Open Access

ARTICLE

Interpretive Structural Modeling Based Assessment and Optimization of Cloud with Internet of Things (CloudIoT) Issues Through Effective Scheduling

Anju Shukla¹, Mohammad Zubair Khan², Shishir Kumar^3,*, Abdulrahman Alahmadi², Reem Ibrahim A. Altamimi², Ahmed H. Alahmadi²

1 VIT Bhopal University, Bhopal, 466114, India
2 Department of Computer Science and Information, Taibah University, Medina, Saudi Arabia
3 Babasaheb Bhimrao Ambedkar University Lucknow, 226025, India

* Corresponding Author: Shishir Kumar. Email:

Intelligent Automation & Soft Computing 2023, 36(2), 2281-2297. https://doi.org/10.32604/iasc.2023.031931

Received 30 April 2022; Accepted 22 September 2022; Issue published 05 January 2023

Abstract

Integrated CloudIoT is an emerging field of study that integrates the Cloud and the Internet of Things (IoT) to make machines smarter and deal with real-world objects in a distributed manner. It collects data from various devices and analyses it to increase efficiency and productivity. Because Cloud and IoT are complementary technologies with distinct areas of application, integrating them is difficult. This paper identifies various CloudIoT issues and analyzes them to make a relational model. The Interpretive Structural Modeling (ISM) approach establishes the interrelationship among the problems identified. The issues are categorised based on driving and dependent power, and a hierarchical model is presented. The ISM analysis shows that scheduling is an important aspect and has both (driving and dependence) power to improve the performance of the CloudIoT model. Therefore, existing CloudIoT job scheduling algorithms are analysed, and a cloud-centric scheduling mechanism is proposed to execute IoT jobs on a suitable cloud. The cloud implementation using an open-source framework to simulate Cloud Computing (CloudSim), based on the job’s workload, is presented. Simulation results of the proposed scheduling model indicate better performance in terms of Average Waiting Time (AWT) and makespan than existing cloud-based scheduling approaches.

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Keywords

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