Cloud Computing Task Scheduling Based on Improved Bat Algorithm

QIAO Liang

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Cloud Computing Task Scheduling Based on Improved Bat Algorithm

更新时间：2026-09-04

- Cloud Computing Task Scheduling Based on Improved Bat Algorithm
- Vol. 34, Issue 7, Pages: 27-32(2017)
- 作者机构：
  
  华北科技学院机构计算机学院,河北,三河,065201
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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乔良. 基于改进蝙蝠算法的云计算任务调度研究[J]. 微电子学与计算机, 2017, 34(7): 27-32.

QIAO Liang. Cloud Computing Task Scheduling Based on Improved Bat Algorithm[J]. 2017, 34(7): 27-32.
乔良. 基于改进蝙蝠算法的云计算任务调度研究[J]. 微电子学与计算机, 2017, 34(7): 27-32. DOI：

QIAO Liang. Cloud Computing Task Scheduling Based on Improved Bat Algorithm[J]. 2017, 34(7): 27-32. DOI：

摘要

针对云计算虚拟机调度中存在的资源分配不均衡

蝙蝠算法收敛速度慢、寻优精度不高等缺点

提出了一种基于K-means和蝙蝠算法的云计算虚拟机调度算法.利用K-means聚类对蝙蝠种群数据进行初始化

提高初始样本数据解的质量; 利用Powell局部搜索算法对当前最优解进行局部搜索

提高收敛速度和精度; 利用改进蝙蝠算法对虚拟机进行分配时

充分利用物理机上的资源

达到了最优化目标.仿真实验表明

与其他标准蝙蝠算法和粒子优化算法相比

本文改进的算法有较快的收敛速度和较高的寻优精度; 在虚拟机调度中

本文改进的调度算法与K-means均值调度算法相比

在物量节点的数量降低了12%左右

在系统资源综合利用率上平均提高了11%左右.

Abstract

For cloud computing resource allocation imbalance exists in the virtual machine scheduling

bat algorithm slow convergence speed and optimization accuracy is not high shortcomings

a method is proposed for calculating the virtual machine scheduling algorithm and K-means algorithm based on cloud bat. Algorithm using the K-means clustering initialized to bat population data

improve the quality of the initial solution of the sample data; By Powell local search algorithm for the optimal solution for the current local search and improve the convergence speed and accuracy; when using the improved bat algorithm to allocate the virtual machine

algorithm make full use of the resources on the physical machine to achieve the optimization goal.Simulation results show that the improved algorithm has faster convergence speed and higher searching accuracy compared with other standard bat algorithm and particle swarm optimization algorithm; in the virtual machine scheduling

this paper improves the scheduling algorithm compared with the K-means mean scheduling algorithm

the number of the physical quantity node is reduced by about 12%

and the comprehensive utilization rate of system resources is increased by about 11%.

关键词

Keywords

references

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