Cluster Task Placement Using Energy State Correlation
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Solution Overview
Problem
Existing methods for allocating computer resources in clusters do not effectively consider energy aspects, leading to inefficient task placement and increased energy consumption in data centers.
Innovation Solution
A method that determines task placement by correlating hardware, availability, and energy state characteristics of processing zones, using energy impact considerations to optimize resource allocation and reduce energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional task allocation methods are used that only consider hardware characteristics and availability, then task placement is simple and quick, but energy consumption and heat loss increase due to inefficient resource utilization
Solution Approach 1:
The system performs preliminary analysis of energy state characteristics and heat loss patterns of processing zones before task allocation. By pre-characterizing the energy properties of different processing zones and predicting their thermal behavior, the system prepares optimization data in advance, enabling energy-efficient task placement without adding complex real-time calculations during task submission
Solution Approach 2:
The system implements feedback mechanisms by monitoring the actual energy consumption and thermal state of processing zones after task allocation. This feedback information is used to refine future allocation decisions, creating a closed-loop system that continuously optimizes energy efficiency while adapting to changing system conditions
2Productivity
If tasks are placed without considering energy state characteristics of processing zones, then allocation is faster and simpler, but heat loss and energy inefficiency increase
Solution Approach 1:
The system changes the allocation parameters by incorporating energy state characteristics and heat loss predictions into the task placement criteria. Instead of only considering hardware capabilities and availability, the system now evaluates multiple parameters including energy efficiency metrics, enabling it to select processing zones that minimize heat loss while maintaining allocation efficiency
Solution Approach 2:
The system adds a new dimension to task allocation by considering thermal and energy characteristics as additional evaluation criteria. This multi-dimensional approach extends the traditional allocation framework to include energy efficiency as a separate optimization dimension, allowing simultaneous consideration of performance and energy consumption
3Loss of energy
If energy state characteristics are incorporated into task placement determination, then energy consumption is reduced, but the complexity of the allocation process increases
Solution Approach 1:
The system performs preliminary analysis of energy state characteristics and heat loss patterns of processing zones before task allocation. By pre-characterizing the energy properties of different processing zones and predicting their thermal behavior, the system prepares optimization data in advance, enabling energy-efficient task placement without adding complex real-time calculations during task submission
Solution Approach 2:
The system uses predictive models and simulations to create virtual representations of energy consumption and thermal behavior. By working with these models rather than actual physical measurements during allocation, the system can evaluate energy efficiency without incurring the time cost of real-time monitoring and measurement
Data Source
AI summary
A method and device for allocating computer resources of a cluster for carrying out at least one job controlled by the cluster is disclosed. In one aspect, the method includes determining the placement of the job from physical features of the job and from physical features and availability of the computer resources of at least one processing area of the cluster. The method further includes receiving energy state features of the computer resources of at least the processing area; determining a recommended placement of the at least one job by correlating the physical features of the job, the physical features, availability and energy state of the computer resources on the basis of predetermined rules; and deducing, from the predetermined recommended placement, a recommended allocation list of the computer resources for carrying out the job in the cluster.