Title: Improved whale social optimisation algorithm and deep fuzzy clustering for optimal and QoS-aware load balancing in cloud computing

Authors: Shelly Shiju George; R. Suji Pramila

Addresses: Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamilnadu, India; Department of Computer Applications, Amal Jyothi College of Engineering, Kanjirapally, Kottayam, Kerala, India ' Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamilnadu, India

Abstract: Cloud computing refers to computing resource sharing in order to provide various services by the internet. The load balancing technique is utilised in the distribution of workloads uniformly across servers, which enhance the effectiveness, capacity, and reliability of the network. However, load balancing is more complex, while components are available in a huge area and the variations of features in implementation time. Hence, the improved whale social optimisation algorithm (IWSOA) is developed for effectual load balancing. By integrating social optimisation algorithm (SOA) and improved whale optimisation algorithm (IWOA) the IWSOA is invented. Here, the tasks are assigned to VMs using the round robin model. The classification of VM is executed on the basis of deep fuzzy clustering. The tasks in underloaded VM are assigned on the basis of capacity, resource utilisation, demand, load, and supply. The IWSOA offers load, capacity, and resource utilisation of 0.1218, 0.5476, and 0.7122.

Keywords: improved whale optimisation algorithm; IWOA; deep fuzzy clustering; social optimisation algorithm; SOA; load balancing; VM migration.

DOI: 10.1504/IJBIC.2023.133508

International Journal of Bio-Inspired Computation, 2023 Vol.22 No.1, pp.40 - 52

Accepted: 21 Apr 2023
Published online: 18 Sep 2023 *

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