TY - CONF
T1 - MONARCH
T2 - 10th USENIX Workshop on Hot Topics in Cloud Computing, HotCloud 2018
AU - Iyer, Anand Padmanabha
AU - Panda, Aurojit
AU - Chowdhury, Mosharaf
AU - Akella, Aditya
AU - Shenker, Scott
AU - Stoica, Ion
N1 - Funding Information:
Acknowledgments 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.
Publisher Copyright:
© 2018 USENIX Association. All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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 geo-distributed graph processing. 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.
AB - 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 geo-distributed graph processing. 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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M3 - Paper
AN - SCOPUS:85084162615
Y2 - 9 July 2018
ER -