AWS Cost Efficiency
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Different ways of enabling memory metrics on AWS EC2 and their respective costs
Memory Metrics on AWS EC2: Methods and Costs Explained
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Memory metrics are crucial for monitoring and optimizing the performance of AWS EC2 instances. Accurate memory metrics facilitate rightsizing by helping to understand memory consumption patterns, identify performance bottlenecks, and optimize instance configurations. These metrics become particularly critical during application launches, significant traffic spikes, or when introducing new features that may impact memory consumption patterns.
By default, AWS does not enable users to measure memory metrics through CloudWatch.
This article will help you understand the ways to measure memory metrics, and the cost implications of measuring memory!
There are three main categories of tools available for capturing and analyzing these memory metrics:
Each of these provide a range of features with varying integration capabilities, cost structures, and implementation complexities, which we'll explore in detail in the following sections.
AWS EC2 instances come with basic CloudWatch metrics such as CPU utilization, disk I/O, and network traffic, but they do not provide memory-related metrics out of the box. To monitor memory, you need to set up the CloudWatch (CW) Agent. It offers two tiers of data collection with different frequencies and granularity. Standard Resolution that typically collects data at intervals of 1 minute and High Resolution at one second (with added cost).
Installing CW agent is straightforward, it comes as a package which can be downloaded and configured using the command line. For the fleet of EC2 instances, use AWS Systems Manager to install CW agent. Once installed, it collects data from the host's memory management subsystem and sends it to CloudWatch, enabling detailed memory metrics monitoring. The agent can be configured to monitor custom memory metrics specific to application needs.
Third-party tools like Datadog, New Relic, Dynatrace, and AppDynamics offer advanced capabilities for monitoring memory metrics on AWS EC2 instances. These solutions typically feature sophisticated analytics and visualization tools, AI-driven anomaly detection, and broad coverage of both infrastructure and application performance along with cross-platform integration capabilities.
Implementing these tools generally involves deploying an agent on each EC2 instance, which collects and sends memory usage data to the monitoring platform. SSM can be used to install agents on EC2, manage them, and automate monitoring. For example datadog agents can be installed and automated using SSM.
Open source monitoring tools like Prometheus, Grafana, and Zabbix offer powerful alternatives for capturing memory metrics on AWS EC2 instances. These tools provide flexibility, customization options, and cost-effectiveness, and they require more setup and maintenance compared to managed/paid solutions. Open source tools usually work using scraping mechanisms with data being collected by periodic HTTP requests.
For example, Prometheus provides following way to collect metrics from EC2 -
Each method offers different levels of customization and integration with existing AWS services, allowing users to choose the most suitable approach for their specific monitoring needs.
These open source tools offer several advantages and considerations:
Scenario: You're running a medium-sized e-commerce website with around 15k transactions daily. You want to track 15 metrics (10 standard + 5 custom). Your infrastructure consists of 10 EC2 instances:
You want to monitor memory metrics for all instances 24/7, with a 14-day retention period for logs and metrics.
Note - Following cost calculation is just for monitoring solutions. It does not consider the underlying EC2 and other services cost for the given application.
Note - Refer appendix for detailed cost calculations
This table provides an outlook of the cost implications of capturing memory metrics for an ecommerce application running on 10 instances with 15 metrics (10 standard + 5 custom).
However, these costs can quickly become prohibitive as scale grows. For a fleet of 1000 instances, cost can reach $50k-$150k. Thus, metrics need to be tracked mindfully.
Following strategies suggest some ways to monitor memory selectively considering the requirements of the application.
As your EC2 fleet grows, implementing efficient memory monitoring becomes crucial for maintaining performance while managing costs. The following strategies offer practical approaches to scale your monitoring efforts effectively.
Stable environments with low variability, such as archived or backup instances, batch processing jobs, development and test environments, static content servers, or low-traffic applications, may not benefit from continuous memory metrics data. Configure monitoring tools to exclude these instances from regular memory metric collection by using tags like memorymetrics:false. This strategy becomes crucial with ‘per instance billing’ solutions as it limits the number of instances to be monitored as opposed to other strategies that optimize on frequency of capturing metrics.
Implement a comprehensive tagging strategy to categorize EC2 instances based on their memory criticality and monitoring needs. This approach can significantly reduce monitoring costs in large-scale deployments by decreasing the frequency of metric collection for less critical instances.
This tiered approach ensures that you allocate more resources to monitoring memory-critical instances while maintaining a basic level of oversight on less critical systems. It allows for a more nuanced and cost-effective memory monitoring strategy across your entire EC2 fleet.
Implement a dynamic memory monitoring system that adjusts based on specific conditions or events. This approach ensures you capture crucial memory data during critical periods without the constant cost of full-time, high-frequency monitoring. Use EventBridge to orchestrate the trigger-based monitoring system.
This trigger-based strategy allows you to maintain a baseline level of memory monitoring and automatically scale up monitoring efforts during critical events. It provides a balance between comprehensive memory insights and cost-efficiency which is particularly beneficial for large-scale deployments with varying workload patterns.
By implementing these strategies, you can create an efficient and economical monitoring system for your EC2 fleet's memory usage, regardless of its scale. Regularly review and adjust your approach as your applications' memory requirements evolve to maintain optimal performance insights while keeping costs under control.
Effective memory monitoring on AWS EC2 is crucial for optimizing application performance. The ideal approach depends on your specific needs and scale. For smaller AWS-focused deployments, the CloudWatch agent offers a cost-effective solution. It integrates well with the AWS ecosystem and can be relatively straightforward to set up on EC2.
Larger-scale multi-platform applications are when specialized observability tools like Datadog or Prometheus are required. They can act as a central point to receive the logs from across the cross-platform application, servers, kubernetes clusters, containerized workloads, etc to build a comprehensive analytics and correlate it with, for example, memory metrics.
Open source alternatives, as the name suggests, require high implementation and maintenance efforts. They can be a great choice for teams with technical bandwidth who want to avoid locking in with commercial vendors. While for teams requiring sophisticated UI, visualization capabilities, AI based anomaly detection etc, paid tools like Datadog can work best.
It is important to keep an eye on costs these tools might incur. They employ varied and often complex pricing models based on factors such as metric count, collection frequency, data volume, and number of instances. As the EC2 fleet grows, implementing advanced strategies for efficiently monitoring the right resources becomes essential. It can simply start with excluding stable and predictable workloads followed by more nuanced strategies like tiered monitoring, and trigger-based monitoring systems etc.
By regularly reassessing strategies and tailoring it to the evolving needs, you can achieve a stable, cost-effective cloud environment that maximizes resource utilization and application performance.
CloudWatch Agent With Standard Resolution: $49.32
Frequency: per 1 minute.
Ingestions per day: 24 hours * 60 (1-minute intervals) = 1,440 ingestions/day/instance
Cost Calculation:
CloudWatch Agent With High Resolution: $304.80
Frequency: per 1 second.
Ingestions per day: 24 hours * 60 minutes * 60 seconds (1-second intervals) = 86,400 ingestions/day/instance
Cost Calculation:
Cost Calculation
Note - Open source monitoring tools have no license fees, but costs are highly variable. Expenses include infrastructure, development, and maintenance, which depend heavily on each team's context and existing resources. The decision to use open source tools should consider factors beyond cost, such as in-house expertise, customization requirements, and long-term scalability. For the given scenario, estimated monthly costs could range from $50 to $150, primarily covering a small EC2 instance for the monitoring stack and associated storage and data transfer costs. However, this estimate excludes the potentially significant time investment for setup and ongoing management.
This provides the understanding of the costs for storage monitoring for each of the given options.
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