Abstract
Objectives: To perform task scheduling with minimising the makespan through implementing an effective load balancing approach. Methods: In this study, the Fuzzy Topsis algorithm (FTPOSIS) is used for the task scheduling and the makespan is minimised with the effective load balancing by modelling the whale optimization algorithm (WOA). Findings: This proposed model controls the admittance of the requests by achieving target QoS in terms of response time. Hence, the admittance is controlled so that the requests which are accepted do not face a delay greater than the time limit stated in the SLA. Using CloudSim tool the simulation is done and the results are exhibited. The effectiveness of the intended algorithm is compared with the existing methods.Novelty: The novelty of this study includes increasing the throughput of the cloud system by reducing the makespan of the cloud scheduling process.Reducing SLA violations and improving the QoS can efficiently give assurance to reduce the delay of transmission, packet loss rate of data. Attaining a balance between constrained resources and QoS. Keywords: Cloud computing system; load balancing; scheduling; makespan;FTOPSIS; WOA; task scheduling
Highlights
The components of Cloud computing are grid computing, distributed computing, autonomic computing and utility computing
In literature lot of heuristic and metaheuristic algorithms are available in cloud resource management, which are presented for load balancing and task scheduling [1,2,3]
In this study TOPSIS is extended to the fuzzy environment to propose the fuzzy TOPSIS (FTOPSIS) algorithm for scheduling in an effective manner based on the size of the task, request priority and optimal distance between the server and the client nodes
Summary
The components of Cloud computing are grid computing, distributed computing, autonomic computing and utility computing. In literature lot of heuristic and metaheuristic algorithms are available in cloud resource management, which are presented for load balancing and task scheduling [1,2,3]. To reduce the resource access a large volume of users can access the same resource To handle this problem certain load balancing and task scheduling algorithms have been established in a friendly manner[7]. The task of the consumers must be done with minimum expenses and there is a need for the cloud providers for the utilization of resources with significant gain To entice their customers the cloud service providers apart from balancing the load must fulfill the QoS parameters [11,12,13].
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