Server Power Consumption Estimation and Migration Recommendation
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Solution Overview
Problem
Information handling systems, such as servers, face challenges in efficiently determining power consumption and performance levels, leading to suboptimal resource allocation and potential energy wastage due to variations in workload and server configurations.
Innovation Solution
A test system comprising a test module, estimation modules, ratio modules, and a recommendation module that measures and calculates performance levels and power consumption of current servers, compares them to target servers, and recommends migration to optimize energy usage by estimating potential energy savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If server performance levels and power consumption are determined without accurate measurement systems, then resource allocation decisions are made quickly, but the accuracy of power consumption assessment deteriorates leading to suboptimal resource allocation and energy wastage
Solution Approach 1:
The system performs preliminary measurements of performance metrics and power consumption during test executions before making resource allocation decisions. This allows accurate baseline data to be established in advance, enabling both precise assessment and efficient decision-making without compromising either measurement accuracy or productivity.
Solution Approach 2:
The system implements feedback loops where measured power consumption and performance data are continuously fed back into the resource allocation algorithms. This feedback mechanism ensures that allocation decisions are based on accurate real-time measurements while maintaining high productivity through automated iterative optimization.
2Productivity
If multiple servers are used to handle workloads, then system capacity and performance are improved, but total power consumption increases
Solution Approach 1:
The system changes operational parameters by identifying servers with similar performance characteristics but different power consumption profiles. By selecting alternative servers with more favorable power-performance ratios, the system maintains required system capacity while reducing total power consumption through parameter optimization rather than brute-force scaling.
Solution Approach 2:
The system creates virtual copies or models of server performance and power consumption data to simulate and evaluate different workload distribution scenarios. This allows optimization of server selection and allocation without actually deploying additional physical servers, thereby maintaining system capacity while avoiding the power consumption increase that would result from adding more physical infrastructure.
3Productivity
If server configurations are varied to optimize performance, then workload handling capability is improved, but complexity of determining optimal configurations increases
Solution Approach 1:
The system segments the complex configuration determination problem into distinct measurement and evaluation components. By separating performance metric collection, power consumption measurement, and optimization algorithm execution into independent modules, the system manages configuration complexity while maintaining improved workload handling capability through systematic analysis of individual configuration parameters.
Data Source
AI summary
A device includes an estimation module, a ratio module, and a recommendation module. The estimation module is adapted to receive a first performance level and a first power consumption level associated with a current server, and adapted to estimate a second performance level and a second power consumption level associated with a first target server based on the first performance level and the first power consumption level. The ratio module is adapted to determine a first performance ratio between the first performance level and the second performance level, and adapted to determine a first power consumption ratio between the first power consumption level and the second power consumption level. The recommendation module is adapted to determine whether to suggest a migration from the current server to the first target server based on the first performance ratio and the first power consumption ratio and adapted to output a first migration determination.