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2018 | OriginalPaper | Buchkapitel

A Load Balancing Algorithm for Resource Allocation in Cloud Computing

verfasst von : Seyedmajid Mousavi, Amir Mosavi, Annamária R. Varkonyi-Koczy

Erschienen in: Recent Advances in Technology Research and Education

Verlag: Springer International Publishing

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Abstract

Utilizing dynamic resource allocation for load balancing is considered as an important optimization process of task scheduling in cloud computing. A poor scheduling policy may overload certain virtual machines while remaining virtual machines are idle. Accordingly, this paper proposes a hybrid load balancing algorithm with combination of Teaching-Learning-Based Optimization (TLBO) and Grey Wolves Optimization algorithms (GWO), which can well contribute in maximizing the throughput using well balanced load across virtual machines and overcome the problem of trap into local optimum. The hybrid algorithm is benchmarked on eleven test functions and a comparative study is conducted to verify the results with particle swarm optimization (PSO), Biogeography-based optimization (BBO), and GWO. To evaluate the performance of the proposed algorithm for load balancing, the hybrid algorithm is simulated and the experimental results are presented.

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Vorheriges Kapitel Ontology Extension for Personalized Accessible Indoor Navigation

Nächstes Kapitel A Fuzzy Data Structure for Variable Length Data and Missing Value Classification

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Titel: A Load Balancing Algorithm for Resource Allocation in Cloud Computing
verfasst von: Seyedmajid Mousavi
Amir Mosavi
Annamária R. Varkonyi-Koczy
Verlag: Springer International Publishing
Buch: Recent Advances in Technology Research and Education
Print ISBN: 978-3-319-67458-2

Electronic ISBN: 978-3-319-67459-9

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-67459-9_36

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