Monitoring, Logging, Analysis, Remediation, and Performance Optimization
Compute, Storage, and Database Performance Optimization
CoreUse measured constraints to tune EC2, EBS, S3 transfers and lifecycle, shared file storage, and RDS without trading away reliability or cost requirements.
Aligned to AWS Certified CloudOps Engineer - Associate (SOA-C03) Version 1.1, verified August 24, 2026.
Why this matters
Performance symptoms cross resource boundaries. Storage latency can be limited by a volume, an instance, or the workload; database latency can be query, wait, connection, compute, or storage pressure.
Must Know
- Validate Compute Optimizer recommendations with CloudWatch workload evidence, tags, maintenance constraints, and post-change service-level signals before rightsizing.
- For EBS, correlate IOPS, throughput, latency, queue depth, workload I/O size, and EC2 instance EBS bandwidth. gp3 permits independent performance provisioning within its supported limits.
- Multipart upload improves large-object transfer through parallel parts and independent retries; Transfer Acceleration changes the internet path; DataSync manages repeated transfers; S3 Lifecycle changes storage retention and class.
- Choose EFS, an appropriate FSx family, or Amazon S3 Files from required protocol, client integration, concurrency, semantics, performance, and lifecycle behavior rather than the word shared.
- Use RDS metrics and Database Insights/Performance Insights evidence to find dominant load and waits. RDS Proxy addresses connection churn and pooling, not every slow query.
- Placement groups affect supported EC2 placement and network or hardware-failure tradeoffs; verify compatible instance types and the availability boundary.
Compare and Distinguish
- Compute Optimizer recommends from observed utilization; CloudWatch supplies workload evidence; Trusted Advisor checks broader account opportunities and risks.
- gp3 tuning changes provisioned performance; changing volume family changes the storage performance model.
- Multipart upload, Transfer Acceleration, DataSync, and Lifecycle solve upload mechanics, internet path, managed movement, and stored-object cost respectively.
- EFS is managed NFS file storage; FSx provides specific managed file-system families; S3 Files exposes a managed file interface for supported object-backed use cases.
Scenario examples
- Scenario: A gp3 volume has adequate capacity but reaches its provisioned throughput. Increase the measured performance dimension within supported bounds and verify instance bandwidth is not the next limit.
- Scenario: Remote users upload very large S3 objects over unreliable links. Use multipart upload; add Transfer Acceleration only when the network path and cost justify it.
- Scenario: Bursty clients exhaust RDS connections while database compute and query waits remain healthy. Put supported clients through RDS Proxy and monitor connection reuse.
Exam traps
- Do not resize from CPU alone when memory, network, or storage is decisive.
- Do not assume every EBS latency problem requires a different volume family.
- Do not use Lifecycle as a transfer-performance feature.
- Do not select RDS Proxy as a general query optimizer.
Key takeaways
- Correlate evidence across the resource and host limits.
- Change the constrained dimension, not a neighboring one.
- Separate transfer, access protocol, retention, and connection management.
- Measure after every optimization.
How it works
- Record a performance baseline across the workload and each dependent resource before changing capacity.
- Locate the first measured ceiling and select the configuration that changes that dimension without weakening reliability.
- Repeat the same measurements after the change and roll back if the service-level result regresses.
When to use it
- Use AWS Compute Optimizer for utilization-based recommendations, validate them against CloudWatch workload evidence, and consult AWS Trusted Advisor for broader account-level opportunities and risks.
- Adjust gp3 provisioned performance when measurements show an IOPS or throughput limit; change the volume family only when the workload needs a different storage performance model.
- Choose multipart upload for large-object upload mechanics, S3 Transfer Acceleration for the internet path, AWS DataSync for managed repeated movement, and S3 Lifecycle for stored-object cost and retention.
- Select Amazon EFS for managed NFS storage, an Amazon FSx family for its specific file-system semantics, or Amazon S3 Files when the supported object-backed managed file interface fits the workload.
Security and governance implications
- Grant optimization tools and operators access to only the metrics, resources, and configuration actions required for the change.
- Preserve encryption, backup, and data-handling requirements while moving data or changing storage and database configuration.
Failure signals and diagnosis
- For EBS latency, compare queue, IOPS, throughput, latency, instance bandwidth, and workload I/O.
- For S3 transfer problems, identify client path, object size, recurrence, and storage-class requirements.
- For RDS latency, inspect waits, queries, CPU, storage, memory, and connections before scaling.
More detail
- Validate Compute Optimizer recommendations against workload history, maintenance constraints, and post-change service indicators.
- For EBS, evaluate the volume settings, EC2 bandwidth, queue behavior, and workload I/O together.
- Match multipart upload, Transfer Acceleration, DataSync, Lifecycle, shared file storage, and RDS Proxy to the exact bottleneck they address.
Ready for the quiz?
- An EBS-backed workload is slow after a rightsizing change. Which metrics distinguish a volume limit from an EC2 instance bandwidth or compute constraint?
- Which transfer requirement distinguishes multipart upload, Transfer Acceleration, DataSync, and S3 Lifecycle?
- Which RDS signals would distinguish connection churn that favors RDS Proxy from query, compute, or storage pressure?
Related objectives
- D1.3 — Task 1.3: Implement performance optimization strategies for compute, storage, and database resources.
- 1.3.1 — Optimize compute resources and remediate performance problems by using performance metrics, resource tags, and AWS tools.
- 1.3.2 — Analyze Amazon EBS performance metrics, troubleshoot issues, and optimize volume types to improve performance and reduce cost.
- 1.3.3 — Implement and optimize S3 performance strategies (for example, AWS DataSync, S3 Transfer Acceleration, multipart uploads, S3 Lifecycle policies) to enhance data transfer, storage efficiency, and access patterns.
- 1.3.4 — Evaluate and select shared storage solutions (for example, Amazon EFS, Amazon FSx, Amazon S3 Files), and optimize the solutions (for example, EFS lifecycle policies) for specific use cases and requirements.
- 1.3.5 — Monitor Amazon RDS metrics (for example, Amazon RDS Performance Insights, CloudWatch alarms) and modify configurations to increase performance efficiency (for example, Performance Insights proactive recommendations, RDS Proxy).
- 1.3.6 — Implement, monitor, and optimize EC2 instances and their associated storage and networking capabilities (for example, EC2 placement groups).