Mizan: A system for dynamic load balancing in large-scale graph processing
AlOnazi, Amani A.
Jamjoom, Hani T.
Williams, Daniel W.
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
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AbstractPregel  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. © 2013 ACM.
Conference/Event name8th ACM European Conference on Computer Systems, EuroSys 2013