Energy-Aware Cloud Workload Placement Using Node Power Profiles
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Solution Overview
Problem
Current workload scheduling algorithms in cloud computing systems do not account for energy efficiency in workload placement, leading to inefficient energy consumption.
Innovation Solution
A method to identify resource utilization levels, calculate idle, activation, and dynamic power levels for compute nodes, and deploy workloads on nodes with the lowest estimated power consumption, considering workload characteristics and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workloads are deployed to evenly distribute resource utilization across compute nodes, then resource utilization efficiency is improved, but energy consumption increases because idle compute nodes are activated
Solution Approach 1:
The patent changes the workload deployment parameter from pure load balancing to energy-aware deployment. It calculates power consumption parameters (idle power, activation power, dynamic power) for each compute node and uses these parameters to determine optimal deployment locations, thereby reducing overall energy consumption while maintaining resource utilization efficiency
Solution Approach 2:
The patent performs preliminary calculation of power consumption parameters for all compute nodes before deploying workloads. By pre-calculating idle power, activation power, and dynamic power for each node, the system can make informed decisions about where to deploy workloads to minimize energy consumption from the outset
2Use of energy by moving object
If workload scheduling algorithms account for energy efficiency in placement, then energy consumption is reduced, but system complexity increases due to additional calculations
Solution Approach 1:
The patent segments the power consumption calculation into distinct components: idle power, activation power, and dynamic power. This segmentation allows the scheduling algorithm to systematically evaluate each component separately and combine them to determine total power consumption, making the complex calculation more manageable and implementable
Solution Approach 2:
The system performs self-measurement of power consumption parameters by monitoring resource utilization levels and calculating power requirements based on actual system state. This self-service approach reduces the need for external measurement devices and simplifies the overall system complexity
Data Source
AI summary
Computer-implemented methods for deploying a workload in a cloud computing system are provided. Aspects include identifying resource utilization levels for processors and memory of each of the plurality of compute nodes, calculating, for each nodes, an idle power level, an activation power level, and a dynamic power level, and identifying characteristics of the workload to be deployed. Aspects also include identifying a plurality of locations that are suitable for deployment of the workload, wherein each of the plurality of locations is one of the plurality of compute nodes, calculating, for each of the plurality of locations based on a simulated deployment of the workload at a corresponding location, an estimated power consumption of the Cloud computing system, and deploying the workload on a first compute node, where the first compute node corresponds to a location associated with a lowest estimated power consumption of the Cloud computing system.