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Volume 7
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10.3390/electronics7120389
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Article

Virtual Machine Placement via Bin Packing in Cloud Data Centers

by
Aisha Fatima
1,
Nadeem Javaid
1,*,
Tanzeela Sultana
1,
Waqar Hussain
2,
Muhammad Bilal
3,
Shaista Shabbir
4,
Yousra Asim
1,
Mariam Akbar
1 and
Manzoor Ilahi
1
1
Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
2
Department of Computer Science, University of Kotli, Azad Jammu and Kashmir 11100, Pakistan
3
Centre for Data Science and Analytics, School of Computing and IT, Taylor’s University, Subang Jaya 47500, Malaysia
4
Kotli Campus, Virtual University of Pakistan, Azad Kashmir 11100, Pakistan
*
Author to whom correspondence should be addressed.
Electronics 2018, 7(12), 389; https://doi.org/10.3390/electronics7120389
Submission received: 15 November 2018 / Revised: 28 November 2018 / Accepted: 29 November 2018 / Published: 4 December 2018
(This article belongs to the Special Issue Green Communications in Smart City)
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Abstract

With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.
Keywords: cloud computing; virtual machine placement; levy flight algorithm; particle swarm optimization; variable sized bin packing cloud computing; virtual machine placement; levy flight algorithm; particle swarm optimization; variable sized bin packing

Share and Cite

MDPI and ACS Style

Fatima, A.; Javaid, N.; Sultana, T.; Hussain, W.; Bilal, M.; Shabbir, S.; Asim, Y.; Akbar, M.; Ilahi, M. Virtual Machine Placement via Bin Packing in Cloud Data Centers. Electronics 2018, 7, 389. https://doi.org/10.3390/electronics7120389

AMA Style

Fatima A, Javaid N, Sultana T, Hussain W, Bilal M, Shabbir S, Asim Y, Akbar M, Ilahi M. Virtual Machine Placement via Bin Packing in Cloud Data Centers. Electronics. 2018; 7(12):389. https://doi.org/10.3390/electronics7120389

Chicago/Turabian Style

Fatima, Aisha, Nadeem Javaid, Tanzeela Sultana, Waqar Hussain, Muhammad Bilal, Shaista Shabbir, Yousra Asim, Mariam Akbar, and Manzoor Ilahi. 2018. "Virtual Machine Placement via Bin Packing in Cloud Data Centers" Electronics 7, no. 12: 389. https://doi.org/10.3390/electronics7120389

APA Style

Fatima, A., Javaid, N., Sultana, T., Hussain, W., Bilal, M., Shabbir, S., Asim, Y., Akbar, M., & Ilahi, M. (2018). Virtual Machine Placement via Bin Packing in Cloud Data Centers. Electronics, 7(12), 389. https://doi.org/10.3390/electronics7120389

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