Job Scheduling System for Data Center Energy and Performance Optimization
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Solution Overview
Problem
Current job scheduling in data centers fails to balance computational efficiency and power consumption, leading to increasing electricity costs and environmental impact.
Innovation Solution
Implementing an energy and performance optimizing job scheduling system that characterizes jobs as 'hot' or 'cold', iteratively determines schedules based on user-provided parameters and energy-heuristics, and adjusts until estimated performance and power characteristics meet predefined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If jobs are scheduled to increase computational efficiency, then processing performance is improved, but power consumption increases
Solution Approach 1:
The patent segments jobs into different categories (e.g., batch jobs, interactive jobs, real-time jobs) and applies different scheduling strategies to each segment. This allows the system to optimize computational efficiency for certain job types while reducing power consumption for others, resolving the contradiction between productivity and energy usage.
Solution Approach 2:
The scheduling system dynamically adjusts job priorities and execution timing based on real-time power availability and system state. When power is abundant, the system can schedule computationally intensive jobs; when power is constrained, it prioritizes low-power tasks, thereby balancing computational efficiency with power consumption across varying operational conditions.
2Use of energy by moving object
If jobs are scheduled to decrease power consumption, then energy efficiency is improved, but computational efficiency deteriorates
Solution Approach 1:
The system performs preliminary characterization of jobs to determine their power and performance profiles before scheduling. By analyzing job attributes in advance and pre-grouping them into appropriate schedules, the system can ensure that low-power jobs are executed during energy-constrained periods while maintaining computational efficiency for critical tasks, thus resolving the contradiction between energy efficiency and productivity.
3Use of energy by moving object
If iterative scheduling optimization is implemented, then energy and performance characteristics are improved, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the scheduling system continuously monitors actual job execution outcomes and uses this information to refine future scheduling decisions. By learning from past performance data and adjusting schedules accordingly, the system achieves improved energy and performance characteristics without requiring overly complex optimization algorithms, thus resolving the contradiction between optimization effectiveness and system complexity.
Data Source
AI summary
Energy and performance optimizing job scheduling that includes queuing jobs; characterizing jobs as hot or cold, specifying a hot and a cold job sub-queue; iteratively for a number of schedules, until estimated performance and power characteristics of executing jobs in accordance with a schedule meets predefined selection criteria: determining a schedule in dependence upon a user provided parameter, the characterization of each job as hot or cold, and an energy and performance optimizing heuristic; estimating performance and power characteristics of executing the jobs in accordance with the schedule; and determining whether the estimated performance and power characteristics meet the predefined selection criteria. If the estimated performance and power characteristics do not meet the predefined selection criteria, adjusting the user-provided parameter for a next iteration and executing the plurality of jobs in accordance with the determined schedule if the estimated performance and power characteristics meet the predefined selection criteria.