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A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments

Authors:

Jongbeom LimAuthors Info & Claims

Human-centric Computing and Information Sciences, Volume 7, Issue 1

Article No.: 109, Pages 1 - 10

https://doi.org/10.1186/s13673-017-0109-2

Published: 01 December 2017 Publication History

Abstract

Since cloud computing provides computing resources on a pay per use basis, a task scheduling algorithm directly affects the cost for users. In this paper, we propose a novel cloud task scheduling algorithm based on ant colony optimization that allocates tasks of cloud users to virtual machines in cloud computing environments in an efficient manner. To enhance the performance of the task scheduler in cloud computing environments with ant colony optimization, we adapt diversification and reinforcement strategies with slave ants. The proposed algorithm solves the global optimization problem with slave ants by avoiding long paths whose pheromones are wrongly accumulated by leading ants.

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Cited By

Jiao G(2022)Simulation Research on the Palm Mechanism of Volleyball Robot Based on Artificial Intelligence and Ant Colony Optimization AlgorithmSecurity and Communication Networks10.1155/2022/60305452022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6030545
Subramanian MNarayanan MBhasker BGnanavel SHabibur Rahman MPradeep Reddy C(2022)Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control SystemComplexity10.1155/2022/45252202022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4525220
Xiao XLi CJiang BCai QLi KTang Z(2022)Adaptive search strategy based chemical reaction optimization scheme for task scheduling in discrete multiphysical coupling applicationsApplied Soft Computing10.1016/j.asoc.2022.108748121:COnline publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.108748
Show More Cited By

A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning

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Published In

cover image Human-centric Computing and Information Sciences

Human-centric Computing and Information Sciences Volume 7, Issue 1

December 2017

729 pages

ISSN:2192-1962

EISSN:2192-1962

Issue’s Table of Contents

Copyright © Copyright © 2017 The Author(s).

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 December 2017

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Citations

Cited By

Jiao G(2022)Simulation Research on the Palm Mechanism of Volleyball Robot Based on Artificial Intelligence and Ant Colony Optimization AlgorithmSecurity and Communication Networks10.1155/2022/60305452022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6030545
Subramanian MNarayanan MBhasker BGnanavel SHabibur Rahman MPradeep Reddy C(2022)Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control SystemComplexity10.1155/2022/45252202022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4525220
Xiao XLi CJiang BCai QLi KTang Z(2022)Adaptive search strategy based chemical reaction optimization scheme for task scheduling in discrete multiphysical coupling applicationsApplied Soft Computing10.1016/j.asoc.2022.108748121:COnline publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.108748
Mangalampalli SSwain SMangalampalli V(2022)Prioritized Energy Efficient Task Scheduling Algorithm in Cloud Computing Using Whale Optimization AlgorithmWireless Personal Communications: An International Journal10.1007/s11277-021-09018-6126:3(2231-2247)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11277-021-09018-6
Talaat FAli HSaraya MSaleh A(2022)Effective scheduling algorithm for load balancing in fog environment using CNN and MPSOKnowledge and Information Systems10.1007/s10115-021-01649-264:3(773-797)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10115-021-01649-2
Ajmal MIqbal ZKhan FAhmad MAhmad IGupta B(2021)Hybrid ant genetic algorithm for efficient task scheduling in cloud data centersComputers and Electrical Engineering10.1016/j.compeleceng.2021.10741995:COnline publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1016/j.compeleceng.2021.107419
Abualigah LDiabat A(2021)A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environmentsCluster Computing10.1007/s10586-020-03075-524:1(205-223)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s10586-020-03075-5
Singhal SSharma A(2021)A job scheduling algorithm based on rock hyrax optimization in cloud computingComputing10.1007/s00607-021-00942-w103:9(2115-2142)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1007/s00607-021-00942-w
Sun YLi JFu XWang HLi H(2020)Application research based on improved genetic algorithm in cloud task schedulingJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-17939838:1(239-246)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/JIFS-179398
Han SKim YJeon YPark JPark DHwang DJeong C(2020)Distributed deep learning platform for pedestrian detection on IT convergence environmentThe Journal of Supercomputing10.1007/s11227-020-03195-076:7(5460-5485)Online publication date: 1-Jul-2020
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