Monarch: Gaining Command on Geo-Distributed Graph Analytics


Anand Padmanabha Iyer, UC Berkeley; Aurojit Panda, NYU; Mosharaf Chowdhury, University of Michigan; Aditya Akella, University of Wisconsin; Scott Shenker and Ion Stoica, UC Berkeley


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.

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@inproceedings {216859,
author = {Anand Padmanabha Iyer and Aurojit Panda and Mosharaf Chowdhury and Aditya Akella and Scott Shenker and Ion Stoica},
title = {Monarch: Gaining Command on Geo-Distributed Graph Analytics},
booktitle = {10th {USENIX} Workshop on Hot Topics in Cloud Computing (HotCloud 18)},
year = {2018},
address = {Boston, MA},
url = {},
publisher = {{USENIX} Association},