Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based

Cloud computing deals with voluminous heterogeneous data, and there is a need to effectively distribute the load across clusters of nodes to achieve optimal performance in terms of resource usage, throughput, response time, reliability, fault tolerance, and so on. The swarm intelligence methodologie...

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Autores principales:	Bhargavi K, Sathish Babu B, Pitt Jeremy
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	swarm artificial intelligence load balancing performance cloud computing efficiency Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/38d11e3651b9435ca284aaefa993c382
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spelling	oai:doaj.org-article:38d11e3651b9435ca284aaefa993c3822021-12-05T14:10:51ZPerformance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based2191-026X10.1515/jisys-2019-0084https://doaj.org/article/38d11e3651b9435ca284aaefa993c3822020-07-01T00:00:00Zhttps://doi.org/10.1515/jisys-2019-0084https://doaj.org/toc/2191-026XCloud computing deals with voluminous heterogeneous data, and there is a need to effectively distribute the load across clusters of nodes to achieve optimal performance in terms of resource usage, throughput, response time, reliability, fault tolerance, and so on. The swarm intelligence methodologies use artificial intelligence to solve computationally challenging problems like load balancing, scheduling, and resource allocation at finite time intervals. In literature, sufficient works are being carried out to address load balancing problem in the cloud using traditional swarm intelligence techniques like ant colony optimization, particle swarm optimization, cuckoo search, bat optimization, and so on. But the traditional swarm intelligence techniques have issues with respect to convergence rate, arriving at the global optimum solution, complexity in implementation and scalability, which limits the applicability of such techniques in cloud domain. In this paper, we look into performance modeling aspects of some of the recent competitive swarm artificial intelligence based techniques like the whale, spider, dragonfly, and raven which are used for load balancing in the cloud. The results and analysis are presented over performance metrics such as total execution time, response time, resource utilization rate, and throughput achieved, and it is found that the performance of the raven roosting algorithm is high compared to other techniques.Bhargavi KSathish Babu BPitt JeremyDe Gruyterarticleswarm artificial intelligenceload balancingperformancecloud computingefficiencyScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 40-58 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	swarm artificial intelligence load balancing performance cloud computing efficiency Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	swarm artificial intelligence load balancing performance cloud computing efficiency Science Q Electronic computers. Computer science QA75.5-76.95 Bhargavi K Sathish Babu B Pitt Jeremy Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based
description	Cloud computing deals with voluminous heterogeneous data, and there is a need to effectively distribute the load across clusters of nodes to achieve optimal performance in terms of resource usage, throughput, response time, reliability, fault tolerance, and so on. The swarm intelligence methodologies use artificial intelligence to solve computationally challenging problems like load balancing, scheduling, and resource allocation at finite time intervals. In literature, sufficient works are being carried out to address load balancing problem in the cloud using traditional swarm intelligence techniques like ant colony optimization, particle swarm optimization, cuckoo search, bat optimization, and so on. But the traditional swarm intelligence techniques have issues with respect to convergence rate, arriving at the global optimum solution, complexity in implementation and scalability, which limits the applicability of such techniques in cloud domain. In this paper, we look into performance modeling aspects of some of the recent competitive swarm artificial intelligence based techniques like the whale, spider, dragonfly, and raven which are used for load balancing in the cloud. The results and analysis are presented over performance metrics such as total execution time, response time, resource utilization rate, and throughput achieved, and it is found that the performance of the raven roosting algorithm is high compared to other techniques.
format	article
author	Bhargavi K Sathish Babu B Pitt Jeremy
author_facet	Bhargavi K Sathish Babu B Pitt Jeremy
author_sort	Bhargavi K
title	Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based
title_short	Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based
title_full	Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based
title_fullStr	Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based
title_full_unstemmed	Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based
title_sort	performance modeling of load balancing techniques in cloud: some of the recent competitive swarm artificial intelligence-based
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/38d11e3651b9435ca284aaefa993c382
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Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based

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