article

A review of task scheduling based on meta-heuristics approach in cloud computing

Authors:

Maitreyee Dutta,

Naveen AggarwalAuthors Info & Claims

Knowledge and Information Systems, Volume 52, Issue 1

Pages 1 - 51

https://doi.org/10.1007/s10115-017-1044-2

Published: 01 July 2017 Publication History

Abstract

Heterogeneous distributed computing systems are the emerging for executing scientific and computationally intensive applications. Cloud computing in this context describes a paradigm to deliver the resource-like computing and storage on-demand basis using pay-per-use model. These resources are managed by data centers and dynamically provisioned to the users based on their availability, demand and quality parameters required to be satisfied. The task scheduling onto the distributed and virtual resources is a main concern which can affect the performance of the system. In the literature, a lot of work has been done by considering cost and makespan as the affecting parameters for scheduling the dependent tasks. Prior work has discussed the various challenges affecting the performance of dependent task scheduling but did not consider storage cost, failure rate-related challenges. This paper accomplishes a review of using meta-heuristics techniques for scheduling tasks in cloud computing. We presented the taxonomy and comparative review on these algorithms. Methodical analysis of task scheduling in cloud and grid computing is presented based on swarm intelligence and bio-inspired techniques. This work will enable the readers to decide suitable approach for suggesting better schemes for scheduling user's application. Future research issues have also been suggested in this research work.

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cover image Knowledge and Information Systems

Knowledge and Information Systems Volume 52, Issue 1

July 2017

284 pages

ISSN:0219-1377

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Copyright © Copyright © 2017 Springer-Verlag London Ltd.

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Berlin, Heidelberg

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Published: 01 July 2017

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Choudhury AGhose MIslam AYogita (2024)Machine learning-based computation offloading in multi-access edge computingJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2024.103090148:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.sysarc.2024.103090
Fan LZhou LWang M(2024)Optimizing data regeneration and storage with data dependency for cloud scientific workflow systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121984238:PDOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121984
Mostafa RHashim FChhabra AManita GXiao Y(2024)Empowering bonobo optimizer for global optimization and cloud scheduling problemCluster Computing10.1007/s10586-024-04671-527:10(14557-14584)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10586-024-04671-5
Zhang SWang JZhang SXing YSun YGao Y(2024)Task scheduling in cloud computing systems using honey badger algorithm with improved density factor and foucault pendulum motionCluster Computing10.1007/s10586-024-04547-827:9(12411-12457)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10586-024-04547-8
Shakya STripathi P(2024)A background-based new scheduling approach for scheduling the IoT network task with data storage in cloud environmentCluster Computing10.1007/s10586-024-04400-y27:6(8577-8594)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s10586-024-04400-y
Theodoros AAndreas CDimitris C(2023)Reinforcement Learning Techniques for Optimizing System Configuration on the Cloud: A Taxonomy and Open ProblemsProceedings of the 2023 International Conference on embedded Wireless Systems and Networks10.5555/3639940.3639995(345-350)Online publication date: 15-Dec-2023
https://dl.acm.org/doi/10.5555/3639940.3639995
Canali CDi Modica GLancellotti RRossi SScotece D(2023)A Validated Performance Model for Micro-services Placement in Fog SystemsSN Computer Science10.1007/s42979-023-01847-54:4Online publication date: 24-May-2023
https://dl.acm.org/doi/10.1007/s42979-023-01847-5
Khiat AHaddadi MBahnes N(2023)Genetic-Based Algorithm for Task Scheduling in Fog–Cloud EnvironmentJournal of Network and Systems Management10.1007/s10922-023-09774-932:1Online publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1007/s10922-023-09774-9
Hammed SArunkumar B(2023)A cost effective‐ secure algorithm for work‐flow scheduling in cloud computingInternet Technology Letters10.1002/itl2.2336:1Online publication date: 11-Jan-2023
Ma YHan YWang JZhao Q(2022)A Constrained Static Scheduling Strategy in Edge Computing for Industrial Cloud SystemsInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.202101010314:1(33-61)Online publication date: 18-May-2022
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