An Optimization-Based Approach for Task Scheduling to Enhance Resource Utilization in Cloud Computing
Keywords:
HEFT, Resource Utilization, PSO, ACOAbstract
In cloud computing, job scheduling is closely related to processing costs and resource use. To ensure the best work completion, a variety of optimal task scheduling techniques make good use of these parameters. Task scheduling especially increases the cloud-based system's source utilization and processing costs. In order to provide optimal scheduling, numerous optimization methodologies are used to enhance task scheduling performance. Several heuristic scheduling techniques, including Min-Min, Max-Min, and Heterogeneous Earliest Finish Time (HEFT) algorithms, have been developed and created for cloud-based systems. Several meta-heuristic task scheduling methods that produce ideal schedules have also been created, including the scheduling technique based on the Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). Task scheduling is a plan for controlling the situation in which work must be done. It is providing resources to appropriate activities; when tasks are introduced with an eye towards their completion, it appears in the NP-hard crisis feature due to a large number of solution explorations and accepts the longest possible time for determining the best outcomes. In a cloud environment, results are produced at their best through resource maintenance processes. Task scheduling solves the problem where resources must be divided among numerous jobs to maximize resource utilization and shorten operating times. Scheduling approaches must be effective in order to achieve higher utilization, and they believe that the network's overall load should be balanced, disturbances should be managed, errors should be tolerated, and the execution time should be reduced.
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C. Rajan, K. Geetha, C. Rasi Priya and R. Sasikala, “Investigation on Bio- Inspired Population-Based Metaheuristic Algorithms for Optimization Problems in Ad Hoc Networks”, World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering, Vol. 9, No. 3, pp-1-8, 2015.
Chia Wei Tseng, Fan Hsun Tseng, Yao Tsung Yang, Chien Chang Liu and Li Der Chou, “Task Scheduling for Edge Computing with Agile VNFs On-Demand Service Model toward 5G and Beyond”, Wireless Communications and Mobile Computing, Hindawi, pp-1-13, 2018.
Davneet Singh Chawla and Kanwalvir Singh Dhindsa, “A Load Balancing based Improved Task Scheduling Algorithm in Cloud Computing”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Vol. 5, Issue 9, pp-161-170, 2017.
Davinder Kaur, and Sarpreet Singh, “An Efficient Job Scheduling Algorithm using Min-Min and Ant Colony Concept for Grid Computing”, International Journal of Engineering and Computer Science, Volume 03, Issue 07, pp-6943-6949, 2014.
Davinder Kaur, and Sarpreet Singh, “An Improved min - min Algorithm for Job Scheduling using Ant Colony Optimization”, International Journal of Computer Science and Mobile Computing, IJCSMC, Vol. 3, Issue. 5, pp-552–556, 2014.
Dezhong Yao, Chen Yu, Hai Jin and Jiehan Zhou, “Energy Efficient Task Scheduling in Mobile Cloud Computing”, 10th International Conference on Network and Parallel Computing (NPC), HAL, Guiyang, China, Springer, Lecture Notes in Computer Science, pp-344-355, 2013.
Elhossiny Ibrahim, Nirmeen A. El-Bahnasawy, and Fatma Omara, “Dynamic Task Scheduling in Cloud Computing Based on the Availability Level of Resources” International Journal of Grid and Distributed Computing Vol. 10, No. 8, pp.21-36, 2017. (http://dx.doi.org/10.14257/ijgdc.2017.10.8.03)
Elhossiny Ibrahim, Nirmeen A El-Bahnasawy and Fatma A Omara, “Job Scheduling based on Harmonization between the Requested and Available Processing Power in the Cloud Computing Environment”, Journal of Information Technology & Software Engineering, Vol. 5, Issue 3, pp-1-4, 2015.
Pradeep K, Ali LJ, Gobalakrishnan N, Raman CJ, Manikandan N (2022) CWOA: Hybrid approach for task scheduling in cloud environment. Comput J 65:1860–1873.
Natesan G, Chokkalingam A. An improved grey wolf optimization algorithm based task scheduling in cloud computing environment. Int. Arab J. Inf. Technol. 2020, 17, 73–81.
Gobalakrishnan N, Arun C (2018) A new multi-objective optimal programming model for task scheduling using genetic gray wolf optimization in cloud computing. Comput J 61:1523–1536.
Casas I, Taheri J, Ranjan R, Wang L, Zomaya AY (2018) GA-ETI: an enhanced genetic algorithm for the scheduling of scientific workflows in cloud environments. J Comput Sci 26:318–331
Zhou J, Dong S (2018) Hybrid glowworm swarm optimization for task scheduling in the cloud environment. Eng Optim 50:949 964.
Nanjappan M, Natesan G, Krishnadoss P (2021) An adaptive neuro-fuzzy inference system and black widow optimization approach for optimal resource utilization and task scheduling in a cloud environment. Wireless Pers Commun 121(3):1891–1916
Zhou T, Tang D, Zhu H, Zhang Z (2021) Multi-agent reinforcement learning for online scheduling in smart factories. Robot Comput Integrat Manufact 72
Zade BM, Mansouri N, Javidi MM. A two-stage scheduler based on New Caledonian Crow Learning Algorithm and reinforcement learning strategy for cloud environment. J Network Comput App. 2022 202:103385.
Kaur M (2016) Elitist multi-objective bacterial foraging evolutionary algorithm for multi-criteria based grid scheduling problem. International Conference on Internet of Things and Applications (IOTA) 431–436:2016.
Ibrahim Attiya, Mohamed Abd Elaziz and Shengwu Xiong, “Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm”, Computational Intelligence and Neuroacience, Hindawi, pp-1-17, 2020.
Jignesh Lakhani and Hitesh A. Bheda, “An Approach to Optimized Resource Scheduling using Task Grouping in Cloud”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, Issue 9, pp. 594-600, 2013.
Jocksam G. de Matos, Carla K. de M. Marques and Carlos H. P. Liberalino, “Genetic and Static Algorithm for Task Scheduling in Cloud Computing”, Int. J. Cloud Computing, Vol. 8, No. 1, pp-1-19, 2019.
Kalka Dubey, Mohit Kumar and S. C. Sharma, “Modified HEFT Algorithm for Task Scheduling in Cloud Environment”, 6th International Conference on Smart Computing and Communications, ICSCC, Kurukshetra, India, Elsevier, pp-725- 732, 2017.
Kiranveer Kaur and Amritpal Kaur, “Optimal Scheduling and Load Balancing in Cloud using Enhanced Genetic Algorithm”, International Journal of Computer Applications, Volume 125, No. 11, pp-1-6, 2015.
Kvn Krishna Mohan, K Prem Sai Reddy, K Geetha Sri, A Prabhu Deva and M. Sundarababu, “Efficient Big Data Processing in Haddop Map Reduce”, Special Issue on 5th National Conference on Recent Trends in Information Technology, P.V.P. Siddhartha Institute of Technology Kanuru, Vijayawada, India, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 6, Issue 3, pp. 1-5, 2016.
Kshirsagar, P. R., Yadav, R. K., Patil, N. N., & Makarand L, M. (2022). Intrusion Detection System Attack Detection and Classification Model with Feed-Forward LSTM Gate in Conventional Dataset. Machine Learning Applications in Engineering Education and Management, 2(1), 20–29. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/21
Gangula, R. ., Vutukuru, M. M. ., & Kumar M., R. . (2023). Network Intrusion Detection Method Using Stacked BILSTM Elastic Regression Classifier with Aquila Optimizer Algorithm for Internet of Things (IoT). International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 118–131. https://doi.org/10.17762/ijritcc.v11i2s.6035
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