week 9 Troubleshoot cloud capacity limitations

 

Step 1: Confirm and Diagnose Capacity Overload

Identify affected capacity and error time: Obtain workspace name, capacity name, and exact error occurrence time.

Use monitoring tools: For Microsoft Fabric, open the Microsoft Fabric Capacity Metrics app and navigate to the Compute page to check capacity utilization and system events at the error time.

Look for overload signs: Confirm if the capacity was overloaded by checking status changes to "Overloaded" and observe resource consumption spikes to understand the timing and severity of the issue (e.g., 100% CU utilization).

Step 2: Check for Throttling and Resource Rejections

Inspect throttling metrics (interactive delay/rejection and background rejection) via the Throttling tab in monitoring tools.

Verify if throttling corresponds to the error time, indicating resource exhaustion leading to delayed or rejected operations.

If no throttling is detected, consider that the error might have other causes or another involved capacity.

Step 3: Identify Heavy Resource Consumers

Using Compute or Capacity Metrics apps, review top Capacity Unit (CU) consuming items such as datasets, reports, or pipelines over relevant timeframes.

Drill down by date and hour if needed to pinpoint peak resource consumption moments.

Investigate items with complex calculations, frequent refreshes, or high concurrency that may contribute to overload.

Optimize these items by query tuning, scheduling changes, or workload redistribution.

Step 4: Verify Resource Quotas and Limits

Confirm any capacity quotas or SKU limitations that might impose hard capacity limits, especially in cloud platforms like Microsoft Fabric or Azure.

Check for supply or hardware constraints in cloud datacenters that might cap available capacity (e.g., server shortages, supply-chain delays).

Review concurrency limits, connection caps, or in ode usage (for file-based storage) that might behave as hidden capacity ceilings.

Step 5: Apply Remediation Strategies

Scale up or scale out: Increase SKU capacity, purchase higher-tier SKUs, or add more capacity units where possible.

Optimize workloads: Implement incremental refresh, reduce data volume, rewrite inefficient queries, decrease frequency of data refreshes, and manage concurrency to reduce load.

Schedule heavy workloads: Stagger data refreshes and computationally intensive jobs to avoid peaks.

Enable Auto-Scaling: Where supported, configure auto-scaling to handle demand spikes smoothly.

Reserve capacity: For critical workloads, create capacity reservations to guarantee resource availability.

Restart affected services: Sometimes a simple restart resolves temporary glitches and frees up resources.

Cross-region or cross-availability zone deployment: If capacity is limited locally, deploy workloads across multiple zones or regions.

Step 6: Monitor Continuously and Plan Ahead

Use cloud-native monitoring and alerting tools to track utilization trends and forecast capacity needs proactively.

Engage with cloud provider support and consult platform status pages for known capacity incidents.

Plan capacity usage and cloud spending 2-3 years ahead, considering market supply constraints and infrastructure availability.

Summary

Troubleshooting cloud capacity limitations requires a systematic approach:

Confirm overload and throttling using monitoring tools.

Identify and optimize the heaviest resource consumers.

Understand platform-specific capacity limits and constraints.

Apply scaling, scheduling, and optimization techniques.

Monitor ongoing usage and coordinate with cloud providers.

Following these steps will reduce downtime, improve application performance, and ensure cloud resources efficiently meet demand.

 

 

Reference:

https://learn.microsoft.com/en-us/fabric/enterprise/capacity-planning-troubleshoot-errors

https://cloud.google.com/filestore/docs/capacity-issues

https://www.zdnet.com/article/azures-capacity-limitations-are-continuing-what-can-customers-do/

https://cyfuture.cloud/kb/cloud-computing/what-are-the-best-ways-to-troubleshoot-cloud-provider-specific-issues-in-your-applications

https://ciohub.org/post/2024/01/mastering-cloud-computing-capabilities-troubleshooting-guide/

https://repost.aws/knowledge-center/ec2-insufficient-capacity-errors

https://en.wikiversity.org/wiki/Cloud_Administration/Troubleshooting

https://docs.oracle.com/en-us/iaas/Content/data-flow/using/troubleshooting.htm

https://www.kamatera.com/blog/cloud-scaling/

https://blogs.toystack.ai/limitations-of-cloud-computing/

https://www.mindfulchase.com/explore/troubleshooting-tips/fixing-iam-permission-conflicts,-networking-issues,-and-resource-quota-limitations-in-google-cloud-platform-gcp.html

https://www.youtube.com/watch?v=G3d14rIcbb4

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