Dynamic Replication Policy on HDFS Based on Machine Learning Clustering

Data growth in recent years has been swift, leading to the emergence of big data science. Distributed File Systems (DFS) are commonly used handle data, like Google System (GFS), Hadoop (HDFS), and others. The DFS should provide availability reliability system case failure. replicates files different locations reliability. These replications consume storage space other resources. importance these differs depending on how frequently they system. So some do not deserve replicate many times because it is unimportant This paper introduces a Dynamic Replication Policy using Machine Learning Clustering (DRPMLC) HDFS, which uses cluster into groups apply replication policies each group reduce consumption, improve read write operations time keep HDFS as High-Performance Computing (HPDC).