Performance Index for Cloud Workload Stability Tracking
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Solution Overview
Problem
Conventional systems fail to accurately track and analyze cloud data platform performance over time due to reliance on synthetic benchmarks that are not representative of real-world production workloads, leading to unstable and unreliable performance metrics that cannot distinguish between workload changes and platform improvements.
Innovation Solution
A performance index system that leverages real production workloads to generate stable and reliable performance metrics, identifying stable workloads and queries to compare across different settings and time periods, enabling near real-time tracking and analysis of performance changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If synthetic benchmarks are used to measure performance, then performance metrics can be obtained, but the metrics become unstable and unreliable because they do not represent real-world production workloads
Solution Approach 1:
The patent copies real production workload characteristics (query patterns, data access patterns, workload mix) to create benchmark workloads that accurately represent actual system usage. This involves capturing production workload metadata and using it to generate synthetic workloads that mirror real-world conditions, thereby achieving both measurement precision and reliability
Solution Approach 2:
The patent changes the parameters of benchmark workloads from generic synthetic patterns to production-specific patterns by adjusting query types, data volumes, access frequencies, and workload mixes based on actual production metrics. This allows the benchmarks to maintain stability while accurately reflecting real-world performance
2Measurement precision
If real production workloads are used for benchmarking, then representative performance metrics are obtained, but it becomes difficult to distinguish between workload changes and platform improvements
Solution Approach 1:
The patent segments performance changes by isolating workload characteristics from platform performance. It separates the workload component (which changes over time) from the platform component (which should remain stable), allowing independent analysis of each. This is achieved through controlled experimentation where workload parameters are held constant while platform changes are measured
Solution Approach 2:
The patent introduces workload metadata and performance indexing mechanisms as intermediaries between raw production workloads and performance measurements. These intermediaries capture and control workload variables, enabling the system to filter out workload-related performance variations and isolate true platform performance changes
3Productivity
If conventional benchmarking methods are used, then performance data can be collected, but the data cannot accurately track performance over time due to workload variability
Solution Approach 1:
The patent implements continuous performance monitoring using production workloads as the benchmark input. Instead of periodic synthetic benchmarks, it continuously feeds actual production workload patterns through the system, maintaining an ongoing measurement stream that accurately reflects performance trends over time while filtering out noise from workload variability
Solution Approach 2:
The patent employs feedback mechanisms that compare performance measurements against controlled workload expectations. By continuously monitoring performance metrics and comparing them against known workload characteristics, the system can identify true performance changes versus expected variations due to normal workload fluctuations, thereby improving trend accuracy
Data Source
AI summary
Methods, systems, and computer programs are described for tracking evaluation of workload stability through performance indexing. A plurality of metric source data is received by at least one hardware processor. Based on this data, a workload is identified as a stable workload candidate. A performance index is then generated, reflecting the characteristics of the identified stable workload candidate. The performance index is continuously tracked over a period of time, enabling the detection and analysis of any modifications to the workload and the subsequent impact on system performance.