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Neural Computing and Applications

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29-04-2020 | Original Article

Autonomic cloud resource provisioning and scheduling using meta-heuristic algorithm

Authors: Mohit Kumar, S. C. Sharma, Shalini Goel, Sambit Kumar Mishra, Akhtar Husain

Published in: Neural Computing and Applications | Issue 24/2020

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Abstract

We investigate that resource provisioning and scheduling is a prominent problem due to heterogeneity as well as dispersion of cloud resources. Cloud service providers are building more and more datacenters due to demand of high computational power which is a serious threat to environment in terms of energy requirement. To overcome these issues, we need an efficient meta-heuristic technique that allocates applications among the virtual machines fairly and optimizes the quality of services (QoS) parameters to meet the end user objectives. Binary particle swarm optimization (BPSO) is used to solve real-world discrete optimization problems but simple BPSO does not provide optimal solution due to improper behavior of transfer function. To overcome this problem, we have modified transfer function of binary PSO that provides exploration and exploitation capability in better way and optimize various QoS parameters such as makespan time, energy consumption, and execution cost. The computational results demonstrate that modified transfer function-based BPSO algorithm is more efficient and outperform in comparison with other baseline algorithm over various synthetic datasets.

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Title: Autonomic cloud resource provisioning and scheduling using meta-heuristic algorithm
Authors: Mohit Kumar
S. C. Sharma
Shalini Goel
Sambit Kumar Mishra
Akhtar Husain
Publication date: 29-04-2020
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 24/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-04955-y

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