Analyzing Task Scheduling Algorithms in Cloud Computing for Optimal Performance

Abstract

Cloud computing has gained significant traction due to its ability to provide scalable, on-demand resources, facilitating efficient data processing and management. A crucial component of cloud performance is task scheduling, which determines how tasks are allocated to remote servers for execution. This paper focuses on analyzing and implementing three widely used task scheduling algorithms: First-Come, First-Served (FCFS), Shortest Job First (SJF), and Round Robin (RR). The study aims to evaluate how these algorithms influence cloud system performance, particularly in terms of task execution efficiency, resource utilization, and system throughput. By simulating various cloud environments and workload scenarios, the paper assesses each algorithm's strengths and limitations. The analysis highlights how the selection of scheduling algorithms directly impacts cloud performance, emphasizing the need for optimized task allocation strategies to ensure better system resource management. The findings demonstrate that while each algorithm has its advantages in specific contexts, effective scheduling is crucial for maintaining overall system stability and maximizing resource utilization. The paper concludes with recommendations for selecting the most appropriate algorithm based on workload characteristics and desired performance outcomes, offering valuable insights into improving cloud computing efficiency.