Optimizing Resource Allocation in Fair Scheduler: A Simulation-Based Approach

Abstract

The effective deployment of computational resources is essential for data processing in the big data era. By ensuring equitable resource distribution, fair scheduling is essential in striking this state of balance. In the context of large data processing, this essay examines the mathematical foundations and practical application of fair scheduling. We explore the intricate details of choosing tools, creating strategy, and addressing practical difficulties. We illustrate the actual impact of fair scheduling and highlight its significance in improving efficiency and resource utilisation in big data processing systems through empirical evaluation and real-world use cases. Our primary focus revolves around the practical implementation of fair scheduling within a big data framework. We go into much detail about the choice of suitable tools and technologies, examine the architectural details of the selected framework, and go over the creation and use of fair scheduling principles and algorithms. Throughout the paper, we offer valuable insights into the challenges faced during implementation and the innovative solutions devised to overcome them.