LoadAtomizer: A locality and I/O load aware task scheduler for MapReduce

Data-intensive computing systems like MapReduce and Dryad have emerged as a framework for leveraging computing resources of a cluster. I/O bottlenecks need to be eased to improve performance in data-intensive computing systems. State-of-the-art frameworks for data-intensive computing have tackled the issue with a data locality based task scheduling policy. However, locality-aware scheduling does not always work good to mitigate I/O bottlenecks when different I/O characteristic jobs run concurrently. This paper presents LoadAtomizer, a locality and I/O load aware task scheduler for MapReduce. LoadAtomizer mitigates the I/O bottlenecks of a cluster with locality and I/O load aware map task assignment and storage selection. LoadAtomizer quickly assigns a slave a map task whose input data is stored in a lightly loaded storage and commands the slave to read the input data from the storage. LoadAtomizer maintains the load information of storages and the network with a topology-aware load tree. A topology-aware load tree enables LoadAtomizer to select quickly a lightly loaded storage that a slave can access through a lightly loaded network path. Experimental results demonstrated that our prototype of LoadAtomizer shortened completion time of multiple jobs by up to 18.6 %.