Monarch: Gaining Command on Geo-Distributed Graph Analytics

Authors: 

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

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.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

BibTeX
@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 = {https://www.usenix.org/conference/hotcloud18/presentation/iyer-monarch},
publisher = {{USENIX} Association},
}