Monarch: Gaining Command on Geo-Distributed Graph Analytics.
Title | Monarch: Gaining Command on Geo-Distributed Graph Analytics. |
Publication Type | Conference Paper |
Year of Publication | 2018 |
Authors | Iyer, A. Pamanabha, Panda A., Chowdhury M., Akella A., Shenker S. J., & Stoica I. |
Published in | Proceedings 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. |
URL | https://www.usenix.org/system/files/conference/hotcloud18/hotcloud18-paper-iyer-monarch.pdf |
ICSI Research Group | Networking and Security |