Synthetic Workload Trace Generation for Capacity Planning
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Solution Overview
Problem
Current capacity planning methods for computing environments are inadequate in predicting and managing future resource demands, leading to inefficiencies and increased costs due to server sprawl and complex resource management challenges, particularly in virtualized data centers where workloads frequently change.
Innovation Solution
The system generates synthetic workload traces that accurately represent future resource demands by analyzing historical patterns and trends, allowing for the creation of multiple scenarios to assess demand variations and optimize resource allocation, thereby enabling just-in-time capacity planning and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional capacity planning methods are used, then computing resources can be provisioned, but prediction accuracy of future resource demands deteriorates leading to over or under provisioning
Solution Approach 1:
The system performs preliminary analysis of historical workload patterns and resource demand trends before actual capacity planning decisions are made. By pre-processing historical data to extract patterns and generate synthetic workload traces, the system prepares accurate predictions in advance, enabling better capacity planning decisions without compromising service delivery quality.
Solution Approach 2:
The system creates synthetic copies of historical workload traces that preserve the statistical properties and patterns of actual workloads. These synthetic traces serve as realistic simulations for capacity planning analysis, allowing accurate prediction of future resource demands without requiring actual future workload data, thus improving prediction accuracy while maintaining service quality.
2Reliability
If more computing resources are provisioned to ensure adequate service delivery, then service quality is maintained, but costs increase due to server sprawl and resource inefficiency
Solution Approach 1:
The system continuously monitors actual resource utilization and compares it against the synthetic workload traces and capacity plans. This feedback mechanism allows the system to identify discrepancies between predicted and actual demands, enabling continuous optimization of resource allocation. By adjusting capacity plans based on actual performance data, the system maintains service quality while reducing resource waste and associated costs.
3Measurement precision
If historical workload data is analyzed in detail to improve prediction accuracy, then prediction quality improves, but time and computational effort for analysis increase
Solution Approach 1:
The system extracts only the essential statistical properties and patterns from historical workload data that are most relevant for capacity planning predictions. By identifying and extracting key features such as workload patterns, resource demand trends, and seasonal variations, the system achieves high prediction accuracy without processing every detail of the historical data, thus reducing analysis time while maintaining prediction quality.
Data Source
AI summary
A method comprises receiving a pattern of resource demands in a workload trace. The method further comprises identifying a plurality of occurrences of the determined pattern in the workload trace, and analyzing the occurrences to determine a trend of the workload trace. The method further comprises generating at least one synthetic workload trace representative of expected resource demands of the received workload trace accounting for the determined trend.