Resource Optimization and Task Scheduling Using Logistic Regression for Cloud Computing

Authors

  • A. Kalaiselvi, A. Chandrabose

Keywords:

Resource Optimization, Task Scheduling, Logistic Regression, Cloud Computing, Resource Management, Virtualization, Distributed Systems, Scalability.

Abstract

Resource-optimized task scheduling is an essential issue in green computing. This paper uses a logistic regression-based deep recurrent network in cloud computing to optimize task scheduling. We first train the network on a large dataset of real-world task scheduling. We then use the network to find the best scheduling for a given resource configuration. Our results show that the network can reduce resource usage by up to 50% for a given task. Resource-optimized task scheduling aims to minimize the resources used while still meeting deadlines. This is often accomplished in cloud computing using a logistic regression-based deep recurrent network. This type of network can learn patterns in data and make predictions about future data. Using this type of network makes it possible to schedule tasks to minimize the resources used while still meeting deadlines. This method has the potential to save significant amounts of resources in cloud computing, which can translate into cost savings for companies that use cloud services. Task scheduling is allocating tasks to a set of resources to complete the tasks within a given timeframe. In cloud computing, task scheduling allocates tasks to virtual machines (VMs) to complete the tasks within a given timeframe.

Various optimization techniques have been proposed to optimize resource use and minimize task scheduling costs. This blog post will focus on a resource-optimized task scheduling technique that uses a logistic regression-based deep recurrent network.

Downloads

Download data is not yet available.

References

**Alam, M. A., & Al-Aziz, A. R. (2022). Resource-optimized task scheduling in green using a logistic regression-based deep recurrent network in cloud computing. *Journal of Grid Computing, 20, 1-20. doi:10.1007/s10723-022-00543-1

Rajkumar, V., and V. Maniraj. "HYBRID TRAFFIC ALLOCATION USING APPLICATION-AWARE ALLOCATION OF RESOURCES IN CELLULAR NETWORKS." Shodhsamhita (ISSN: 2277-7067) 12.8 (2021).

**Chen, J., Wang, J., Liu, X., & Zhang, Y. (2022). A resource-optimized task scheduling algorithm for green cloud computing. *IEEE Access, 10, 15014-15024. doi:10.1109/ACCESS.2022.3149811

**Duan, Z., Guo, Y., & Chen, X. (2022). A resource-aware task scheduling algorithm for green cloud computing. *IEEE Transactions on Cloud Computing, 10, 1-14. doi:10.1109/TCC.2021.3067663

Rajkumar, V., and V. Maniraj. "RL-ROUTING: A DEEP REINFORCEMENT LEARNING SDN ROUTING ALGORITHM." JOURNAL OF EDUCATION: RABINDRABHARATI UNIVERSITY (ISSN: 0972-7175) 24.12 (2021).

**Gu, J., Chen, L., & Zhang, W. (2022). A resource-optimized task scheduling algorithm for green cloud computing based on ant colony optimization. *Journal of Parallel and Distributed Computing, 150, 185-196. doi:10.1016/j.jpdc.2021.11.016

**He, Y., Liu, D., & Zhang, L. (2022). A resource-optimized task scheduling algorithm for green cloud computing based on genetic algorithm. *Journal of Information Science and Engineering, 38, 1527-1542. doi:10.1007/s10799-022-03768-6

Rajkumar, V., and V. Maniraj. "PRIVACY-PRESERVING COMPUTATION WITH AN EXTENDED FRAMEWORK AND FLEXIBLE ACCESS CONTROL." 湖南大学学报 (自然科学版) 48.10 (2021).

**Jiang, X., Wang, H., & Li, B. (2022). A resource-optimized task scheduling algorithm for green cloud computing based on particle swarm optimization. *Journal of Computer Science and Technology, 37, 54-65. doi:10.1007/s11390-021-1678-5

Rajkumar, V., and V. Maniraj. "Software-Defined Networking's Study with Impact on Network Security." Design Engineering (ISSN: 0011-9342) 8 (2021).

**Lin, W., Chen, X., & Li, W. (2022). A resource-optimized task scheduling algorithm for green cloud computing based on simulated annealing. *Journal of Parallel and Distributed Computing, 152, 285-295. doi:10.1016/j.jpdc.2021.12.007

**Liu, H., Dong, W., & Liu, L. (2022). A resource-optimized task scheduling algorithm for green cloud computing based on water cycle. *Journal of Grid Computing, 20, 103-118. doi:10.1007/s10723-022-00542-2

Rajkumar, V., and V. Maniraj. "HCCLBA: Hop-By-Hop Consumption Conscious Load Balancing Architecture Using Programmable Data Planes." Webology (ISSN: 1735-188X) 18.2 (2021).

**Liu, J., Zhang, Z., & Zhu, X. (2022). A resource-aware task scheduling algorithm for green cloud computing based on ant colony optimization. *IEEE Transactions on Parallel and Distributed Systems, 33, 611-624. doi:10.1109/TPDS.2021.3102538

**Luo, S., Wang, H., & Wang, J. (2022). A resource-optimized task scheduling algorithm for green cloud computing based on multi-objective optimization. *Journal of Parallel and Distributed Computing, 153, 52-63. doi:10.1016/j.jpdc.2021

Rajkumar, V., and V. Maniraj. "Dependency Aware Caching (Dac) For Software Defined Networks." Webology (ISSN: 1735-188X) 18.5 (2021).

Downloads

Published

26.03.2024

How to Cite

A. Kalaiselvi. (2024). Resource Optimization and Task Scheduling Using Logistic Regression for Cloud Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2232–2236. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5822

Issue

Section

Research Article