Monarch: Gaining Command on Geo-Distributed Graph Analytics

Anand Padmanabha Iyer

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.

Published On: July 9, 2018

Presented At/In: 10th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud '18)

Link: https://www.usenix.org/conference/hotcloud18/presentation/iyer-monarch

Authors: Anand Padmanabha Iyer, Aurojit Panda, Mosharaf Chowdhury, Aditya Akella, Scott Shenker, Ion Stoica