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Swarm-Like Distributed Algorithm for Scheduling a Microservice-Based Application to the Cloud Servers
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A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

1
College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
2
Computer Science Department, Iqra University, Karachi 75500, Pakistan
*
Author to whom correspondence should be addressed.
Academic Editor: Filipe Araujo
Electronics 2021, 10(21), 2718; https://doi.org/10.3390/electronics10212718
Received: 15 September 2021 / Revised: 26 October 2021 / Accepted: 2 November 2021 / Published: 8 November 2021
(This article belongs to the Special Issue Cloud Computing and Applications, Volume II)
Cloud computing offers flexible, interactive, and observable access to shared resources on the Internet. It frees users from the requirements of managing computing on their hardware. It enables users to not only store their data and computing over the internet but also can access it whenever and wherever it is required. The frequent use of smart devices has helped cloud computing to realize the need for its rapid growth. As more users are adapting to the cloud environment, the focus has been placed on load balancing. Load balancing allocates tasks or resources to different devices. In cloud computing, and load balancing has played a major role in the efficient usage of resources for the highest performance. This requirement results in the development of algorithms that can optimally assign resources while managing load and improving quality of service (QoS). This paper provides a survey of load balancing algorithms inspired by swarm intelligence (SI). The algorithms considered in the discussion are Genetic Algorithm, BAT Algorithm, Ant Colony, Grey Wolf, Artificial Bee Colony, Particle Swarm, Whale, Social Spider, Dragonfly, and Raven roosting Optimization. An analysis of the main objectives, area of applications, and targeted issues of each algorithm (with advancements) is presented. In addition, performance analysis has been performed based on average response time, data center processing time, and other quality parameters. View Full-Text
Keywords: cloud computing; load balancing; swarm intelligence algorithms; comparative study cloud computing; load balancing; swarm intelligence algorithms; comparative study
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MDPI and ACS Style

Elmagzoub, M.A.; Syed, D.; Shaikh, A.; Islam, N.; Alghamdi, A.; Rizwan, S. A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment. Electronics 2021, 10, 2718. https://doi.org/10.3390/electronics10212718

AMA Style

Elmagzoub MA, Syed D, Shaikh A, Islam N, Alghamdi A, Rizwan S. A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment. Electronics. 2021; 10(21):2718. https://doi.org/10.3390/electronics10212718

Chicago/Turabian Style

Elmagzoub, M. A., Darakhshan Syed, Asadullah Shaikh, Noman Islam, Abdullah Alghamdi, and Syed Rizwan. 2021. "A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment" Electronics 10, no. 21: 2718. https://doi.org/10.3390/electronics10212718

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