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Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

R. Joshua Samuel Raj1, M. Varalatchoumy2, V. L. Helen Josephine3, A. Jegatheesan4, Seifedine Kadry5, Maytham N. Meqdad6, Yunyoung Nam7,*

1 Department of Information Science & Engineering, CMR Institute of Technology, Bengaluru, 560037, India
2 Department of Computer Science & Engineering, Cambridge Institute of Technology, Bengaluru, 560036, India
3 Department of Computer Applications, CMR Institute of Technology, Bengaluru, 560037, India
4 Department of Computer Science & Engineering, Swarnandhra College of Engineering and Technology, Narasapur, 534280, India
5 Department of Applied Data Science, Noroff University College, Kristiansand, 4612, Norway
6 Department of Computer Techniques Engineering, Al-Mustaqbal University College, Hillah, 51001, Iraq
7 Department of Computer Science and Engineering, Soonchunhyang University, 31538, Korea

* Corresponding Author: Yunyoung Nam. Email: email

Computers, Materials & Continua 2022, 71(1), 1095-1109. https://doi.org/10.32604/cmc.2022.021859

Abstract

Internet of Things (IoT) is transforming the technical setting of conventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to the IoT enabled models are resource-limited and necessitate crisp responses, low latencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentioned challenges. But the intrinsic high latency of CC makes it nonviable. The longer latency degrades the outcome of IoT based smart systems. CC is an emergent dispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes the energy utilization of the cloud infrastructure and rises the income of service providers by the minimization of the processing time of the user job. With this motivation, this paper presents an intelligent Chaotic Artificial Immune Optimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabled cloud environment. The proposed CAIOA-RS algorithm solves the issue of resource allocation in the IoT enabled cloud environment. It also satisfies the makespan by carrying out the optimum task scheduling process with the distinct strategies of incoming tasks. The design of CAIOA-RS technique incorporates the concept of chaotic maps into the conventional AIOA to enhance its performance. A series of experiments were carried out on the CloudSim platform. The simulation results demonstrate that the CAIOA-RS technique indicates that the proposed model outperforms the original version, as well as other heuristics and metaheuristics.

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APA Style
Raj, R.J.S., Varalatchoumy, M., Josephine, V.L.H., Jegatheesan, A., Kadry, S. et al. (2022). Evolutionary algorithm based task scheduling in iot enabled cloud environment. Computers, Materials & Continua, 71(1), 1095-1109. https://doi.org/10.32604/cmc.2022.021859
Vancouver Style
Raj RJS, Varalatchoumy M, Josephine VLH, Jegatheesan A, Kadry S, Meqdad MN, et al. Evolutionary algorithm based task scheduling in iot enabled cloud environment. Comput Mater Contin. 2022;71(1):1095-1109 https://doi.org/10.32604/cmc.2022.021859
IEEE Style
R.J.S. Raj et al., "Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment," Comput. Mater. Contin., vol. 71, no. 1, pp. 1095-1109. 2022. https://doi.org/10.32604/cmc.2022.021859



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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