Mizan: a system for dynamic load balancing in large-scale graph processing

  • Authors:

  • Zuhair Khayyat;Karim Awara;Amani Alonazi;Hani Jamjoom;Dan Williams;Panos Kalnis

  • Affiliations:

  • King Abdullah University of Science and Technology, Saudi Arabia;King Abdullah University of Science and Technology, Saudi Arabia;King Abdullah University of Science and Technology, Saudi Arabia;IBM T. J. Watson Research Center, Yorktown Heights, NY;IBM T. J. Watson Research Center, Yorktown Heights, NY;King Abdullah University of Science and Technology, Saudi Arabia

  • Venue:
  • Proceedings of the 8th ACM European Conference on Computer Systems
  • Year:
  • 2013

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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.