An Energy-Consumption-Oriented Cloud Workflow Scheduling Optimization Method

Abstract

The invention discloses an energy consumption-oriented cloud workflow scheduling optimization method. The method comprises the following steps: (1) establishing energy consumption-oriented cloud workflow process model and resource model; (2) calculating a task priority; (3) taking out a task t with highest priority from a task set T, finding out a virtual machine set VMt capable of executing the task t, and calculating energy consumption for distributing the task t to each virtual machine in the VMt and completing all distributed tasks; (4) finding out a vm with minimal energy consumption, if only one vm has the minimal energy consumption, distributing the task t to the vm, and if a plurality of vms have the minimal energy consumption, distributing the task t to the vm with characteristics that the vm has the minimal energy consumption and a host in which the vm is located has highest performance per watt; deleting the task t from the task set T, and if the task set T is not null, going to the step (3), or otherwise, going to the step (5); and (5) outputting a workflow scheduling scheme. According to the scheduling optimization method provided by the invention, an energy consumption factor is considered, so that the energy consumption for task processing by the host is effectively reduced while workflow execution time efficiency is kept.

Description

technical field [0001] The present invention relates to the fields of computer technology, information technology and system engineering, and more specifically, relates to an energy-consumption-oriented cloud workflow scheduling optimization method. Background technique [0002] Workflow under the cloud computing environment, referred to as "cloud workflow", is the integration of cloud computing and workflow-related technologies, which can effectively improve the service quality of cloud computing, and in cross-organizational business collaboration that requires efficient computing performance and large-scale storage support , scientific computing and other fields have broad application prospects. In a cloud computing environment, there are usually many hosts that are not necessarily of the same model; when the workflow is executed, the virtual machine is usually used as the smallest allocation unit of computing resources to receive and process workflow tasks, and the virtua...

AI Technical Summary

Problems solved by technology

Most of the current cloud workflow scheduling / execution optimization methods usually only focus on the optimization of execution time or cost, and seldom consider energy consumption factors. A few cloud workflow execution / scheduling optimization methods based on DVFS technology consider energy consumption factors, but DVFS The technology needs to adjust the operating voltage / frequency of the server CPU, shut down / restart the server repeatedly, which will affect the performance of the server and increase the wear rate of the server components, which will lead to an increase in the cost of server procurement and replacement. In actual use, there is a certain limitation

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