Optimizing Cloud Resource Allocation and Load Balancing through Eco-Efficient Task Scheduling

Authors

  • S. Amutha Associate Professor, Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai- 600062, India
  • P. Kamarajapandian Assistant Professor, Department of Computer Science and Engineering, Kommuri Pratap Reddy Institute of Technology, Hyderabad, Telangana-501301,India.
  • J. Nirmaladevi Professor, Department of Computer Science and Engineering, Bannari Amman Institute of Technology,Sathyamangalam-637 408. Tamil Nadu,India
  • S. Saravanan Associate Professor, Department of AI&DS, Karpagam academy of higher education Faculty of engineering, Coimbatore, Tamil Nadu- 641021
  • S. Vijayalakshmi Professor, Department of Electronics and Communication Engineering, R.M.K.. Engineering College , Kavaraipettai, Tamil Nadu 601 206, India.
  • S. Athimoolam Assistant Professor, Department of Electronics and Communication Enginering ,PSR Engineering College, Sevalpatti, Sivskasi- 626140. Tamil Nadu

Keywords:

Cloud computing, EcoShed, Task Scheduling, Resource Allocation, Load Balancing, Dynamic Task Scheduler, Computational Efficiency

Abstract

Cloud computing is a rapidly evolving field that requires efficient resource allocation and fair distribution of tasks to achieve optimal performance and cost-effectiveness. To address these concerns, this study explores EcoSched, a pioneering study in the realm of cloud computing, aiming to revolutionize resource allocation and task scheduling methodologies for enhanced efficiency and sustainability. In response to the evolving demands of this field, this research investigates dynamic task scheduling methods tailored to optimize resource utilization and task distribution in cloud environments. This innovative framework emphasizes the eco-efficient assignment of tasks by categorizing them based on computational intensity, interdependencies, and stringent deadlines. Employing a refined task assignment mechanism supported by a sophisticated dynamic task scheduler, tasks are intelligently allocated to suitable virtual machines in real-time. Moreover, the integration of heuristic and predictive analysis enhances the decision-making process within the scheduler, ensuring optimal task placement. In parallel, EcoSched incorporates a robust load balancer capable of dynamically adjusting task allocations across the cloud infrastructure. By proactively mitigating resource bottlenecks and minimizing response times, this load balancer significantly enhances system performance. The proposed methodology showcases remarkable improvements in response time and resource utilization metrics, surpassing conventional scheduling approaches. This research offers valuable insights into the scalability and adaptability of the introduced techniques, laying the groundwork for future advancements in dynamic task scheduling strategies. With a focus on optimizing resource allocation and load balancing, this study contributes to the evolution of resilient, efficient, and sustainable cloud environments. EcoSched sets the stage for meeting the escalating computational demands while promoting eco-efficiency, thus shaping the future landscape of cloud computing.

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Published

11.01.2024

How to Cite

Amutha, S. ., Kamarajapandian, P. ., Nirmaladevi, J. ., Saravanan, S. ., Vijayalakshmi, S. ., & Athimoolam, S. . (2024). Optimizing Cloud Resource Allocation and Load Balancing through Eco-Efficient Task Scheduling. International Journal of Intelligent Systems and Applications in Engineering, 12(11s), 137–143. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4430

Issue

Vol. 12 No. 11s (2024)

Section

Research Article

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