Research Article Minimization of Latency Using Multitask Scheduling in Industrial Autonomous Systems Amit Singhal , 1 Sudeep Varshney, 2 T. A. Mohanaprakash, 3 R. Jayavadivel, 4 K. Deepti, 5 Pundru Chandra Shaker Reddy , 6 and Molla Bayih Mulat 7 1 Department of Computer science and Engineering, Raj Kumar Goel Institute of Technology, Ghaziabad, Uttar Pradesh 201003, India 2 Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh 201301, India 3 Department of Computer science and Engineering, Panimalar Engineering College, Chennai, Tamilnadu, India 4 Department of Computer Science and Engineering, School of Engineering, Presidency University, Bangalore, Karnataka 560064, India 5 Department of Electronics & Communication Engineering, Vasavi College of Engineering, Hyderabad, Telangana, India 6 School of Computing and Information Technology, REVA University, Bangalore, India 7 Department of Chemical Engineering College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia Correspondence should be addressed to Amit Singhal; amitsfcs@rkgit.edu.in and Molla Bayih Mulat; molla.bayih@aastustudent.edu.et Received 17 May 2022; Revised 1 July 2022; Accepted 9 July 2022; Published 21 July 2022 Academic Editor: Kalidoss Rajakani Copyright © 2022 Amit Singhal et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Using enhanced ant colony optimization, this study proposes an efficient heuristic scheduling technique for cloud infrastructure that addresses the issues with nonlinear loads, slow processing complexity, and incomplete shared memory asset knowledge that plagued earlier resource supply implementations. The cloud-based planning architecture has been tailored for dynamic planning. Therefore, to determine the best task allocation method, a contentment factor was developed by integrating these three objectives of the smallest waiting period, the extent of commodity congestion control, and the expense of goal accomplishment. Ultimately, the incentive and retribution component would be used to modify the ant colony calculation perfume-generating criteria that accelerate a solution time. In particular, they leverage an activity contributed of the instability component to enhance the capabilities of such a method, and they include a virtual desktop burden weight component in the operation of regional pheromone revamping to assure virtual computers’ immense. Experiences with the routing protocol should be used to explore or demonstrate the feasibility of our methodology. In comparison with traditional methods, the simulation results show that the proposed methodology has the most rapid generalization capability, and it has the shortest duration of the project, the most distributed demand, and the best utilization of the capabilities of the virtual computer. Consequently, their hypothetical technique of optimizing the supply of resources exceeds world competition. 1. Introduction Cloud technology establishes a major era inside the expan- sion of online virtualization. It has a more substantial benefit over pervasive computing. It is an extremely effective multi- faceted distributed architecture, and cloud hosting was made more efficient by massive computational services [1]. In cloud technology, the tasks that should be accomplished were allocated or dispersed among numerous computational powers. Distributed storage [2] enables consumers to get quantitative and consultancy services as well as adequate backup facilities according to their demands. Clustering is Hindawi Wireless Communications and Mobile Computing Volume 2022, Article ID 1671829, 10 pages https://doi.org/10.1155/2022/1671829