异构云系统中成本和能耗联合优化的调度方法

A Scheduling Method for Joint Optimization of Cost and Energy Consumption in Heterogeneous Cloud System

为了解决异构云系统(HCS)中成本和能耗联合优化问题,将群体智能优化算法应用于任务调度问题,提出了一种基于反向解的白鲨优化算法(RS_WSO)。RS_WSO是一种元启发式算法,包括种群初始化、计算反向解、追踪猎物、寻找猎物阶段。在表观基因组(EP)和高斯消元(GE)两个科学工作流进行实验,结果表明RS_WSO算法相比当前先进的元启发式算法,在节约成本、减少能量消耗方面,具有明显优势。

In view of a joint optimization of cost and energy consumption in heterogeneous cloud systems(HCS),a swarm intelligence optimization algorithm is applied for task scheduling problems,with a reverse solution based white shark optimization algorithm(RS_WSO)to be proposed.As a metaheuristic algorithm,RS_WSO includes population initialization,calculation of reverse solutions,prey tracking and hunting.Experiments are carried out in two scientific workflows,involving epigenome(EP)and Gauss elimination(GE).The results show that RS_WSO algorithm is characterized with clear advantages in terms of cost saving and energy consumption compared with the current advanced meta-heuristic algorithm.