Coarse-grained traffic matrix estimation for data center networks
We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) swit...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hu, Zhiming [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015transfer abstract |
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| Schlagwörter: |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data - Chown, Justin ELSEVIER, 2016, the international journal for the computer and telecommunications industry, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:56 ; year:2015 ; day:1 ; month:02 ; pages:25-34 ; extent:10 |
| Links: |
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| DOI / URN: |
10.1016/j.comcom.2014.02.016 |
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ELV039579018 |
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| 520 | |a We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. | ||
| 520 | |a We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. | ||
| 650 | 7 | |a Data center networks |2 Elsevier | |
| 650 | 7 | |a Traffic matrix estimation |2 Elsevier | |
| 650 | 7 | |a Network tomography |2 Elsevier | |
| 700 | 1 | |a Qiao, Yan |4 oth | |
| 700 | 1 | |a Luo, Jun |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Chown, Justin ELSEVIER |t Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data |d 2016 |d the international journal for the computer and telecommunications industry |g Amsterdam [u.a.] |w (DE-627)ELV019098014 |
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10.1016/j.comcom.2014.02.016 doi GBVA2015002000009.pica (DE-627)ELV039579018 (ELSEVIER)S0140-3664(14)00069-3 DE-627 ger DE-627 rakwb eng 004 004 DE-600 510 VZ 610 VZ 44.87 bkl Hu, Zhiming verfasserin aut Coarse-grained traffic matrix estimation for data center networks 2015transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. Data center networks Elsevier Traffic matrix estimation Elsevier Network tomography Elsevier Qiao, Yan oth Luo, Jun oth Enthalten in Elsevier Science Chown, Justin ELSEVIER Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data 2016 the international journal for the computer and telecommunications industry Amsterdam [u.a.] (DE-627)ELV019098014 volume:56 year:2015 day:1 month:02 pages:25-34 extent:10 https://doi.org/10.1016/j.comcom.2014.02.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 56 2015 1 0201 25-34 10 045F 004 |
| spelling |
10.1016/j.comcom.2014.02.016 doi GBVA2015002000009.pica (DE-627)ELV039579018 (ELSEVIER)S0140-3664(14)00069-3 DE-627 ger DE-627 rakwb eng 004 004 DE-600 510 VZ 610 VZ 44.87 bkl Hu, Zhiming verfasserin aut Coarse-grained traffic matrix estimation for data center networks 2015transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. Data center networks Elsevier Traffic matrix estimation Elsevier Network tomography Elsevier Qiao, Yan oth Luo, Jun oth Enthalten in Elsevier Science Chown, Justin ELSEVIER Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data 2016 the international journal for the computer and telecommunications industry Amsterdam [u.a.] (DE-627)ELV019098014 volume:56 year:2015 day:1 month:02 pages:25-34 extent:10 https://doi.org/10.1016/j.comcom.2014.02.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 56 2015 1 0201 25-34 10 045F 004 |
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10.1016/j.comcom.2014.02.016 doi GBVA2015002000009.pica (DE-627)ELV039579018 (ELSEVIER)S0140-3664(14)00069-3 DE-627 ger DE-627 rakwb eng 004 004 DE-600 510 VZ 610 VZ 44.87 bkl Hu, Zhiming verfasserin aut Coarse-grained traffic matrix estimation for data center networks 2015transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. Data center networks Elsevier Traffic matrix estimation Elsevier Network tomography Elsevier Qiao, Yan oth Luo, Jun oth Enthalten in Elsevier Science Chown, Justin ELSEVIER Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data 2016 the international journal for the computer and telecommunications industry Amsterdam [u.a.] (DE-627)ELV019098014 volume:56 year:2015 day:1 month:02 pages:25-34 extent:10 https://doi.org/10.1016/j.comcom.2014.02.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 56 2015 1 0201 25-34 10 045F 004 |
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10.1016/j.comcom.2014.02.016 doi GBVA2015002000009.pica (DE-627)ELV039579018 (ELSEVIER)S0140-3664(14)00069-3 DE-627 ger DE-627 rakwb eng 004 004 DE-600 510 VZ 610 VZ 44.87 bkl Hu, Zhiming verfasserin aut Coarse-grained traffic matrix estimation for data center networks 2015transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. Data center networks Elsevier Traffic matrix estimation Elsevier Network tomography Elsevier Qiao, Yan oth Luo, Jun oth Enthalten in Elsevier Science Chown, Justin ELSEVIER Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data 2016 the international journal for the computer and telecommunications industry Amsterdam [u.a.] (DE-627)ELV019098014 volume:56 year:2015 day:1 month:02 pages:25-34 extent:10 https://doi.org/10.1016/j.comcom.2014.02.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 56 2015 1 0201 25-34 10 045F 004 |
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10.1016/j.comcom.2014.02.016 doi GBVA2015002000009.pica (DE-627)ELV039579018 (ELSEVIER)S0140-3664(14)00069-3 DE-627 ger DE-627 rakwb eng 004 004 DE-600 510 VZ 610 VZ 44.87 bkl Hu, Zhiming verfasserin aut Coarse-grained traffic matrix estimation for data center networks 2015transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. Data center networks Elsevier Traffic matrix estimation Elsevier Network tomography Elsevier Qiao, Yan oth Luo, Jun oth Enthalten in Elsevier Science Chown, Justin ELSEVIER Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data 2016 the international journal for the computer and telecommunications industry Amsterdam [u.a.] (DE-627)ELV019098014 volume:56 year:2015 day:1 month:02 pages:25-34 extent:10 https://doi.org/10.1016/j.comcom.2014.02.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 56 2015 1 0201 25-34 10 045F 004 |
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We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. |
| abstractGer |
We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. |
| abstract_unstemmed |
We address the problem of estimating the real-time traffic flows in data center networks (DCNs), using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin–destination (OD) pairs in ISP networks, the traffic flows across servers or Top of Rack (ToR) switch pairs in DCNs are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to DCNs directly. In this paper, we make the first step to solve the TM estimation problem in DCNs by leveraging the characteristics of prevailing data center architectures and decomposing the topologies of DCNs, which makes TM estimation problems in DCNs easy to handle. We also state a basic theory to obtain the aggregate traffic characteristics of these clusters unbiasedly. We propose two efficient TM estimation algorithms based on the decomposed topology and the aggregate traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy. |
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