Maximize Amazon Web Services With In-memory Computing

As we move to a data-driven world, the demand for systems that can handle continuously growing amounts of data becomes more apparent. Businesses have also realized the value of real-time analytics when it comes to making smart, data-driven business decisions. The rise of solid-state drives (SSD) presented a vast improvement when it comes to disk-based storage, but their performance doesn’t come close to that of in-memory computing.

In-memory solutions have shown that they can provide the fastest processing times and most responsive compute capabilities compared to other solutions available today. This is why digital transformation has been something that companies have been racing to achieve in recent years.

Although in-memory computing has been around for years, there are some organizations still reluctant to adopt the technology for fear of its complexity and the risks involved in migrating to a different platform.

Fortunately, it’s not that complicated to implement in-memory technology to your systems, especially if you’re using instances of Amazon Elastic Compute Cloud (AWS EC2). The benefits of in-memory computing will significantly boost the performance of Amazon Web Services (AWS) workloads while also reducing costs related to cloud resources used.

What is AWS EC2?

Elastic Compute Cloud or EC2 is an Amazon Web Service that provides organizations complete control over their computing resources and the reliability of running their systems on a proven environment. Designed to make it easier for developers to scale computing for the web, EC2 comes with a compute capacity that can be resized according to your needs and a simple web interface that allows for obtaining and configuring capacity with relative ease.

What makes AWS EC2 an ideal alternative is its deep compute platform that’s unmatched by other alternatives in the market today. It provides users the choice of operating system, processor, storage, networking, and purchase model so you can create a system that’s tailored to fit your system requirements.

For those looking to integrate in-memory computing into their AWS ecosystem, Amazon Machine Images (AMI) is a viable solution because it comes with AWS-ElastiCache and its variety of specialized file and database solutions. AMI allows developers to code in-memory capabilities into applications and provide them a reasonable approach to harnessing the potential value of in-memory computing.

The challenge here is that DevOps teams and AWS system administrators are usually not allowed to alter the code of the applications they manage, regardless of whether or not they are responsible for AWS budgets and management of EC2 workload goals.

This is where AMI comes in. To help developers deliver on the promise of the value of in-memory capabilities, they provision AMI’s that can optimize all instances of EC2 with in-memory capabilities for their managed workloads. Developers usually look for pre-configured AMI’s that ensure data persistence and cache consistency when building in-memory AMI’s customized for specific application performance requirements and availability targets.

They may also need to use the operating system’s kernel, the AWS hypervisor kernel, and other AWS resources in the coding of custom caching utilities into in-memory AMI solutions.

Pre-configured vs. Custom AMI’S

Whether you choose to build your own custom in-memory AMI or go to the AWS Marketplace for an in-memory EC2‐optimized AMI, processing speed isn’t the be-all and end-all goal. There are some essential factors that you need to consider:

  • Data persistence. If you require data to persist beyond the RAM cache, ensure that your AMI uses EBS-SSD. These automatically replicate themselves within their AWS Availability Zones to provide failover and protect stored data from component failure. Top-tier in-memory AMI’s will also maximize write-concurrency by leveraging the algorithms and hybrid caching options of an EBS-SSD.
  • Consistency. There are different consistency considerations when it comes to distributed workloads, and these are vital in minimizing the chances of cached data being inconsistent with its counterpart in the data persistence layer. It’s important to note that there will always be small differences due to latency; however, a good in-memory solution will minimize this issue to a degree that it won’t affect scalability, availability, and performance.
  • Single tenant caching. In a virtualized environment, contentions for RAM can overshadow the benefits of in-memory solutions if not addressed immediately. Such contentions should be mitigated at the hypervisor level. To make this possible, a portion of RAM should be dedicated to the deployed in-memory solution.
  • Simplicity. Arguably the most important consideration when choosing an in-memory computing solution, reducing the complexity of deployment ensures that the chosen solution’s value is immediately realized across the organization and its spectrum of IT needs, including applications, databases, and infrastructure services.

If you want to choose the path of least resistance, choosing a pre-configured in-memory AMI might be the best option, as it’s already optimized with all the necessary hypervisor and operating system utilities for EC2 instances and is likely the least expensive.

They also make use of other AWS components to provide an efficient in-memory computing platform for EC2 workloads. If, on the other hand, you have unique requirements, building your own solution is best. It may be more expensive and complex, but the investment will be worth it if it addresses concerns and provides use cases specific to your business.

Edward Huskin is a freelance data and analytics consultant. He specializes in finding the best technical solution for companies to manage their data and produce meaningful insights.