how a particular service works. It also requires constantly evaluating whether the service you are using is the best fit for your application.

For example, think back about five years to when AWS Lambda (AWS’s serverless computing service) appeared. At the time, if you were running your apps inside virtual servers, you might have been able to improve overall performance by moving some of the workloads to serverless functions—not because anything in your virtual servers had become less efficient, but because better efficiency opportunities opened up with the addition of a new AWS service.

Thus, being constantly aware of the various services in AWS and the opportunities they offer is key to optimizing long-term performance.

Plan for Changing Cost Structures

AWS not only changes its menu of service offerings frequently, it also updates pricing. Since cost optimization is one key component of performance optimization, you must ensure that your cloud workloads are running in the most cost-efficient way even as cost structures change.

Staying aware of new cloud services (such as lower-cost S3 storage tiers) when they appear is one way to do this. But you also need visibility into the tremendously complex world of AWS pricing in all of its region-specific details.

Plan Long-Term for OSS Dependencies

Open-source software is something else that is constantly changing. If you rely on open source code to help run your AWS applications, you need to stay on top of changes in the projects you depend on.

If you are using the same version of an open-source application or module that you built into your application when you first deployed it, you may be missing out on performance optimization opportunities that have arisen since then.

Here again, constant awareness of and visibility into the complex open-source ecosystem is essential for ensuring that you are not missing out on optimization opportunities.

Application-Specific Performance Monitoring

Every application is a special snowflake and you need to keep that in mind when it comes to the way you track and optimize application performance.

The metrics or KPIs that work for optimizing the performance of one app may not be ideal for another. The pre-deployment testing routines you use for applications might vary, too.

So, make sure to tailor your monitoring strategy to your applications. Doing so is the only way to ensure you have the greatest, most relevant level of visibility into application performance over the long term.

Backup and Disaster Recovery

No matter how well your AWS workloads perform, sooner or later something will go wrong and they will fail. Whether the failure leads to a critical availability disruption or a mere hiccup hinges in large part on whether you have a backup and disaster recovery plan in place beforehand.

For AWS apps, an effective backup and recovery plan entails more than just backing up data to storage buckets and downloading it to rebuild servers. It should also take advantage of opportunities like the ability to create image-level backups of virtual servers and restore them automatically to EC2 if disaster strikes.

And of course, as AWS changes, you’ll need to keep your backup and recovery strategy in sync.

Changing SLA Requirements

Last but not least, keep your SLAs in mind. The SLAs you have to support today could change in the future—and by extension, so will the levels of availability and responsiveness that your users expect.

For this reason, make sure your AWS performance strategy is capable not only of meeting the SLAs you currently have in place, but also adapting and scaling as SLA requirements become more rigid.


Although we tend to think of performance within the context of the present, it’s really a long-tail game. Sure, you can autoscale your workloads or use rightsizing tools to help choose the best type of EC2 instance for right now. But truly maximizing performance requires you to think longer-term and be prepared to react with agility as new performance challenges and opportunities arise.

This post originally appeared on devops.com.

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