What is the Simplest Checklist for Screening Shared CPU Candidates?
When optimizing cloud infrastructure costs, identifying which workloads can migrate onto shared CPU instances is one of the most rewarding yet nuanced tactics. However, the devil is in the details: blindly shifting workloads to shared CPU types without proper screening commonly backfires. Overloaded processors, unpredictable latency spikes, and user complaints ensue, often negating the cost savings.
You ever wonder why this post distills a straightforward, actionable checklist for screening candidates suitable for shared cpu instances, drawing on practical experience across aws, azure, and google cloud. We’ll leverage tools like AWS Compute Optimizer and Azure Advisor and emphasize critical metrics beyond average CPU usage. Our goal is to help you confidently identify “low sustained CPU, manageable bursts, and latency-tolerant” workloads so you can slice cloud waste without breaking functionality.
Why Shared CPU Screening MattersShared CPU instances—often grouped under “burstable” or “spot” categories—enable significant cost savings by leveraging oversubscription. Instead of provisioning a dedicated vCPU per workload, cloud providers allocate CPUs dynamically among multiple tenants during idle periods. Exactly.. Economically, this is brilliant; operationally, it demands judicious candidate selection because:
Shared CPU definitions vary by provider: AWS T3/T4g, Azure B-series, and GCP E2 instances all approach CPU bursting differently. Average CPU metrics mask burstiness: A workload showing a 10% average CPU might experience dangerous 95th or 99th percentile spikes. Latency-sensitive workloads can’t tolerate CPU starvation: Even short CPU contention causes latency or failure spikes. Always-on small services hide cloud waste: These low-intensity but continuously-running workloads are prime cost optimization targets if screened well.Without a rigorous screening approach, you’re just guessing—and guesswork leads to costly rollbacks.
Checklist: Core Screening Criteria for Shared CPU CandidatesBelow is a practical, step-by-step checklist to evaluate if a workload qualifies for a shared CPU instance migration. We’ll clarify why each step matters, how to measure it with cloud tooling, and what typical thresholds to consider.
Understand Your Provider’s Shared CPU DefinitionBefore screening, review how your cloud provider defines and implements shared CPU capability. For example:
AWS: T-series instances provide CPU credit accrual and burst capacity, measured in CPU credits that accumulate during low usage. Azure: B-series virtual machines credit CPU cycles when idle and allow bursts within these credits. GCP: E2 instances do not guarantee dedicated CPU, but actual contention depends on host load and instance mix.Reference documentation:
AWS Burstable Performance Instances Azure B-series VMs GCP E2 Machines Measure CPU Usage Using Percentiles, Not AveragesAverage CPU utilization is a poor predictor of burst tolerance. Instead, inspect the P95 and P99 CPU usage percentiles. These metrics show the workloads' CPU demand during frequent and rare spikes respectively.
Use cloud-native monitoring tools and insights:
AWS Compute Optimizer: Analyzes instance-level CPU and memory metrics, recommending right-sized instances with detailed percentile usage. Azure Advisor: Provides performance metrics including CPU percentiles to evaluate VM sizing accuracy. Custom CloudWatch or Azure Monitor: Query P95/P99 from CPU utilization metrics using percentile-based aggregation over sufficiently large time windows.Example: A service with 10% average CPU but spikes to 90-95% during P99 is a risky shared CPU candidate unless latency tolerance is high.
Verify Peak Durations and Frequency within the Observation WindowShort infrequent spikes cause less contention than prolonged bursts. Your monitoring period should span at least 7–14 days to capture daily and weekly patterns.
Look for spike duration: How long does CPU stay above 70% or 80%? Ten-minute spikes are manageable; one-hour sustained spikes indicate overload risk. Look for spike frequency: Rare bursts can be handled by CPU credits; constant spikes rapidly deplete credits risking throttling.This is where your telemetry / APM tools come into play. Time series graphs combined with percentiles allow you to understand workload burst dynamics.
Confirm Low Sustained CPU BaselineSelect workloads with a clearly low baseline CPU, ideally below or near 20%. This ensures credits can accumulate and support bursts without immediate depletion.
Beware of workloads running flat-out at 40-50%. These are generally unsuitable for shared CPU since they can't build credit reserves.
Assess Latency and Availability ToleranceUnderstanding your service's tolerance to CPU contention is crucial. Shared CPU workloads may experience transient CPU throttling causing increased response latencies.
Measure existing latency percentiles and define rollback criteria clearly before migration pilots.
Evaluate Storage and Network Egress CostsCost optimization isn’t just about CPU time. Small always-on workloads might incur significant storage and egress costs in aggregate.
Check if the candidate workloads exchange large data volumes or use high IOPS storage: Hidden costs here diminish shared CPU instance savings. Sample Screening Summary Table Criteria Metric Threshold / Guidance Notes Provider Shared CPU Model N/A Understand instance burst mechanics Review AWS T-series, Azure B-series, etc. CPU Utilization Percentiles P95, P99 P95 < 40%, P99 < 70% Indicative, adjust per latency tolerance Peak CPU Duration Duration above 70% < 15 minutes typical; avoid sustained > 30 min Short bursts manageable; prolonged not Baseline CPU Average CPU < 20% Ensures burst credit build-up Latency Tolerance P95, P99 latency Known tolerance levels Define rollback latency thresholds Storage & Egress Costs Storage IOPS, Network egress volume Evaluate actual vs budgeted cost Non-CPU costs impact total savings Putting It All Together: Workflow to Screen Candidates Run initial sizing and utilization recommendations. For AWS, start with Compute Optimizer. For Azure, consult Azure Advisor. Pull detailed CPU usage percentiles and time windows. Query metrics for P95 and P99 CPU over the last 14 days. Analyze spike duration and frequency. Generate heatmaps or histograms to identify if bursts are short and infrequent. Review latency metrics and SLOs. Identify latency thresholds; check if occasional latency increases are acceptable. Estimate full cost impact including storage and egress. Don’t rely on CPU costs alone. Create a pilot migration plan with clear rollback criteria. Define failure signals such as latency spikes or CPU throttling events. Run a small pilot. Monitor closely using the same metrics and update your checklist. Scale migration for candidates that pass pilot criteria. Common Pitfalls to Avoid Ignoring spike percentiles in favor of averages: Average CPU can disguise resource starvation during spikes. Short observation windows: One or two days of metrics rarely capture realistic workload patterns. Treating vCPU count as a hard performance guarantee: Shared vCPUs are subject to contention, so never assume a fixed execution rate. Underestimating service latency sensitivity: Not all workloads tolerate CPU throttling equally. Neglecting storage and egress costs: These hidden factors reduce net savings. ConclusionSimplifying check here the the process of screening shared CPU candidates boils down to rigorously applying the right metrics—percentiles over averages, thoughtful time windows, and workload-specific latency tolerance considerations. Leveraging cloud-native tools like AWS Compute Optimizer and Azure Advisor significantly lightens the observational burden, but a methodical approach tailored to your application’s behavior is vital.
Always remember the big picture: https://dibz.me/blog/what-should-i-measure-besides-cpu-for-a-shared-cpu-migration-1253 it’s not just “CPU usage” but the interplay of low sustained CPU, manageable bursts, and latency tolerance that defines a successful shared CPU migration. When done right, this approach unlocks significant cost savings while preserving user experience and uptime.
Happy optimizing!