Performance Analysis of Vertex-centric Graph Algorithms on the Azure
Cloud Platform
M. Redekopp, Y. Simmhan, и V. Prasanna. Workshop on Parallel Algorithms and Software for Analysis of Massive
Graphs (ParGraph), стр. 1--8. (2011)
Аннотация
Finding key vertices in large graphs is an important problem in many
applications such as social networks, bioinformatics, and distribution
networks. Betweenness centrality is a popular algorithm for finding
such vertices and has been studied extensively, yielding several
parallel formulations suitable to supercomputers and clusters. In
this paper we implement and study betweenness centrality in the context
of cloud-based platforms using Microsoft Windows Azure as our case
study. We demonstrate scalable parallel performance and investigate
key issues related to a cloud-based implementation including mitigating
penalties associated with VM failures as well as the impact of communication
overheads in the cloud. We use a combination of empirical and analytical
evaluation using both synthetic small-world and real-world social
interaction graphs.
%0 Conference Paper
%1 Redekopp:pargraph:2011
%A Redekopp, Mark
%A Simmhan, Yogesh
%A Prasanna, Viktor K.
%B Workshop on Parallel Algorithms and Software for Analysis of Massive
Graphs (ParGraph)
%D 2011
%K Azure, Cloud, USC graphs, peer reviewed,
%P 1--8
%T Performance Analysis of Vertex-centric Graph Algorithms on the Azure
Cloud Platform
%U http://ceng.usc.edu/~simmhan/pubs/redekopp-pargraph-2011.pdf
%X Finding key vertices in large graphs is an important problem in many
applications such as social networks, bioinformatics, and distribution
networks. Betweenness centrality is a popular algorithm for finding
such vertices and has been studied extensively, yielding several
parallel formulations suitable to supercomputers and clusters. In
this paper we implement and study betweenness centrality in the context
of cloud-based platforms using Microsoft Windows Azure as our case
study. We demonstrate scalable parallel performance and investigate
key issues related to a cloud-based implementation including mitigating
penalties associated with VM failures as well as the impact of communication
overheads in the cloud. We use a combination of empirical and analytical
evaluation using both synthetic small-world and real-world social
interaction graphs.
@inproceedings{Redekopp:pargraph:2011,
abstract = {Finding key vertices in large graphs is an important problem in many
applications such as social networks, bioinformatics, and distribution
networks. Betweenness centrality is a popular algorithm for finding
such vertices and has been studied extensively, yielding several
parallel formulations suitable to supercomputers and clusters. In
this paper we implement and study betweenness centrality in the context
of cloud-based platforms using Microsoft Windows Azure as our case
study. We demonstrate scalable parallel performance and investigate
key issues related to a cloud-based implementation including mitigating
penalties associated with VM failures as well as the impact of communication
overheads in the cloud. We use a combination of empirical and analytical
evaluation using both synthetic small-world and real-world social
interaction graphs.},
added-at = {2014-08-13T04:08:36.000+0200},
author = {Redekopp, Mark and Simmhan, Yogesh and Prasanna, Viktor K.},
biburl = {https://www.bibsonomy.org/bibtex/27e8045daf64e809a4312c32797df9573/simmhan},
booktitle = {Workshop on Parallel Algorithms and Software for Analysis of Massive
Graphs (ParGraph)},
interhash = {cfdb15dd3d2d37baccee22d9f0b3839c},
intrahash = {7e8045daf64e809a4312c32797df9573},
keywords = {Azure, Cloud, USC graphs, peer reviewed,},
owner = {Simmhan},
pages = {1--8},
timestamp = {2014-08-13T04:08:36.000+0200},
title = {Performance Analysis of Vertex-centric Graph Algorithms on the Azure
Cloud Platform},
url = {http://ceng.usc.edu/~simmhan/pubs/redekopp-pargraph-2011.pdf},
year = 2011
}