Dynamic Power Allocation for Server Racks
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Solution Overview
Problem
Server racks face performance loss due to underutilization caused by power limits, which are set without considering individual server workloads, leading to inefficient power distribution and infrastructure requirements for cooling and power distribution network redesign.
Innovation Solution
A method and apparatus for dynamically allocating power capping limits to servers based on monitoring actual power consumption and estimating probability distributions of power demand, using a dynamic power allocator to iteratively set new limits that reduce performance loss by optimizing power usage according to workload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If fixed power limits are imposed on servers in a server rack, then the total power consumption is controlled within the power supply envelope, but server performance loss occurs due to underutilization
Solution Approach 1:
The patent implements dynamic power allocation where the power capping limit for each server is adjusted in real-time based on monitored power consumption patterns and workload conditions. Instead of static power limits, the system continuously adapts individual server power caps to maximize utilization while maintaining total rack power within the supply envelope, thereby resolving the contradiction between energy control and performance.
2Reliability
If conservative power limits are set by power controller units based on processor reliability benchmarks, then processor longevity is protected, but performance loss occurs due to processor throttling
Solution Approach 1:
The system employs feedback mechanisms where power controller units continuously monitor actual power consumption, thermal conditions, and workload demands. Based on this feedback, the power capping limits are dynamically adjusted rather than maintaining fixed conservative limits. This allows processors to operate at higher utilization levels when conditions permit, while still protecting longevity through real-time monitoring and adjustment, thus resolving the contradiction between reliability and productivity.
3Productivity
If better temperature cooling facilities and raised power limits are provided, then performance loss is reduced, but physical infrastructure changes and increased power consumption are required
Solution Approach 1:
The patent resolves this contradiction by changing the parameter of power capping limits through software-based dynamic adjustment rather than physical infrastructure changes. The system modifies power allocation parameters iteratively based on monitored conditions, allowing existing cooling infrastructure to operate within its current capacity while achieving improved computational capability through optimized power distribution. This avoids the need for additional cooling facilities or physical modifications to the data center infrastructure.
4Ease of operation
If heuristic approaches are used to determine power budgets for individual servers, then power allocation is simplified, but performance loss occurs due to ad-hoc power assignment not accounting for foreseeable power demand
Solution Approach 1:
The system applies preliminary action by proactively adjusting power capping limits based on forecasted power demand derived from monitored patterns and workload characteristics. Rather than reacting to power consumption after it occurs, the system anticipates future power needs and adjusts limits in advance, allowing servers to maintain higher utilization without exceeding power constraints. This resolves the contradiction by maintaining simple automated operation while eliminating performance loss through predictive power allocation.
Data Source
AI summary
Embodiments of the invention relate generally to the field of power management of computer systems, and more particularly to a method and apparatus for dynamically allocating power to servers in a server rack. The method comprises: measuring power consumption of a computer system having one or more servers; estimating probability distribution of power demand for each of the one or more servers, the estimation based on the measured power consumption; estimating performance loss via the estimated probability distribution; computing power capping limits for each of the one or more servers, the computation based on the estimated probability distribution and the performance loss; and dynamically allocating the power capping limits to each of the one or more servers by modifying previous power capping limits of each of the one or more servers.