Cloud Service Scheduling Algorithm Research and Optimization
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Author(s)
Cui, Hongyan
Liu, Xiaofei
Yu, Tao
Zhang, Honggang
Fang, Yajun
Xia, Zongguo
Date Issued
January 2017
Journal
Security and Communication Networks
Publisher
Hindawi Publishing Corporation
Citation
Cui, Hongyan; Liu, Xiaofei; Yu, Tao; Zhang, Honggang; Fang, Yajun and Xia, Zongguo. "Cloud Service Scheduling Algorithm Research and Optimization." Security and Communication Networks 2017, 2503153 (January 2017): 1-7 © 2017 Hongyan Cui et al
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Final published version
Abstract
We propose a cloud service scheduling model that is referred to as the Task Scheduling System (TSS). In the user module, the process time of each task is in accordance with a general distribution. In the task scheduling module, we take a weighted sum of makespan and flowtime as the objective function and use an Ant Colony Optimization (ACO) and a Genetic Algorithm (GA) to solve the problem of cloud task scheduling. Simulation results show that the convergence speed and output performance of our Genetic Algorithm-Chaos Ant Colony Optimization (GA-CACO) are optimal.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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DOI of Published Version
https://doi.org/10.1155/2017/2503153
