Cicada: Predictive Guarantees for Cloud Network Bandwidth
In cloud-computing systems, network-bandwidth guarantees have been shown to improve predictability of application performance and cost. Most previous work on cloud-bandwidth guarantees has assumed that cloud tenants know what bandwidth guarantees they want. However, application bandwidth demands can...
| Main Authors: | , , , |
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2014
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| Online Access: | http://hdl.handle.net/1721.1/85975 |
| _version_ | 1826215176877113344 |
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| author | LaCurts, Katrina Mogul, Jeffrey C. Balakrishnan, Hari Turner, Yoshio |
| author2 | Hari Balakrishnan |
| author_facet | Hari Balakrishnan LaCurts, Katrina Mogul, Jeffrey C. Balakrishnan, Hari Turner, Yoshio |
| author_sort | LaCurts, Katrina |
| collection | MIT |
| description | In cloud-computing systems, network-bandwidth guarantees have been shown to improve predictability of application performance and cost. Most previous work on cloud-bandwidth guarantees has assumed that cloud tenants know what bandwidth guarantees they want. However, application bandwidth demands can be complex and time-varying, and many tenants might lack sufficient information to request a bandwidth guarantee that is well-matched to their needs. A tenant's lack of accurate knowledge about its future bandwidth demands can lead to over-provisioning (and thus reduced cost-efficiency) or under-provisioning (and thus poor user experience in latency-sensitive user-facing applications). We analyze traffic traces gathered over six months from an HP Cloud Services datacenter, finding that application bandwidth consumption is both time-varying and spatially inhomogeneous. This variability makes it hard to predict requirements. To solve this problem, we develop a prediction algorithm usable by a cloud provider to suggest an appropriate bandwidth guarantee to a tenant. The key idea in the prediction algorithm is to treat a set of previously observed traffic matrices as "experts" and learn online the best weighted linear combination of these experts to make its prediction. With tenant VM placement using these predictive guarantees, we find that the inter-rack network utilization in certain datacenter topologies can be more than doubled. |
| first_indexed | 2024-09-23T16:18:00Z |
| id | mit-1721.1/85975 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:18:00Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | mit-1721.1/859752019-04-11T07:29:47Z Cicada: Predictive Guarantees for Cloud Network Bandwidth LaCurts, Katrina Mogul, Jeffrey C. Balakrishnan, Hari Turner, Yoshio Hari Balakrishnan Networks & Mobile Systems networking machine learning traffic prediction In cloud-computing systems, network-bandwidth guarantees have been shown to improve predictability of application performance and cost. Most previous work on cloud-bandwidth guarantees has assumed that cloud tenants know what bandwidth guarantees they want. However, application bandwidth demands can be complex and time-varying, and many tenants might lack sufficient information to request a bandwidth guarantee that is well-matched to their needs. A tenant's lack of accurate knowledge about its future bandwidth demands can lead to over-provisioning (and thus reduced cost-efficiency) or under-provisioning (and thus poor user experience in latency-sensitive user-facing applications). We analyze traffic traces gathered over six months from an HP Cloud Services datacenter, finding that application bandwidth consumption is both time-varying and spatially inhomogeneous. This variability makes it hard to predict requirements. To solve this problem, we develop a prediction algorithm usable by a cloud provider to suggest an appropriate bandwidth guarantee to a tenant. The key idea in the prediction algorithm is to treat a set of previously observed traffic matrices as "experts" and learn online the best weighted linear combination of these experts to make its prediction. With tenant VM placement using these predictive guarantees, we find that the inter-rack network utilization in certain datacenter topologies can be more than doubled. 2014-03-31T20:15:06Z 2014-03-31T20:15:06Z 2014-03-24 2014-03-31T20:15:06Z http://hdl.handle.net/1721.1/85975 MIT-CSAIL-TR-2014-004 Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ 13 p. application/pdf |
| spellingShingle | networking machine learning traffic prediction LaCurts, Katrina Mogul, Jeffrey C. Balakrishnan, Hari Turner, Yoshio Cicada: Predictive Guarantees for Cloud Network Bandwidth |
| title | Cicada: Predictive Guarantees for Cloud Network Bandwidth |
| title_full | Cicada: Predictive Guarantees for Cloud Network Bandwidth |
| title_fullStr | Cicada: Predictive Guarantees for Cloud Network Bandwidth |
| title_full_unstemmed | Cicada: Predictive Guarantees for Cloud Network Bandwidth |
| title_short | Cicada: Predictive Guarantees for Cloud Network Bandwidth |
| title_sort | cicada predictive guarantees for cloud network bandwidth |
| topic | networking machine learning traffic prediction |
| url | http://hdl.handle.net/1721.1/85975 |
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