Abstract
Cloud computing has become more popular, and it has been continuously developed in architecture, software, and network. Hadoop-MapReduce is a common software framework processing parallelizable problem across big datasets using a distributed cluster. Cloud Hadoop-MapReduce can scale incrementally in the number of processing nodes. Hence, the Hadoop-MapReduce is designed to provide a processing platform with powerful computation. Network traffic is always a most important bottleneck in data-intensive computing and network latency decreases significant performance in data parallel systems. Network bottleneck is caused by network bandwidth and the network speed is much slower than disk data access. So that, good data locality can reduces network traffic and increases performance in data-intensive HPC systems. However, Hadoop’s scheduler has a defect of data locality in resource assignment. This paper includes a locality-aware scheduling algorithm for Hadoop-MapReduce scheduler. Firstly, we propose a mathematical model of weight of data interference in Hadoop scheduler. Secondly, we present the algorithm to use weight of data interference to provide data locality-aware resource assignment in Hadoop scheduler. Finally, we build an experimental environment with 3 physical machines which were installed Xen Cloud Platform and 2 virtual machines which are installed hadoop on each physical machine. Then, run simulation to verify the performance of locality-aware scheduling algorithm for Hadoop-MapReduce scheduler.