Abstract
Abstract 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.
Highlights
Cloud computing is a distributed computing paradigm used to store huge amount of data to provide software, platform, or infrastructure services on demand to the users with features like high tolerance, scalability, availability, robustness and so on [1]
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
Summary
Cloud computing is a distributed computing paradigm used to store huge amount of data to provide software, platform, or infrastructure services on demand to the users with features like high tolerance, scalability, availability, robustness and so on [1]. Swarm artificial intelligence techniques which are developed recently includes ageist spider monkey, shark smell optimization, whale optimization, lion optimization, spider, antlion optimization, jellyfish food search, eagle search, elephant, raven roosting, dragonfly, crow search and others. All these techniques are not suitable for cloud domain, this paper focuses on performance modeling of some of the efficient techniques used for cloud load balancing which includes whale optimization, spider, dragonfly, and raven roosting [10,11,12,13,14,15].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
