Abstract
Recently, cloud computing is an evolving research field deployed for computing by many researchers. The computing is offered as a service in cloud which is regarded as novel technology. In order to meet customer necessitates, various services are offered on the basis of customer dynamic request continuously in cloud computing, and it is the foremost task of cloud computing for providing the desired services to every consumer. The challenge lies in servicing all the customers with the limited existing resource, and it has been tricky to meet all the demanded services by the cloud providers. The allotment of perspective cloud resources through the cloud providers is yet another endeavor which should be done in reasonable way. Hence, cloud consumers’ quality of service and fulfillment are the most noteworthy factors to be considered. The prevailing research discussed about the challenges, techniques, key performance issues etc., encompassed in cloud computing resource sharing. Ant colony optimization algorithm is greatly utilized for optimizer analysis of load on physical machine on the basis of local migration agent which aids in migrated VMs load computation and for choosing proper physical server. Conversely, trapping of local optima may happen at certain time which in turn impacts on performance degradation pertaining to global search. The search diversity enhancing is one among the possible solutions for evading the trapping into local optima in ACO. Mutation-based improved ant colony optimization (IACO) is greatly deployed in this research work for analysis of physical machine load VM migration besides effectual resource exploitation.
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Bhardwaj, D., Gupta, A.K., Sharma, A. (2022). Improved Ant Colony Optimization for Optimal Resource Utilization in Cloud Computing. In: Gao, XZ., Tiwari, S., Trivedi, M.C., Singh, P.K., Mishra, K.K. (eds) Advances in Computational Intelligence and Communication Technology. Lecture Notes in Networks and Systems, vol 399. Springer, Singapore. https://doi.org/10.1007/978-981-16-9756-2_38
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DOI: https://doi.org/10.1007/978-981-16-9756-2_38
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