Improved whale social optimisation algorithm and deep fuzzy clustering for optimal and QoS-aware load balancing in cloud computing Online publication date: Mon, 18-Sep-2023
by Shelly Shiju George; R. Suji Pramila
International Journal of Bio-Inspired Computation (IJBIC), Vol. 22, No. 1, 2023
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Bio-Inspired Computation (IJBIC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook