Analisis Performa Hadoop Multi-Node Cluster pada Skenario Pemrosesan Small Files - I Putu Ngurah Rio Aditya Pratama; Ngakan Kutha Krisnawijaya (Jurnal Teknik Informatika dan Teknologi Informasi, 2026)
The rapid growth of Big Data demands computing systems that are capable of processing data efficiently and in a distributed manner. Apache Hadoop, as a distributed computing framework, is widely used to address the limitations of centralized systems; however, it still faces challenges in handling small files, which can lead to increased file management overhead and imbalanced resource utilization. This study aims to analyze the performance of a Hadoop Multi-Node Cluster in small file processing scenarios, particularly in terms of workload distribution and CPU and memory resource utilization across nodes. The research method involves implementing a Hadoop Multi-Node Cluster in a local environment with a configuration of one master node and five worker nodes, followed by concurrent small file processing tests using the MapReduce mechanism. Performance evaluation is conducted by monitoring resource utilization through Hadoop ResourceManager, as well as Prometheus and Grafana monitoring systems to obtain real-time performance visualizations. The results of this study are expected to provide insights into the effectiveness of resource distribution in Hadoop Multi-Node Clusters for handling small files and to serve as a reference for the development and evaluation of distributed computing systems in academic and research environments.