Abstract

Large companies like Facebook, Google, and Microsoft as well as a number of small and medium enterprises daily process massive amounts of data in batch jobs and in real time applications. This generates high network traffic, which is hard to support using traditional, oversubscribed, network infrastructures. To address this issue, several novel network topologies have been proposed, aiming at increasing the bandwidth available in enterprise clusters. Hadoop faces a number of issues to achieve the best performance from the underlying systems. These include a serialization barrier that delays the reduce phase, and the lack of portability to different interconnects. To keep up with the increasing volume of data sets, Hadoop also requires efficient I/O capability from the underlying computer systems to process and analyze data. We describe Hadoop-A, an acceleration framework that optimizes Hadoop with plug-in components for fast data movement. A novel network-levitated merge algorithm is introduced to merge data without repetition and disk access Our experimental results show that Hadoop-A significantly speeds up data movement in MapReduce and doubles the throughput of Hadoop.

Authors and Affiliations