Predictive Power Management for Computing Center Processor Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Computing centers face inefficiencies in power management, as idle physical machines consume energy and activating additional machines can cause delays in responding to requests, leading to wasteful energy expenditure and performance lag.
Innovation Solution
A predictive power management system that monitors processor usage in multi-processor environments, maintains a sorted list of usage changes, and predicts the number of required processors by calculating delta values, allowing for proactive transitions between active and idle states based on configurable parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If idle physical machines are maintained to avoid activation delays, then response time is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary actions by proactively activating physical machines based on predicted future workload demands before actual requests arrive. The workload prediction module forecasts upcoming computational needs, and the machine activation module pre-activates required machines, eliminating activation delays while avoiding continuous maintenance of all machines in idle state, thus reducing energy consumption.
2Loss of energy
If idle physical machines are powered down to conserve energy, then energy waste is reduced, but request handling speed decreases
Solution Approach 1:
The system activates machines in advance based on predicted workload before actual requests arrive. The workload prediction module forecasts future computational demands, and the machine activation module pre-activates the required number of physical machines, ensuring they are ready to handle requests immediately when they arrive, thus maintaining high request handling speed while avoiding continuous operation of all machines.
3Loss of energy
If continuous monitoring and dynamic activation/deactivation is implemented, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements self-service through automated workload prediction and machine activation/deactivation. The workload prediction module autonomously forecasts future computational demands using historical data and patterns, and the machine activation module automatically adjusts the number of active physical machines based on predictions, eliminating the need for manual intervention and reducing operational complexity despite the dynamic nature of the system.
Data Source
AI summary
Aspects and implementations of the present disclosure are directed to systems and methods for predictive power management in a computing center. In general, in some implementations, a system for conserving resources in a multi-processor computing environment monitors usage of the processors in the environment and maintains a sorted list of usage changes that occur in each of a plurality of periodic intervals. The system uses the sorted list to predict, according to configurable parameters, how many processors will need to be available during a subsequent interval. In some implementations, the monitored intervals are consecutive and immediately prior to the subsequent interval. In some implementations, the usage changes during a periodic interval are determined as the difference between a maximum number of active-busy processors during the periodic interval and an initial number of active-busy processors for the periodic interval.