Abstract
Cloud computing is often referred to as a model that provides limitless computing services with a pay-as-you-go model. Modern cloud infrastructures provide resources as the VMs to physical machines using virtualization technology. Every virtual machine focuses on running its own operating system, and it leads to utilize resources from its host physical machine (PM). For load balancing, cloud is capable of migrating VMs from PMs which have heavy load to the ones which have light load. The objective of this process is to use the resources of a physical machine below certain threshold. Uncertainty can be a major reason of the overloading of virtual machines. Previously proposed load balancing method used genetic algorithm for the migration of the virtual machine. The delay of this algorithm increases in the network as virtual machines are migrated. This work puts forward a new algorithm, namely butterfly optimization for VM migration. The proposed optimization algorithm has been implemented in the MATLAB software. The achieved results are compared against the outcomes of the previous algorithm. The introduced approach is evaluated over three performance parameters including delay, bandwidth used and space used.
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Arshiya, Singh, J., Aggarwal, S. (2022). A Performed Optimized Load Balancing Genetic Approach Technique in Cloud Environment. In: Pundir, A.K.S., Yadav, N., Sharma, H., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-1324-2_29
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DOI: https://doi.org/10.1007/978-981-19-1324-2_29
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