A review of metaheuristic scheduling techniques in cloud computing

Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on-demand access to shared pool of resources over Internet in a self-service, dynamically scalable and metered manner. Cloud computing is still in its infancy, so to reap its full benefits, muc...

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Bibliographic Details
Published inEgyptian informatics journal Vol. 16; no. 3; pp. 275 - 295
Main Authors Kalra, Mala, Singh, Sarbjeet
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.11.2015
Elsevier
Subjects
Ant colony optimization
Cloud task scheduling
Genetic algorithm and particle swarm optimization
League Championship Algorithm (LCA) and BAT algorithm
Metaheuristic techniques
League Championship Algorithm (LCA) and BAT algorithm
Ant colony optimization
Cloud task scheduling
Genetic algorithm and particle swarm optimization
Metaheuristic techniques
Online AccessGet full text
ISSN1110-8665
2090-4754
DOI10.1016/j.eij.2015.07.001

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Abstract Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on-demand access to shared pool of resources over Internet in a self-service, dynamically scalable and metered manner. Cloud computing is still in its infancy, so to reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map tasks to appropriate resources that optimize one or more objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes a long time to find an optimal solution. There are no algorithms which may produce optimal solution within polynomial time to solve these problems. In cloud environment, it is preferable to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey and comparative analysis of various scheduling algorithms for cloud and grid environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), and two novel techniques: League Championship Algorithm (LCA) and BAT algorithm.
AbstractList Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on-demand access to shared pool of resources over Internet in a self-service, dynamically scalable and metered manner. Cloud computing is still in its infancy, so to reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map tasks to appropriate resources that optimize one or more objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes a long time to find an optimal solution. There are no algorithms which may produce optimal solution within polynomial time to solve these problems. In cloud environment, it is preferable to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey and comparative analysis of various scheduling algorithms for cloud and grid environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), and two novel techniques: League Championship Algorithm (LCA) and BAT algorithm.
Author Kalra, Mala
Singh, Sarbjeet
Author_xml – sequence: 1
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  surname: Kalra
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  givenname: Sarbjeet
  surname: Singh
  fullname: Singh, Sarbjeet
  email: sarbjeet@pu.ac.in
  organization: Computer Science and Engineering Department, UIET, Panjab University, Chandigarh, India
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Issue 3
Keywords League Championship Algorithm (LCA) and BAT algorithm
Ant colony optimization
Cloud task scheduling
Genetic algorithm and particle swarm optimization
Metaheuristic techniques
Language English
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Snippet Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on-demand access to shared pool of resources over...
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SubjectTerms Ant colony optimization
Cloud task scheduling
Genetic algorithm and particle swarm optimization
League Championship Algorithm (LCA) and BAT algorithm
Metaheuristic techniques
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Title A review of metaheuristic scheduling techniques in cloud computing
URI https://dx.doi.org/10.1016/j.eij.2015.07.001
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