research-article

Resource Management using Feed Forward ANN-PSO in Cloud Computing Environment

Authors:

Narander Kumar and

Pooja PatelAuthors Info & Claims

ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies

March 2016

Article No.: 57, Pages 1 - 6

https://doi.org/10.1145/2905055.2905115

Published: 04 March 2016 Publication History

Abstract

In cloud computing Environment creates a huge amount of requests for the same type of resources according to the future plan. This situation created many problems, challenges and issues in a cloud computing Environment. To handle such huge amount of requests of resources in dynamic and heterogeneous cloud computing environment, so require such scheduling algorithm that can resolve all these problems and takes decision on how to allocate Virtualized resources to the request in a cloud Environment. In this paper, the main aim to minimize the cost and Makespan and resolve their issues like Dynamic and heterogeneous cloud computing environment, reliability, availability and overloaded problem. To make available a solution to all those problems we go through ANN-PSO model to implant in the cloud computing environment. We perform experiments using ANN-PSO Model in a cloud computing environment by analyzing the same parameters of cloud like the estimated time of request and resources, performance analysis, utilization of resources, Number of particles and their Makespan and cost, fitness function. By this we find out the result that achieves the lowest cost, Makespan and resolve all these issues by using ANN-PSO model. In addition ANN-PSO Model is adjusting according to their different quality of service constraints.

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Cited By

Wang ZHu XWei PYuan B(2024)An improved particle swarm optimization algorithm for scheduling tasks in cloud environmentExpert Systems10.1111/exsy.13529Online publication date: 12-Mar-2024
https://doi.org/10.1111/exsy.13529
Prity FUddin KNath N(2023)Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospectsIran Journal of Computer Science10.1007/s42044-023-00163-87:2(337-358)Online publication date: 7-Nov-2023
https://doi.org/10.1007/s42044-023-00163-8
Gupta STripathi S(2023)A comprehensive survey on cloud computing scheduling techniquesMultimedia Tools and Applications10.1007/s11042-023-17216-683:18(53581-53634)Online publication date: 22-Nov-2023
https://doi.org/10.1007/s11042-023-17216-6
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ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies

March 2016

843 pages

ISBN:9781450339629

DOI:10.1145/2905055

Copyright © 2016 ACM.

© 2016 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

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Published: 04 March 2016

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ICTCS '16

ICTCS '16: Second International Conference on Information and Communication Technology for Competitive Strategies

March 4 - 5, 2016

Udaipur, India

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Cited By

Wang ZHu XWei PYuan B(2024)An improved particle swarm optimization algorithm for scheduling tasks in cloud environmentExpert Systems10.1111/exsy.13529Online publication date: 12-Mar-2024
https://doi.org/10.1111/exsy.13529
Prity FUddin KNath N(2023)Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospectsIran Journal of Computer Science10.1007/s42044-023-00163-87:2(337-358)Online publication date: 7-Nov-2023
https://doi.org/10.1007/s42044-023-00163-8
Gupta STripathi S(2023)A comprehensive survey on cloud computing scheduling techniquesMultimedia Tools and Applications10.1007/s11042-023-17216-683:18(53581-53634)Online publication date: 22-Nov-2023
https://doi.org/10.1007/s11042-023-17216-6
Prity FGazi MUddin K(2023)A review of task scheduling in cloud computing based on nature-inspired optimization algorithmCluster Computing10.1007/s10586-023-04090-y26:5(3037-3067)Online publication date: 29-Jun-2023
https://doi.org/10.1007/s10586-023-04090-y
Rashida SSabaei MEbadzadeh MRahmani A(2020)An intelligent approach for predicting resource usage by combining decomposition techniques with NFTS networkCluster Computing10.1007/s10586-020-03099-x23:4(3435-3460)Online publication date: 2-May-2020
https://dl.acm.org/doi/10.1007/s10586-020-03099-x
de Carvalho JTrinta FVieira DCortes O(2018)Evolutionary solutions for resources management in multiple clouds: State-of-the-art and future directionsFuture Generation Computer Systems10.1016/j.future.2018.05.08788(284-296)Online publication date: Nov-2018
https://doi.org/10.1016/j.future.2018.05.087
Sathya Sofia AGaneshkumar P(2018)Multi-objective Task Scheduling to Minimize Energy Consumption and Makespan of Cloud Computing Using NSGA-IIJournal of Network and Systems Management10.1007/s10922-017-9425-026:2(463-485)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10922-017-9425-0

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