Cloud service scheduling algorithm research and optimization

Hongyan Cui; Xiaofei Liu; Tao Yu; Honggang Zhang; Yajun Fang; Zongguo Xia

doi:10.1155/2017/2503153

Cloud service scheduling algorithm research and optimization

Hongyan Cui
, Xiaofei Liu
, Tao Yu
, Honggang Zhang
, Yajun Fang
, Zongguo Xia

Engineering

Beijing University of Posts and Telecommunications
Key Lab of Network System Architecture and Convergence
Tsinghua University
Massachusetts Institute of Technology
University of Massachusetts Boston

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	2503153
Journal	Security and Communication Networks
Volume	2017
DOIs	https://doi.org/10.1155/2017/2503153
State	Published - 2017

ASJC Scopus Subject Areas

Information Systems
Computer Networks and Communications

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10.1155/2017/2503153

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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.",

author = "Hongyan Cui and Xiaofei Liu and Tao Yu and Honggang Zhang and Yajun Fang and Zongguo Xia",

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T1 - Cloud service scheduling algorithm research and optimization

AU - Cui, Hongyan

AU - Liu, Xiaofei

AU - Yu, Tao

AU - Zhang, Honggang

AU - Fang, Yajun

AU - Xia, Zongguo

PY - 2017

Y1 - 2017

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AB - 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.

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JO - Security and Communication Networks

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M1 - 2503153

ER -

Cloud service scheduling algorithm research and optimization

Abstract

ASJC Scopus Subject Areas

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