YUVARAJ GANDHI, S and REVATHI, T (2023) Optimizing Workload Scheduling in Cloud Paradigm using Robust Neutrosophic C-Means Clustering Boosted with Fish School Search. Work Scheduling, Uncertainty, Neutrosophic C-Means clustering, Fish School Search Algorithm, indeterminacy., 23. ISSN 2224-2678

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Abstract

In the present internet world, accessing cloud resources for a low cost, according to their needs, is
available to all users. Sharing resources is becoming increasingly necessary as people complete their activities
in the cloud. It becomes essential for distributed workloads to be optimized to perform efficient workload
scheduling and progressing resource utilization in a cloud environment. Scheduling cloud resources
considerably benefits from the invention of machine learning and metaheuristic models to address this scenario.
Though many existing algorithms are developed in cloud-based task scheduling using unsupervised clustering
methods, the problem of unknown task requirements or resource availability in adverse conditions is still
challenging. In this study, an uncertainty-based unsupervised technique is constructed to group incoming tasks
according to the required resources, and it is scheduled to the most suitable resources more prominently. This
paper introduced a Robust Neutrosophic C-Means Clustering boosted with the fish school search algorithm
(RNCM-FSSA) for clustering the incoming tasks and the resources based on their requirement and availability.
With the degree of indeterminacy, neutrosophic C-means discriminating the deterministic and indeterministic
schemes and scheduling them to the optimal resources more effectively. Using the fitness value computed by
FFSA, the potential cluster centroids are utilized for clustering, thus avoiding the early convergence in the
grouping process. The simulation results explore that the robustness of the proposed RCNM-SSA achieves
better resource utilization, the degree of imbalance is minimal, and computation complexity is also
considerably decreased compared with other unsupervised models

Item Type: Article
Uncontrolled Keywords: Work Scheduling, Uncertainty, Neutrosophic C-Means clustering, Fish School Search Algorithm, indeterminacy.
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Mr Team Mosys
Date Deposited: 18 Apr 2024 06:33
Last Modified: 18 Apr 2024 06:33
URI: http://ir.psgcas.ac.in/id/eprint/2136

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