Cloud Service Scheduling Algorithm Research and Optimization. (11th January 2017)

• Record Type: Journal Article
• Authors: Cui, Hongyan; Liu, Xiaofei; Yu, Tao; Zhang, Honggang; Fang, Yajun; Xia, Zongguo
• Other Names: Lee Jong-Hyouk Academic Editor.
• Is Part Of: Security and communication networks. Volume 2017(2017)
• Publication Date: 2017-01-11
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2017/2503153
• Languages: English
• ISSNs: 1939-0114

Abstract

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.