Choreo: network-aware task placement for cloud applications
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...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Association for Computing Machinery (ACM)
2014
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| Online Access: | http://hdl.handle.net/1721.1/85949 https://orcid.org/0000-0002-6732-6799 https://orcid.org/0000-0002-1455-9652 |
| _version_ | 1826195134240260096 |
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| author | Deng, Shuo Balakrishnan, Hari LaCurts, Katrina Leigh Goyal, Ameesh K. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Deng, Shuo Balakrishnan, Hari LaCurts, Katrina Leigh Goyal, Ameesh K. |
| author_sort | Deng, Shuo |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:07:43Z |
| format | Article |
| id | mit-1721.1/85949 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:07:43Z |
| publishDate | 2014 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/859492022-09-26T15:54:04Z Choreo: network-aware task placement for cloud applications Deng, Shuo Balakrishnan, Hari LaCurts, Katrina Leigh Goyal, Ameesh K. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science LaCurts, Katrina Leigh Deng, Shuo Goyal, Ameesh K. Balakrishnan, Hari 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. National Science Foundation (U.S.) (Grant 0645960) National Science Foundation (U.S.) (Grant 1065219) National Science Foundation (U.S.) (Grant 1040072) 2014-03-28T15:17:22Z 2014-03-28T15:17:22Z 2013-10 Article http://purl.org/eprint/type/ConferencePaper 9781450319539 http://hdl.handle.net/1721.1/85949 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. https://orcid.org/0000-0002-6732-6799 https://orcid.org/0000-0002-1455-9652 en_US http://dx.doi.org/10.1145/2504730.2504744 Proceedings of the 2013 conference on Internet measurement conference (IMC '13) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) MIT web domain |
| spellingShingle | Deng, Shuo Balakrishnan, Hari LaCurts, Katrina Leigh Goyal, Ameesh K. Choreo: network-aware task placement for cloud applications |
| title | Choreo: network-aware task placement for cloud applications |
| title_full | Choreo: network-aware task placement for cloud applications |
| title_fullStr | Choreo: network-aware task placement for cloud applications |
| title_full_unstemmed | Choreo: network-aware task placement for cloud applications |
| title_short | Choreo: network-aware task placement for cloud applications |
| title_sort | choreo network aware task placement for cloud applications |
| url | http://hdl.handle.net/1721.1/85949 https://orcid.org/0000-0002-6732-6799 https://orcid.org/0000-0002-1455-9652 |
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