Choreo: network-aware task placement for cloud applications
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Balakrishnan_Choreo.pdf
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Author(s)
Deng, Shuo
Balakrishnan, Hari
LaCurts, Katrina Leigh
Goyal, Ameesh K.
Date Issued
October 2013
Journal
Proceedings of the 2013 conference on Internet measurement conference (IMC '13)
Publisher
Association for Computing Machinery (ACM)
Citation
Katrina LaCurts, Shuo Deng, Ameesh Goyal, and Hari Balakrishnan. 2013. Choreo: network-aware task placement for cloud applications. In Proceedings of the 2013 conference on Internet measurement conference (IMC '13). ACM, New York, NY, USA, 191-204.
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Author's final manuscript
Abstract
Cloud computing infrastructures are increasingly being used by network-intensive applications that transfer significant amounts of data between the nodes on which they run. This paper shows that tenants can do a better job placing applications by understanding the underlying cloud network as well as the demands of the applications. To do so, tenants must be able to quickly and accurately measure the cloud network and profile their applications, and then use a network-aware placement method to place applications. This paper describes Choreo, a system that solves these problems. Our experiments measure Amazon's EC2 and Rackspace networks and use three weeks of network data from applications running on the HP Cloud network. We find that Choreo reduces application completion time by an average of 8%-14% (max improvement: 61%) when applications are placed all at once, and 22%-43% (max improvement: 79%) when they arrive in real-time, compared to alternative placement schemes.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/2504730.2504744
