Amazon EC2 is without a doubt the most widely used service out of the AWS’s in-depth service portfolio. It surely provides the secure and resizable compute capacity in the cloud. EC2 provides complete control over resources but users end-up losing control over the cost. Cost is the biggest concern for users when operating in cloud. This blog digs a little deeper into how to reduce EC2 Compute costs.
1. Fine-tune using Elastic Graphics:
AWS provides GPU-powered G instances for graphic-intensive applications. Apart from this, AWS provides Elastic Graphics to add graphics acceleration to EC2 instances. To avoid the higher cost pitfall, evaluate the EC2 instances with Elastic Graphics for your use case. You can simply choose an instance with the right amount of compute, memory and storage for your use case, and then use Elastic Graphics to add graphics acceleration required for your application for a fraction of the cost that you might end up paying on GPU instances.
2. Fine-tune using Elastic Inferences:
AWS provides GPU-powered P instances for machine learning training modeling and for running inferences. Apart from this, AWS provides Elastic Inferences to add graphics acceleration to EC2 instances. Evaluate the EC2 instances with Elastic Inferences for your use case. You can simply choose an instance with the right amount of compute, memory and storage for your use case, and then use Elastic Inferences to add inference acceleration required for your application for a fraction of the cost that you might end up paying on GPU instances.
3. Fine-tune across instance processors (across instance type):
A1 instances are powered with AWS-designed Graviton processors. First identify which processor, Intel, AMD or ARM, best meets your needs and then compare the prices for the same before provisioning processors across different instance types.
If you are running Linux-based workloads for Dev and staging environments, then you can opt for A1 instances to reduce the overall EC2 compute cost in your cloud infrastructure.
There could be many reasons for your ever-increasing cloud bill and unnecessary spending on the EC2 instances is generally one of them. This happens due to unused or underused compute resources. You are not alone in this; users have made this a general practice to provision more than they need to handle the expected traffic and also the unexpected traffic.
Now, the Auto-scaling group comes in the picture. You can leverage auto-scaling groups to handle the scalability requirements for your expected or unexpected traffic.
Let’s take a scenario if your compute requirement can be met by one m4.4xlarge instance. But when you look at the actual CPU usage, it turns out that it varies between 20-70% during the day. Not efficient, isn’t it?
So, you can put the Scaling Policy in use. It allows for scaling resources based on demand. You can simply create an Auto Scaling Group with four m4.xlarge instances, instead of using one m4.4xlarge instance, and create a policy that will reduce the number of running instances by two when CPU usage is below 50% (varies as per your use case). Further, you can set the policy to reduce the instance to only one if the usage drops below 25%.
Moreover, these scaling policies can be fine-tuned to bring down the cost by provisioning fewer resources so that resources are not left unused or underused.
5. Spot Instances:
AWS defines Spot Instances as “Unused EC2 instances that are available for less than the on-demand price.”
As the definition suggests, spot instances enable you to request the unused EC2 instances at the steep discounts leading to lower EC2 costs. The catch here to use spot instances in your favor is that if your applications can run with flexibility and if your applications can be interrupted.
6. EC2 Fleet:
You must have been familiar with the concept of the EC2 fleet. If not, let us put it into simple words.
Fleet is the feature offered by AWS to enable the user to provision capacity across EC2 instance types and across different purchase models (such as On-Demand, RI, Spot Instances).
With a fleet, you can specify target capacity, EC2 instance type as per your use case, and what should be the mix of your fleet should consider of On-Demand, RI and Spot purchase models. Not only this, but you can also specify the priority of which instance type to spin first, the region and the Availability Zone in which fleet should be spun.
The EC2 Fleet request allows you to define the allocation strategy, i.e., which instance to spin first, On-Demand or Spot, to meet the capacity requirements.
By default, the EC2 fleet will spin the lowest cost On-Demand instances or the Spot Instances depending on your prioritization which to spin first.
7. Scheduled RIs:
You might be running workloads in your cloud infrastructure on periodic (daily, weekly, monthly) or part-time basis. If your use case perfectly defines a timeline to run an application, then you can leverage Scheduled RIs (Reserved Instances) to reduce the overall EC2 bill.
With Scheduled RIs, you can reserve capacity on a recurring basis on a daily, weekly or monthly schedule. After the purchase, the RIs are ready to launch during the time specified by you.
So, basically, it is upon the user to reserve instance type and choose the time at which the RI coupons are implemented.
How Centilytics help you reduce EC2 cost?
Centilytics is an intelligent cloud management platform that offers a single pane of glass to monitor, analyze and optimize your cloud infrastructure. Users can enable platform capabilities to get visibility into the cost incurred by EC2 in the cloud bills.
You can prepare custom dashboards to analyze every aspect of EC2 or just focus on cost under the Cost Monitoring module. You get robust monitoring with visualizations to quickly comprehend how cost fluctuates as per the provisioning of EC2 resources in your infrastructure.
Sign-up for a 3-month free trial to experience the value that Centilytics adds to your cloud management stack!