Flexibility and scalability are vital for any business striving to stay competitive in a rapidly evolving landscape. Amazon Elastic Compute Cloud (EC2) offers solutions that cater to a diverse range of needs.
With so many available options, it’s important businesses are able to tailor their usage to specific workload requirements without overspending. This starts with an understanding of exactly what’s possible as well as what cost-optimization tools are available.
How to choose the right instance
When considering which instance you need, there are three things to consider: CPU, memory, and storage. The general-purpose instances (any M instance) balance compute, memory, and networking, which is ideal for web servers.
Compute-optimized instances (either C or H instances) are more suitable for workloads that require high performance, giving you the lowest cost per CPU core. These are good for compute-intensive applications such as scientific modeling, data processing, or gaming servers.
If the compute-optimized instances aren’t enough, you might need accelerated computing. The specifications of these instances vary widely, but typically, they use GPUs designed to handle high-performance workloads and deep learning.
Storage-optimized instances are appropriate for workloads that need high read and write capabilities for large amounts of data. Unlike other instances, these come with physically attached storage, making them ideal for NoSQL databases, and some real-time analytics.
Finally, memory-optimized instances are for workloads that need a lot of RAM. The optimization means you get a lower cost per unit of RAM, making them ideal for in-memory databases (Redis, SAP HANA, Memcached), caches, and simulations.
When choosing your instance, it’s important to think about your needs as well as the size of the instance. This is perhaps the most difficult step as choosing an instance that’s too small could disrupt your workload, while choosing one too large could lead to over-provisioning of resources.
Auto-scaling for ultimate flexibility
In order to avoid under- or over-provisioning of resources, EC2 Auto Scaling can add or remove compute capacity to meet demand. This not only optimizes performance but also reduces costs by combining purchase options and instance types. Auto Scaling uses predictive and dynamic policies to increase availability so you always have the right compute capacity. This helps you save money by ensuring you don’t have more instances than you need while ensuring uptime during peak times.
As you define the scaling policies, you get ultimate control over how this service adds and removes EC2 instances. This means it can scale in real-time or you can establish set demand patterns (such as a jump in traffic when everyone arrives at work). This level of automation is vital for smooth-running, cost-optimized workloads.
You can set up an Auto Scaling group to further automate this process based on your needs. Putting instances into a group allows for features like health check replacements and scaling policies, ensuring that the desired number of instances is always maintained. If an instance becomes unhealthy, it’s automatically replaced, and the group’s size can be manually or automatically adjusted to meet demand. The flexibility and automation provided by an Auto Scaling group make it a vital tool for cost optimization.
Leveraging spot instances
Spot instances allow you to save money over other EC2 instances but the catch is that AWS could pull the plug at any time. As a result, these are best used for non-critical workloads.
Spot instances might be difficult to manage for production workloads but the cost savings are often worth the risk of the instance being reprovisioned at short notice — especially if you have stateless workloads such as batch procession jobs, CI/CD operations, distributed databases, or anything that can handle interruptions.
Save costs by right-sizing your instances
Every instance type has different sizes available. As these are fixed, you pay for the entire instance, not just what you use. This means you could be overspending if you use an instance that’s larger than your needs. Auto Scaling can avoid over provisioning but it’s worth doing a check of your instances and right-sizing any that are larger than they need to be.
In matching your performance and capacity needs to the correct instance type, you can save money. Start by looking at your deployed instances and identify any opportunities where you could either remove or downsize without compromising workload requirements. This helps you balance your speed and performance needs with cost.
Bear in mind that while it’s easy to change the size of your instance, it’s tricky to change the type of instance, so getting set up with the right one based on the needs of your workload is a crucial first step.
Capacity planning
In understanding your capacity needs, you can better plan for the future. If you are running a workload that has variable needs, it’s important to configure Auto Scaling to work in your favor or potentially even use a burstable instance to give you a CPU boost as needed. If you can plan for these peaks in use, you’re going to be able to tailor your instance needs more closely.
Capacity optimization is also a powerful tool when it comes to reducing EC2 costs. Intel-Granulate’s continuous workload optimization, which can be found on the AWS Marketplace, helps do just this in a way that helps manage resources more effectively while reducing costs.
Amazon Auto Scaling and burstable EC2 instances are great ways to manage peak demand but shouldn’t be your first line of defense when it comes to optimization. Intel-Granulate can help you get more from your infrastructure by increasing speed and throughput before Auto Scaling policies need to take effect.
By continuously optimizing your workloads, when used in conjunction with Amazon’s cost optimization tools and strategies such as right-sizing, Intel-Granulate can help keep costs down without affecting performance.
Alone, Intel-Granulate could help you save as much as 45% while offering a 41% increase in throughput. If you’d like to learn more about how capacity optimization can help you cut instance costs, book a demo today.