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Load balancing in cloud computing using worst-fit bin-stretching

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Abstract

Cloud computing has emerged as a new cost-efficient technology that provides on-demand resources over the Internet for users who pay only for their actual use. Load balancing plays an important role in cloud computing; it schedules the tasks on the virtual machines effectively to ensure cost-efficient execution of users tasks and optimal utilization of cloud resources. Because load balancing is a NP-hard optimization problem, much effort has been directed toward proposing fast algorithms that approximate the optimal solution. This paper deals with this problem and proposes new load balancing algorithms that meet requirements of cloud users and providers by reducing the makespan and improving resource utilization. For this, we modeled load balancing as a bin-stretching problem. By adopting the Worst-Fit heuristic to the bin-stretching problem, we propose a new load balancing algorithm called Worst-Fit-Based Load balancing algorithm (WFBLBA). Furthermore, by investigating the Decreasing Worst-Fit heuristic, we propose a decreasing variant of load balancing algorithm (WFDBLBA). Experimental evaluation using CloudSim simulator show that our algorithms not only outperform compared heuristics in terms of makespan, resource utilization and waiting time, but also cope better with high machine heterogeneity than compared ones.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the General Research Project under grant number (GRP-40-331).

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Authors and Affiliations

  1. Computer Science Department, King Khalid University, Abha, Kingdom of Saudi Arabia

    Sami Dhahbi

  2. LIMTIC Laboratory, University of Tunis ElManar, Tunis, Tunisia

    Sami Dhahbi

  3. Computer Science department, University of Monastir, Tunis, Tunisia

    Mouhebeddine Berrima

  4. Computer Science Department, King Khalid University, Abha, Kingdom of Saudi Arabia

    Fuad A. M. Al-Yarimi

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  1. Sami Dhahbi
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  2. Mouhebeddine Berrima
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  3. Fuad A. M. Al-Yarimi
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Correspondence to Sami Dhahbi.

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Appendix A: Analysis with time-shared policy

Appendix A: Analysis with time-shared policy

Here we present simulation result using time-shared as scheduling policy. Here the number of PEs of VMs is choosen random from [1-4]. From the below figure we can see that the results of makespan are coherent with those using space-shared policy. In particular, our algorithms are more efficient in high machine heterogeneity configurations.

Fig. 8
figure 8

Analysis of the Makespan

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Dhahbi, S., Berrima, M. & Al-Yarimi, F.A.M. Load balancing in cloud computing using worst-fit bin-stretching. Cluster Comput 24, 2867–2881 (2021). https://doi.org/10.1007/s10586-021-03302-7

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  • DOI: https://doi.org/10.1007/s10586-021-03302-7

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