Task Scheduling Optimization in Cloud Environments

Author(s)	Chandragiri Sai Saketh, Daddala Pavan, Chukka Bhuvana Bharath, Seelam Bhanu Kiran, I. Shalini
Country	India
Abstract	Cloud computing systems depend on effective task scheduling to achieve better performance and efficient use of resources. This study presents a Generalized Priority (GP) scheduling approach designed to improve task execution in cloud environments. The proposed method is evaluated against conventional scheduling techniques such as First Come First Serve (FCFS) and Round Robin (RR). By using queue-based workload analysis, the GP algorithm directs incoming tasks to the least loaded queue, which helps reduce waiting time, response time, and average queue length. In addition, a Skewness-Avoidance Multi-Resource Allocation (SAMR) mechanism is introduced to distribute diverse resources more effectively and maintain balance across physical machines. Simulation and experimental findings indicate that the proposed framework enhances scheduler efficiency and significantly lowers waiting time compared to existing methods. Future improvements may include support for larger workloads and the integration of parallel cloud algorithms to further optimize execution speed.
Keywords	Cloud computing, Skewness-Avoidance Multi-Resource Allocation (SAMR), First Come First Serve (FCFS), Round Robin (RR).
Field	Engineering
Published In	Volume 17, Issue 2, April-June 2026
Published On	2026-04-11

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