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Design telemetry before moving the first workload, then use it as part of the migration control system: to judge readiness, detect regressions, decide whether to proceed with a wave or cutover, and confirm when the target environment is stable. Start with an agreed baseline and workload-specific acceptance criteria; there is no universal threshold that makes every migration safe.
What telemetry must tell you during a migration
Migration monitoring has two jobs: show whether the production workload is healthy, and show whether the migration itself is progressing as planned. Those views overlap, but they are not interchangeable. Migration-service activity and migration-related network load can affect a workload without representing its ordinary production traffic.
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For each workload, agree what success means with its owner. Criteria may include functional compatibility, acceptable downtime, performance, and business-specific requirements. Google Cloud’s migration validation guidance gives compatibility, minimal downtime, and performance as examples. AWS recommends comparing migrated workloads with identified KPIs or benchmarks in its Migration Lens.
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Choose signals that support a decision, assign an owner to interpret them, and specify what action a concerning result triggers. A dashboard without a decision or response owner is visibility without control.
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Set a baseline before changing the workload
Record representative source-environment behavior before migration. Include application and user experience as well as infrastructure, and capture both normal and peak usage windows where they matter. Preserve the measurement context—such as workload version, time period, and usage pattern—so the target can be compared fairly.
Use the same monitoring approach during migration wherever practical. Add target-specific signals when needed, but retain comparable measurements for the core workload outcomes. After migration, repeat measurements under similar usage conditions and compare them with the baseline and agreed criteria. An AWS Partner Network article on migration measurement recommends reusing monitoring tools for comparability and measuring again after migration; that article dates to 2017, so use the method rather than treating its vendor examples as current recommendations.
Define what each service emits and how signals connect
Create a signal contract for each workload or service: the telemetry it emits, naming and contextual fields, collection locations, access rules, and how its components correlate. The needed mix depends on architecture and diagnostic needs; common categories are metrics, logs, traces, and events.
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- Metrics: quantify behavior over time, such as latency, errors, resource use, or migration progress.
- Logs: record events with useful component and time context. Structured fields make filtering and analysis more reliable.
- Traces: help follow a request through services and dependencies.
- Events: identify relevant changes or operations, including migration actions that help explain a shift in workload behavior.
Use consistent service, component, environment, and time context so responders can connect signals across the source and target. Correlation identifiers can link a request across services without relying on unlimited verbose logging. AWS’s Well-Architected Framework frames the underlying goal as: “How do you design your workload so that you can understand its state?”
Keep end-to-end visibility through the mixed-environment period
During a phased migration, dependencies may cross environment boundaries. Map the source and target components, dependencies, and responsible teams represented on each dashboard. Preserve a way to follow end-to-end behavior even when one request or workflow spans both environments.
Plan when monitoring agents, policies, and alerts move, rather than assuming they will follow workloads automatically. Account for cross-environment network traffic and collector costs. Google Cloud notes that multiple monitoring and alerting tools may be needed when dependencies span environments in its migration validation guidance. AWS also recommends watching additional migration traffic and migration-service metrics and logs in its Migration Lens.
Choose comparison signals that match the workload’s goals
Compare source and target behavior for the same workload and, where possible, similar time and usage conditions. Select dimensions based on the acceptance criteria rather than treating any fixed metric list as mandatory.
- User experience: request latency, response time, or other measures users experience.
- Correctness and service health: error rate, availability, and functional compatibility.
- Capacity and resources: CPU, I/O, queues, or network throughput where relevant to the workload.
- Migration progress and impact: migration downtime and progress, distinguished from ordinary production activity.
- Business outcome: the workload’s SLO or other agreed acceptance criterion.
- Operational readiness: telemetry completeness, alert correctness, and the ability of responders to act.
AWS recommends evaluating migrated workload performance against workload KPIs or benchmarks, while Google Cloud identifies compatibility, downtime, and performance as example criteria. Neither supplies a universal threshold for all workloads.
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Put the signals tied to acceptance criteria where workload owners and responders can use them. Show relevant thresholds, the owner for each response, and enough context to distinguish migration activity from production behavior. Choose real-time or near-real-time monitoring according to the workload’s needs.
Test the collection and response path, not only the dashboard layout. AWS Migration Lens recommends threshold alarms, a dashboard for selected metrics, and automated testing for application metrics. Before cutover, verify that telemetry is arriving, metric checks work, alerts fire as intended, and runbook ownership is clear.
Validate each wave before proceeding to cutover
- Exercise the workload. Before cutover, run stress and user acceptance tests as appropriate to the service and its acceptance criteria. AWS Migration Lens states: “Perform stress and user acceptance tests on migrated workloads before the actual cutover.”
- Check the evidence path. Confirm telemetry completeness, metric checks, alert behavior, and access for the people who will respond.
- Compare the wave with its baseline. Review the selected workload and migration signals under relevant usage conditions. Investigate material deviations before proceeding.
- Make and record the decision. Use the workload owner’s acceptance criteria to proceed, pause, investigate, or roll back. Record accepted deviations, unresolved issues, and any changes to thresholds or dashboards.
- Repeat after migration. Re-measure under comparable usage and reassess after each wave; resource names, scopes, and expected behavior can change as workloads move.
Balance diagnostic detail with cost and access needs
More telemetry detail can make diagnosis easier, but it also increases processing and storage costs. Set collection detail and retention according to business and security needs, and limit access appropriately. Correlation and structured context can help teams answer questions without collecting maximum-volume, highly verbose data by default.
Stabilize operations before retiring source tooling
Once the target operation is stable, update runbooks and support documentation, and verify that telemetry and alerts continue to work. Retire source-only agents or tools only when they are no longer needed. When deciding whether to keep existing tools or adopt managed services, account for licensing and operational overhead; cross-environment coverage may require more than one monitoring or alerting tool during transition.
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