Résumé

Apache Hadoop is one of the most popular distributed computing systems, used largely for big data analysis and processing. The Hadoop cluster hosts multiple parallel workloads requiring various resource usage (CPU, RAM, etc.). In practice, in heterogeneous Hadoop environments, resource-intensive tasks may be allocated to the lower performing nodes, causing load imbalance between and within clusters and and high data transfer cost. These weaknesses lead to performance deterioration of the Hadoop system and delays the completion of all submitted jobs. To overcome these challenges, this paper proposes an efficient and dynamic load balancing policy in a heterogeneous Hadoop YARN cluster. This novel load balancing model is based on clustering nodes into subgroups of nodes similar in performance, and then allocating different jobs in these subgroups using a multi-criteria ranking. This policy ensures the most accurate match between resource demands and available resources in real time, which decreases the data transfer in the cluster. The experimental results show that the introduced approach allows reducing noticeably the completion time s by 42% and 11% compared with the H-fair and a load balancing approach respectively. Thus, Hadoop can rapidly release the resources for the next job which enhance the overall performance of the distributed computing systems. The obtained finding also reveal that our approach optimizes the use of the available resources and avoids cluster over-load in real time.