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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Main Authors: Hu, Zhiming, Qiao, Yan, Luo, Jun
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2019
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Online Access:https://hdl.handle.net/10356/89342
http://hdl.handle.net/10220/48361
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Institution: Nanyang Technological University
Language: English
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spelling 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
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Traffic Matrix
Measurements
DRNTU::Engineering::Computer science and engineering
spellingShingle 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
description 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.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Hu, Zhiming
Qiao, Yan
Luo, Jun
format Article
author Hu, Zhiming
Qiao, Yan
Luo, Jun
author_sort 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
publishDate 2019
url https://hdl.handle.net/10356/89342
http://hdl.handle.net/10220/48361
_version_ 1681047339592056832