Monarch: Gaining Command on Geo-Distributed Graph Analytics.

TitleMonarch: Gaining Command on Geo-Distributed Graph Analytics.
Publication TypeConference Paper
Year of Publication2018
AuthorsIyer, A. Pamanabha, Panda A., Chowdhury M., Akella A., Shenker S., & Stoica I.
Published inProceedings of HotCloud 2018
Abstract

A number of existing and emerging application scenarios generate graph-structured data in a geo-distributed fashion. Although there is a lot of interest in distributed graph processing systems, none of them support graphs that are geo-distributed. Geo-distributed analytics, on the other hand, has not focused on iterative workloads such as distributed graph processing.

In this paper, we look at the problem of efficient geo-distributed graph analytics. We find that optimizing the iterative processing style of graph-parallel systems is the key to achieving this goal rather than extending existing geo-distributed techniques to graph processing. Based on this, we discuss our proposal on building Monarch, the first system to our knowledge that focuses on geo-distributed graph processing. Our preliminary evaluation of Monarch shows encouraging results.

Acknowledgment

We would like to thank the reviewers for their valuable feedback. In addition to NSF CISE Expeditions Award CCF-1730628, this research is supported in part by DHS Award HSHQDC-16-3-00083, and gifts from Alibaba, Amazon Web Services, Ant Financial, CapitalOne, Ericsson, Facebook, Google, Huawei, Intel, Microsoft, Scotiabank, Splunk and VMware.

URLhttps://www.usenix.org/system/files/conference/hotcloud18/hotcloud18-paper-iyer-monarch.pdf
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