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Non-dominated Ranking Biogeography Based Optimization Algorithm for Virtual Machine Placement in Cloud Computing

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Intelligent Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 283))

Abstract

In cloud environment, placing virtual machines in appropriate servers with guarantee of high performance level is a crucial problem, known to be NP-hard due to its complexity and large solutions space. In this paper, a Non-dominated Ranking Biogeography Based Optimization algorithm, named NRBBO, is proposed in order to minimize simultaneously the total resource wastage and power consumption of all servers in virtual machines placement. Using Synthetic data from literature, the effectiveness of the proposed approach has been investigated by conducting several experiments. NRBBO is compared to other multi-objective solutions and the obtained results show that NRBBO is more efficient and has better convergence and coverage in finding the optimal solutions for the virtual machines problem.

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Authors and Affiliations

  1. Electronics and Computer Science Faculty, Laboratory LSI, USTHB, El Alia, BP 32 16111, Bab-Ezzouar, Algiers, Algeria

    Asma Bouhank & Mourad Daoudi

Authors
  1. Asma Bouhank
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  2. Mourad Daoudi
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Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Bouhank, A., Daoudi, M. (2022). Non-dominated Ranking Biogeography Based Optimization Algorithm for Virtual Machine Placement in Cloud Computing. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_25

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