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| Content Provider | ACM Digital Library |
|---|---|
| Author | Awara, Karim Jamjoom, Hani Alonazi, Amani Williams, Dan Kalnis, Panos Khayyat, Zuhair |
| Abstract | Pregel [23] was recently introduced as a scalable graph mining system that can provide significant performance improvements over traditional MapReduce implementations. Existing implementations focus primarily on graph partitioning as a preprocessing step to balance computation across compute nodes. In this paper, we examine the runtime characteristics of a Pregel system. We show that graph partitioning alone is insufficient for minimizing end-to-end computation. Especially where data is very large or the runtime behavior of the algorithm is unknown, an adaptive approach is needed. To this end, we introduce Mizan, a Pregel system that achieves efficient load balancing to better adapt to changes in computing needs. Unlike known implementations of Pregel, Mizan does not assume any a priori knowledge of the structure of the graph or behavior of the algorithm. Instead, it monitors the runtime characteristics of the system. Mizan then performs efficient fine-grained vertex migration to balance computation and communication. We have fully implemented Mizan; using extensive evaluation we show that---especially for highly-dynamic workloads---Mizan provides up to 84% improvement over techniques leveraging static graph pre-partitioning. |
| Starting Page | 169 |
| Ending Page | 182 |
| Page Count | 14 |
| File Format | |
| ISBN | 9781450319942 |
| DOI | 10.1145/2465351.2465369 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-04-15 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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