Workload Resource Demand Pattern Evaluation System
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Solution Overview
Problem
Current capacity planning methods struggle to accurately identify and represent resource demand patterns in computing environments, leading to inefficient resource allocation and increased costs due to the complexity of modern IT systems and the variability of workload demands.
Innovation Solution
A system and method for evaluating the representativeness of resource demand patterns by analyzing historical workload data to determine a metric of confidence, using techniques such as pattern analysis, trend evaluation, and synthetic workload trace generation to ensure accurate capacity planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern analysis is performed on historical workload data to identify resource demand patterns, then capacity planning accuracy is improved, but the complexity of analyzing and evaluating patterns increases
Solution Approach 1:
The patent segments the pattern evaluation process into distinct components: identifying pattern occurrences, evaluating representativeness through statistical metrics (such as coefficient of variation and pattern consistency), and filtering anomalies. This segmentation allows complex pattern analysis to be broken down into manageable steps that can be systematically executed.
Solution Approach 2:
The patent implements feedback mechanisms by evaluating the representativeness of identified patterns against historical workload data and using statistical metrics to validate pattern accuracy. The system continuously refines pattern identification based on evaluation results, adjusting the analysis to improve capacity planning accuracy while managing complexity through iterative refinement.
2Reliability
If more historical workload data is analyzed to improve pattern representativeness, then capacity planning reliability is improved, but the time required for analysis increases
Solution Approach 1:
The patent applies partial action by selecting representative samples from historical workload data rather than requiring complete analysis of all historical data. The system identifies pattern occurrences and evaluates their representativeness using statistical metrics, allowing reliable capacity planning conclusions to be drawn from a manageable subset of data that adequately represents the workload characteristics.
Solution Approach 2:
The patent changes parameters such as the time window for historical data analysis, the threshold for pattern occurrence frequency, and the statistical metrics used for representativeness evaluation. By adjusting these parameters, the system can optimize the balance between analysis depth and time required, achieving reliable pattern identification without excessive analysis time.
3Measurement precision
If statistical metrics are used to evaluate pattern occurrences, then measurement precision of pattern representativeness is improved, but computational requirements increase
Solution Approach 1:
The patent extracts key statistical metrics from the comprehensive analysis of historical workload data, focusing on specific measures such as coefficient of variation, pattern consistency ratios, and occurrence frequency thresholds. By extracting and evaluating only these critical metrics rather than performing complete statistical analysis on all data, the system achieves precise pattern representativeness measurement with reduced computational requirements.
Data Source
AI summary
A method comprises receiving, by pattern evaluation logic, a plurality of occurrences of a prospective pattern of resource demands in a representative workload. The method further comprises evaluating, by the pattern evaluation logic, the received occurrences of the prospective pattern of resource demands, and determining, by the pattern evaluation logic, based on the evaluation of the received occurrences of the prospective pattern of resource demands, how representative the prospective pattern is of resource demands of the representative workload.