Power-Aware Workload Allocation in Heterogeneous Data Centers
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Solution Overview
Problem
Data centers face inefficiencies in power management due to platform heterogeneity and varying power/performance tradeoffs, leading to increased energy consumption and ownership costs, as existing workload allocation methods primarily focus on resource utilization rather than energy efficiency.
Innovation Solution
Implementing a power-aware allocation policy that uses performance estimation and component-based attribute prediction to match workloads with the most energy-efficient platforms based on their power consumption characteristics and management capabilities, employing analytical and statistical models to optimize workload allocation across diverse resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workload allocation focuses on resource utilization, then resource usage is optimized, but energy efficiency deteriorates
Solution Approach 1:
The patent changes the allocation parameters from simple utilization metrics to power-aware metrics. The allocation policy considers power consumption characteristics alongside performance requirements, transforming the decision-making parameters to include energy efficiency factors. This allows the system to select platforms that minimize power consumption while meeting workload demands.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring power consumption characteristics and performance metrics. This feedback loop enables the allocation policy to adaptively adjust workload placement decisions based on observed power efficiency outcomes, learning from past allocations to improve future energy efficiency.
2Use of energy by moving object
If platform heterogeneity is increased to provide power management capabilities, then power efficiency improves, but system complexity increases
Solution Approach 1:
The patent creates a universal allocation policy framework that can handle diverse platform types with varying power management capabilities. The system designs the allocation mechanism to work across heterogeneous platforms uniformly, using a common decision-making approach that adapts to different platform characteristics without requiring platform-specific complex management logic.
3Measurement precision
If analytical prediction layers are added to predict workload power/performance, then allocation accuracy improves, but computational overhead increases
Solution Approach 1:
The system performs preliminary characterization of workload power and performance attributes before actual allocation decisions. By pre-analyzing and storing power consumption patterns and performance metrics for different workload-platform combinations, the system avoids performing complex predictions in real-time, reducing computational overhead during runtime allocation while maintaining high accuracy.
Data Source
AI summary
A data center may be operated to achieve reduced power consumption by matching workloads to specific platforms. Attributes of the platforms may be compiled and those attributes may be used to allocate workloads to specific platforms. The attributes may include performance attributes, as well as power consumption attributes.