ATME : accurate traffic matrix estimation in both public and private datacenter networks
Understanding the pattern of end-to-end traffic flows in datacenter networks (DCNs) is essential to many DCN designs and operations (e.g., traffic engineering and load balancing). However, little research work has been done to obtain traffic information efficiently and yet accurately. Researchers of...
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sg-ntu-dr.10356-893422020-03-07T11:49:00Z ATME : accurate traffic matrix estimation in both public and private datacenter networks Hu, Zhiming Qiao, Yan Luo, Jun School of Computer Science and Engineering Traffic Matrix Measurements DRNTU::Engineering::Computer science and engineering Understanding the pattern of end-to-end traffic flows in datacenter networks (DCNs) is essential to many DCN designs and operations (e.g., traffic engineering and load balancing). However, little research work has been done to obtain traffic information efficiently and yet accurately. Researchers often assume the availability of traffic tracing tools (e.g., OpenFlow) when their proposals require traffic information as input, but these tools may have high monitoring overhead and consume significant switch resources even if they are available in a DCN. Although estimating the traffic matrix (TM) between origin-destination pairs using only basic switch SNMP counters is a mature practice in IP networks, traffic flows in DCNs show totally different characteristics, while the large number of redundant routes in a DCN further complicates the situation. To this end, we propose to utilize resource provisioning information in public cloud datacenters and the service placement information in private datacenters for deducing the correlations among top-of-rack switches, and to leverage the uneven traffic distribution in DCNs for reducing the number of routes potentially used by a flow. These allow us to develop ATME as an efficient TM estimation scheme that achieves high accuracy for both public and private DCNs. We compare our two algorithms with two existing representative methods through both experiments and simulations; the results strongly confirm the promising performance of our algorithms. Accepted version 2019-05-24T06:35:35Z 2019-12-06T17:23:21Z 2019-05-24T06:35:35Z 2019-12-06T17:23:21Z 2015 Journal Article Hu, Z., Qiao, Y., & Luo, J. (2018). ATME : accurate traffic matrix estimation in both public and private datacenter networks. IEEE Transactions on Cloud Computing, 6(1), 60-73. doi:10.1109/TCC.2015.2481383 https://hdl.handle.net/10356/89342 http://hdl.handle.net/10220/48361 10.1109/TCC.2015.2481383 en IEEE Transactions on Cloud Computing © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCC.2015.2481383. 14 p. application/pdf |
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Traffic Matrix Measurements DRNTU::Engineering::Computer science and engineering Hu, Zhiming Qiao, Yan Luo, Jun ATME : accurate traffic matrix estimation in both public and private datacenter networks |
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Understanding the pattern of end-to-end traffic flows in datacenter networks (DCNs) is essential to many DCN designs and operations (e.g., traffic engineering and load balancing). However, little research work has been done to obtain traffic information efficiently and yet accurately. Researchers often assume the availability of traffic tracing tools (e.g., OpenFlow) when their proposals require traffic information as input, but these tools may have high monitoring overhead and consume significant switch resources even if they are available in a DCN. Although estimating the traffic matrix (TM) between origin-destination pairs using only basic switch SNMP counters is a mature practice in IP networks, traffic flows in DCNs show totally different characteristics, while the large number of redundant routes in a DCN further complicates the situation. To this end, we propose to utilize resource provisioning information in public cloud datacenters and the service placement information in private datacenters for deducing the correlations among top-of-rack switches, and to leverage the uneven traffic distribution in DCNs for reducing the number of routes potentially used by a flow. These allow us to develop ATME as an efficient TM estimation scheme that achieves high accuracy for both public and private DCNs. We compare our two algorithms with two existing representative methods through both experiments and simulations; the results strongly confirm the promising performance of our algorithms. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Hu, Zhiming Qiao, Yan Luo, Jun |
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Article |
author |
Hu, Zhiming Qiao, Yan Luo, Jun |
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Hu, Zhiming |
title |
ATME : accurate traffic matrix estimation in both public and private datacenter networks |
title_short |
ATME : accurate traffic matrix estimation in both public and private datacenter networks |
title_full |
ATME : accurate traffic matrix estimation in both public and private datacenter networks |
title_fullStr |
ATME : accurate traffic matrix estimation in both public and private datacenter networks |
title_full_unstemmed |
ATME : accurate traffic matrix estimation in both public and private datacenter networks |
title_sort |
atme : accurate traffic matrix estimation in both public and private datacenter networks |
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2019 |
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https://hdl.handle.net/10356/89342 http://hdl.handle.net/10220/48361 |
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1681047339592056832 |