A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

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 whe...

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Autores principales:	M. A. Elmagzoub, Darakhshan Syed, Asadullah Shaikh, Noman Islam, Abdullah Alghamdi, Syed Rizwan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	cloud computing load balancing swarm intelligence algorithms comparative study Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/e101b68fb3924989ab7d143814e06218
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spelling	oai:doaj.org-article:e101b68fb3924989ab7d143814e062182021-11-11T15:43:01ZA Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment10.3390/electronics102127182079-9292https://doaj.org/article/e101b68fb3924989ab7d143814e062182021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2718https://doaj.org/toc/2079-9292Cloud 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.M. A. ElmagzoubDarakhshan SyedAsadullah ShaikhNoman IslamAbdullah AlghamdiSyed RizwanMDPI AGarticlecloud computingload balancingswarm intelligence algorithmscomparative studyElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2718, p 2718 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cloud computing load balancing swarm intelligence algorithms comparative study Electronics TK7800-8360
spellingShingle	cloud computing load balancing swarm intelligence algorithms comparative study Electronics TK7800-8360 M. A. Elmagzoub Darakhshan Syed Asadullah Shaikh Noman Islam Abdullah Alghamdi Syed Rizwan A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment
description	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.
format	article
author	M. A. Elmagzoub Darakhshan Syed Asadullah Shaikh Noman Islam Abdullah Alghamdi Syed Rizwan
author_facet	M. A. Elmagzoub Darakhshan Syed Asadullah Shaikh Noman Islam Abdullah Alghamdi Syed Rizwan
author_sort	M. A. Elmagzoub
title	A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment
title_short	A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment
title_full	A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment
title_fullStr	A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment
title_full_unstemmed	A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment
title_sort	survey of swarm intelligence based load balancing techniques in cloud computing environment
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e101b68fb3924989ab7d143814e06218
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A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

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