Open Access

ARTICLE

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

by M. Manikandan^1,*, R. Subramanian², M. S. Kavitha³, S. Karthik³

1 Department of Computer Science & Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India
2 Department of Electrical and Electronics Engineering, SNS College of Technology, Coimbatore, 641035, India
3 Department of Computer Science & Engineering, SNS College of Technology, Coimbatore, 641035, India

* Corresponding Author: M. Manikandan. Email:

Computer Systems Science and Engineering 2022, 42(3), 935-948. https://doi.org/10.32604/csse.2022.021816

Received 15 July 2021; Accepted 18 August 2021; Issue published 08 February 2022

Abstract

In today’s world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds. Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation. The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines. The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications.

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Keywords

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