SCHEDULE: NOV 12-18, 2011

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Hadoop Acceleration Through Network Levitated Merge

SESSION: MapReduce

EVENT TYPE: Paper

TIME: 10:30AM - 11:00AM

AUTHOR(S):Yandong Wang, Xinyu Que, Weikuan Yu, Dror Goldenberg, Dhiraj Sehgal

ROOM:TCC 305

ABSTRACT:
Hadoop is a popular open-source implementation of the MapReduce programming model for cloud computing. However, it faces a number of issues to achieve the best performance from the underlying system. These include a serialization barrier that delays the reduce phase, repetitive merges and disk access, and lack of capability to leverage latest high speed interconnects. We describe Hadoop-A, an acceleration framework that optimizes Hadoop with plugin components implemented in C++ for fast data movement, overcoming its existing limitations. A novel network-levitated merge algorithm is introduced to merge data without repetition and disk access. In addition, a full pipeline is designed to overlap the shuffle, merge and reduce phases. Our experimental results show that Hadoop-A doubles the data processing throughput of Hadoop, and reduces CPU utilization by more than 36%.

Chair/Author Details:

Yandong Wang - Auburn University

Xinyu Que - Auburn University

Weikuan Yu - Auburn University

Dror Goldenberg - Mellanox Technologies

Dhiraj Sehgal - Mellanox Technologies

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The full paper can be found in the ACM Digital Library