Dacoop: Accelerating Data-Iterative Applications on Map/Reduce Cluster

Abstract

Map/reduce is a popular parallel processing framework for massive-scale data-intensive computing. The data-iterative application is composed of a serials of map/reduce jobs and need to repeatedly process some data files among these jobs. The existing implementation of map/reduce framework focus on perform data processing in a single pass with one map/reduce job and do not directly support the data-iterative applications, particularly in term of the explicit specification of the repeatedly processed data among jobs. In this paper, we propose an extended version of Hadoop map/reduce framework called Dacoop. Dacoop extends Map/Reduce programming interface to specify the repeatedly processed data, introduces the shared memory-based data cache mechanism to cache the data since its first access, and adopts the caching-aware task scheduling so that the cached data can be shared among the map/reduce jobs of data-iterative applications. We evaluate Dacoop on two typical data-iterative applications: k-means clustering and the domain rule reasoning in sementic web, with real and synthetic datasets. Experimental results show that the data-iterative applications can gain better performance on Dacoop than that on Hadoop. The turnaround time of a data-iterative application can be reduced by the maximum of 15.1%.