Optimizing Workload Scheduling in Cloud Paradigm using Robust Neutrosophic C-Means Clustering Boosted with Fish School Search

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.