Cluster Power Management via Workload Allocation
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Solution Overview
Problem
Managing power consumption in computing clusters is challenging due to variations in information handling systems and the need for efficient allocation of workloads across multiple systems, particularly in block storage environments where data is distributed across multiple environments.
Innovation Solution
A method and system that calculate power storage consumption for each information handling system by determining I/O power usage, accumulated I/O power consumption across multiple systems, and disk array power consumption, allowing for the allocation of additional computing workloads based on power storage consumption rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If additional computing workloads are allocated to information handling systems in a computing cluster, then productivity and data processing capacity increase, but power consumption increases
Solution Approach 1:
The system dynamically changes workload allocation parameters based on real-time power consumption measurements. By monitoring power usage metrics and adjusting workload distribution accordingly, the system optimizes the balance between productivity and energy consumption, allocating more workloads to systems with lower current power usage.
Solution Approach 2:
The system implements a feedback mechanism where power consumption data from each information handling system is continuously collected and used to adjust future workload allocation decisions. This closed-loop control ensures that productivity goals are met while minimizing overall power consumption by adapting to changing system states.
2Reliability
If workloads are distributed across multiple information handling systems, then reliability and system availability improve, but measurement and monitoring complexity increases
Solution Approach 1:
The system combines power consumption monitoring for multiple information handling systems into a unified management approach. By aggregating power usage data and implementing centralized workload allocation based on combined metrics, the system simplifies monitoring complexity while maintaining distributed reliability across the computing cluster.
Solution Approach 2:
The workload allocation system serves multiple functions simultaneously: it monitors power consumption, measures system availability, distributes workloads optimally, and provides reliability management. This multi-functional approach reduces overall system complexity by consolidating multiple monitoring and control tasks into a single integrated system.
Data Source
AI summary
Managing power consumption for computing cluster, including for each IHS of the computing cluster: executing I/O computing workloads at the IHS associated with movement of block storage data, stored at a disk array in communication with the IHS, between the disk array and the IHS; during execution of the I/O computing workloads, determining an I/O power usage of the IHS; calculating an accumulated I/O power consumption of the plurality of IHS based on a summation of the I/O power usage of each of the IHS; during movement of the block storage data, calculating a power consumption of the disk array; calculating, for each IHS, a power storage consumption of the IHS based on the I/O power usage of the IHS, the accumulated I/O power consumption, and the power consumption of the disk array; allocating additional workloads among the plurality of IHS based on the power storage consumption of each IHS.