Distributed Resource Management in Operating Systems: A Case Study on HDFS and YARN

This research study focuses on analysing the role of distributed resource management in enhancing the scalability and reliability of the linked systems. This study presents a detailed analysis on the architectures, benefits, and inherent drawbacks of the Hadoop Distributed File System (HDFS) and Yet Another Resource Negotiator (YARN). YARN offers flexible resource scheduling through Fair and Capacity schedulers, while HDFS offers fault-tolerant, scalable storage through a block-based, replicated, and locality-optimized design. Although robust, limitations like resource contention in YARN and the Name Node's single point of failure in HDFS still exist. In order to address the evolving challenges in modern computing, this study also explores the potential research domains like serverless architecture for dynamic scaling, latency-conscious edge computing, and AI-based resource forecasting.