Power Budget Allocation in Server Clusters
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Solution Overview
Problem
Server clusters consume significant power, leading to high operating costs and environmental impact, with existing power management technologies failing to efficiently allocate and conserve energy across host computers.
Innovation Solution
A system and method that allocate power resources among host computers in a cluster using lower and upper power bounds, calculating spare capacity and redistributing it based on current usage to optimize power efficiency and reduce energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If power is allocated uniformly to all host computers in a cluster, then each host receives equal power resources, but total power consumption increases and power efficiency decreases
Solution Approach 1:
The patent applies local quality by allocating power resources differently to different hosts based on their specific needs and usage patterns. Each host receives power allocation tailored to its role, workload, and efficiency metrics rather than uniform distribution, thereby improving overall power efficiency while reducing total consumption.
Solution Approach 2:
The system dynamically adjusts power allocation to hosts based on real-time metrics including power efficiency, usage patterns, and workload demands. This dynamic adjustment allows the cluster to optimize power distribution continuously, improving efficiency while minimizing total power consumption through adaptive resource management.
2Loss of energy
If power allocation is optimized to reduce energy consumption, then power efficiency improves and costs decrease, but power availability to individual hosts may be constrained
Solution Approach 1:
The system performs preliminary actions by pre-calculating power allocations based on historical data, workload patterns, and efficiency metrics. This allows the system to proactively optimize power distribution before actual consumption occurs, reducing energy waste while ensuring adequate power availability to hosts based on their anticipated needs.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor power consumption, efficiency metrics, and host performance. This feedback loop allows the system to adjust power allocation in real-time, optimizing energy consumption while maintaining adequate power availability to hosts based on actual usage and performance requirements.
3Productivity
If existing power management technologies are used, then implementation is straightforward, but power allocation efficiency remains suboptimal and energy costs are high
Solution Approach 1:
The system enables self-service by allowing hosts to report their own power consumption, workload status, and efficiency metrics. This self-reported data is used by the allocation algorithm to optimize power distribution, improving allocation efficiency while reducing energy costs through decentralized information gathering and centralized optimization.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting power allocation based on multiple variables including efficiency metrics, workload demands, host priorities, and consumption patterns. This multi-parameter optimization approach significantly improves power allocation efficiency and reduces energy costs compared to static or simple threshold-based management systems.
Data Source
AI summary
A system and method for allocating power resources among host computers in a cluster uses lower and upper bounds with respect to a power budget to be distributed to each of the hosts. Each host is allocated a portion of the cluster power capacity. Any excess amount of the capacity is then allocated to the hosts based at least partly on the lower bound (reserve capacity) and the upper bound (host power limit) of each of the clients.