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Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
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J. Sens. Actuator Netw. 2019, 8(3), 44; https://doi.org/10.3390/jsan8030044
Received: 30 June 2019 / Revised: 31 July 2019 / Accepted: 1 August 2019 / Published: 11 August 2019
(This article belongs to the Special Issue Advances in Sensor Networks for Smart Cities)
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Abstract

Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer networks (the Internet). Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state-of-the-art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management.
Keywords: cloud computing; cloudlet scheduling; NP-hard problems; swarm intelligence; monarch butterfly optimization cloud computing; cloudlet scheduling; NP-hard problems; swarm intelligence; monarch butterfly optimization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Strumberger, I.; Tuba, M.; Bacanin, N.; Tuba, E. Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm. J. Sens. Actuator Netw. 2019, 8, 44.

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