Dynamic Power Budget Allocation in Multi-Processor Server Farms
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Solution Overview
Problem
Server farms face increasing energy consumption and costs due to growing demand for internet-based services, with existing power allocation methods failing to optimize performance within a fixed power budget, leading to inefficiencies in processing incoming jobs.
Innovation Solution
A method using a queuing theoretic model to determine the desired frequency/power state for each server in a multi-processor system, allowing for dynamic control of operating states to minimize mean response time and optimize power allocation among servers, leveraging dynamic frequency and voltage scaling mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If power budget is increased to handle growing server demand, then processing capacity and performance are improved, but energy consumption and operating costs increase
Solution Approach 1:
The patent applies dynamic power allocation by continuously adjusting the power distribution to individual servers based on real-time workload conditions. The system monitors queue lengths and dynamically modifies power budgets, transitioning from static to dynamic power management to optimize the trade-off between processing capacity and energy consumption.
Solution Approach 2:
The system changes power allocation parameters dynamically based on workload demand. By adjusting power distribution parameters according to queue lengths and server utilization, the system optimizes processing capacity while controlling energy consumption, rather than maintaining fixed power allocations.
2Loss of time
If more servers are activated to process incoming jobs, then mean response time is reduced, but power consumption increases
Solution Approach 1:
The system dynamically adjusts power allocation parameters to individual servers based on real-time queue lengths and workload conditions. When queues are long, more power is allocated to reduce response time; when queues are short, power is reduced to save energy, creating an adaptive response time-power trade-off.
Solution Approach 2:
The patent implements feedback mechanisms that monitor queue lengths and server performance metrics, then use this information to adjust power allocation decisions. This closed-loop control enables the system to respond to changing conditions and optimize the balance between response time and power consumption.
3Ease of operation
If power is allocated uniformly across all servers, then system simplicity is maintained, but performance optimization is lost
Solution Approach 1:
The patent transitions from uniform power allocation to localized power allocation, where each server receives power based on its specific workload conditions and queue length. This allows performance optimization at the individual server level while maintaining a relatively simple centralized control mechanism.
Data Source
AI summary
Systems, apparatuses, methods, and software that implement power budget allocation optimization algorithms in multi-processor systems, such as server farms. The algorithms are derived from a queuing theoretic model that minimizes the mean response time of the system to the jobs in the workload while accounting for a variety of factors. These factors include, but are not necessarily limited to, the type of power (frequency) scaling mechanism(s) available within the processors in the system, the power-to-frequency relationship(s) of the processors for the scaling mechanism(s) available, whether or not the system is an open or closed loop system, the arrival rate of jobs incoming into the system, the number of jobs within the system, and the type of workload being processed.