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dc.contributor.authorKhayyat, Zuhair
dc.contributor.authorAwara, Karim
dc.contributor.authorAlOnazi, Amani
dc.contributor.authorJamjoom, Hani T.
dc.contributor.authorWilliams, Daniel W.
dc.contributor.authorKalnis, Panos
dc.date.accessioned2015-08-04T07:10:51Z
dc.date.available2015-08-04T07:10:51Z
dc.date.issued2013
dc.identifier.citationKhayyat, Z., Awara, K., Alonazi, A., Jamjoom, H., Williams, D., & Kalnis, P. (2013). Mizan. Proceedings of the 8th ACM European Conference on Computer Systems - EuroSys ’13. doi:10.1145/2465351.2465369
dc.identifier.isbn9781450319942
dc.identifier.doi10.1145/2465351.2465369
dc.identifier.urihttp://hdl.handle.net/10754/564648
dc.description.abstractPregel [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. © 2013 ACM.
dc.publisherAssociation for Computing Machinery (ACM)
dc.titleMizan: A system for dynamic load balancing in large-scale graph processing
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalProceedings of the 8th ACM European Conference on Computer Systems - EuroSys '13
dc.conference.date15 April 2013 through 17 April 2013
dc.conference.name8th ACM European Conference on Computer Systems, EuroSys 2013
dc.conference.locationPrague
dc.contributor.institutionIBM T. J. Watson Research Center, Yorktown Heights, NY, United States
kaust.personAlOnazi, Amani A.
kaust.personKalnis, Panos
kaust.personKhayyat, Zuhair
kaust.personAwara, Karim


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