Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing
Author:
Mangalampalli Sudheer1ORCID, Karri Ganesh Reddy1ORCID, Gupta Amit2, Chakrabarti Tulika3, Nallamala Sri Hari4, Chakrabarti Prasun5, Unhelkar Bhuvan6ORCID, Margala Martin7
Affiliation:
1. School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, India 2. Department of ECE, Nalla Malla Reddy Engineering College, Hyderabad 500088, India 3. Department of Chemistry, Sir Padampat Singhania University, Udaipur 313601, India 4. Vasireddy Venkatadri Institute of Technology, Nambur 522510, India 5. Department of Computer Science and Engineering, Sir Padampat Singhania University, Udaipur 313601, India 6. Muma School of Business, University of South Florida, Sarasota-Manatee, FL 33620, USA 7. School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Abstract
Cloud computing is a distributed computing model which renders services for cloud users around the world. These services need to be rendered to customers with high availability and fault tolerance, but there are still chances of having single-point failures in the cloud paradigm, and one challenge to cloud providers is effectively scheduling tasks to avoid failures and acquire the trust of their cloud services by users. This research proposes a fault-tolerant trust-based task scheduling algorithm in which we carefully schedule tasks within precise virtual machines by calculating priorities for tasks and VMs. Harris hawks optimization was used as a methodology to design our scheduler. We used Cloudsim as a simulating tool for our entire experiment. For the entire simulation, we used synthetic fabricated data with different distributions and real-time supercomputer worklogs. Finally, we evaluated the proposed approach (FTTATS) with state-of-the-art approaches, i.e., ACO, PSO, and GA. From the simulation results, our proposed FTTATS greatly minimizes the makespan for ACO, PSO and GA algorithms by 24.3%, 33.31%, and 29.03%, respectively. The rate of failures for ACO, PSO, and GA were minimized by 65.31%, 65.4%, and 60.44%, respectively. Trust-based SLA parameters improved, i.e., availability improved for ACO, PSO, and GA by 33.38%, 35.71%, and 28.24%, respectively. The success rate improved for ACO, PSO, and GA by 52.69%, 39.41%, and 38.45%, respectively. Turnaround efficiency was minimized for ACO, PSO, and GA by 51.8%, 47.2%, and 33.6%, respectively.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference43 articles.
1. Resource scheduling methods in cloud and fog computing environments: A systematic literature review;Rahimikhanghah;Clust. Comput.,2022 2. Mangalampalli, S., Sree, P.K., Swain, S.K., and Karri, G.R. (2023). Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation, Scrivener Publishing LLC. 3. Journey from cloud of things to fog of things: Survey, new trends, and research directions;Chakraborty;Softw. Pract. Exp.,2023 4. Shao, K., Song, Y., and Wang, B. (2023). PGA: A New Hybrid PSO and GA Method for Task Scheduling with Deadline Constraints in Distributed Computing. Mathematics, 11. 5. Cost-based hierarchy genetic algorithm for service scheduling in robot cloud platform;Yin;J. Cloud Comput.,2023
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