Designing a high-performing architecture with EC2 and Auto Scaling Groups (ASGs)
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When designing a high-performing architecture with EC2 and Auto Scaling Groups (ASGs), thinking beyond traditional EC2-based metrics like CPU utilization or memory is essential. Instead, consider the business-driven data points that indicate when you need more compute power or when it’s safe to scale down. These could include application-specific metrics, service-level indicators, or workflow-related triggers that truly reflect your workload demands.
Here are some examples of non-EC2 metrics that can drive Auto Scaling decisions:
Metric: ApproximateNumberOfMessages (from Amazon SQS)
Scenario: You’re processing messages from an SQS queue. When the queue grows beyond a certain threshold (e.g., 100 messages), it signals the need for more workers to handle the load. Similarly, when the queue is nearly empty, you can safely reduce the number of EC2 instances.
Metric: RequestCountPerTarget (from Application Load Balancer)
Scenario: Scale out your instances when incoming HTTP requests to your backend services exceed, say, 1,000 requests per second. Scale in when traffic drops below 500 requests per second.
Metric: TargetResponseTime (from Application Load Balancer or custom metrics)
Scenario: If your backend application takes too long to respond (e.g., average latency exceeds 300 ms), it might indicate a need for additional capacity to meet demand.
Metric: WriteThroughput or ReadThroughput (from Amazon RDS or DynamoDB)
Scenario: Scale out compute when database write operations spike beyond the normal threshold (e.g., 1,000 writes per second) and scale in when activity normalizes.
Metric: CacheMissCount (from Amazon ElastiCache)
Scenario: If your cache misses increase, it might indicate that the backend application is under pressure, requiring additional compute resources to handle the increased database lookups or recomputation of cached values.
Metric: GetRecords.IteratorAgeMilliseconds (from Amazon Kinesis Data Streams)
Scenario: If the age of records in the Kinesis stream increases, it indicates your processing application is lagging, and you need to add more compute instances.
Metric: OrdersProcessedPerMinute or ActiveSessions
Scenario: You might track the number of orders being processed or active user sessions in real-time. If these numbers exceed your defined thresholds, scale out your compute to ensure a smooth user experience.
Metric: DiskReadOps or DiskWriteOps (from EC2 or EBS)
Scenario: For applications with heavy I/O workloads, high disk read/write operations might require scaling out instances to distribute the workload.
Metric: 4XXErrorCount or 5XXErrorCount (from Application Load Balancer or API Gateway)
Scenario: A sudden spike in error rates could indicate that your application instances are overloaded. Scaling out can help alleviate the pressure and reduce errors.
Metric: Custom event counters (e.g., TasksInProgress or TransactionsPending)
Scenario: Scale-out when the number of pending tasks or unprocessed transactions exceeds a set threshold, ensuring critical workflows remain on schedule.
When thinking about Auto Scaling Groups, the key is to align scaling decisions with the data points that best represent your workload's behavior—not just the underlying infrastructure. By focusing on meaningful metrics like application performance, user activity, or workflow-specific indicators, you ensure that your scaling strategy directly supports your business objectives.
Don’t limit yourself to EC2-centric metrics. Look at your application as a whole and identify metrics that truly reflect the demand on your system. Whether it’s queue depth, database throughput, cache misses, or custom business metrics, these are the indicators that drive intelligent scaling decisions. With this broader perspective, you’ll design an architecture that not only performs well but also adapts dynamically to real-world demands.
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