Tomofanout: a novel approach for large-scale IP traffic matrix estimation with excellent accuracy
Abstract Traffic matrix (TM) plays an important role in many network engineering and management tasks. However, the accurate TM estimation is still a challenge because the problem is highly under-constrained. In this paper, we propose a considerably accurate approach, termed as Tomofanout, for the e...
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Gespeichert in:
| Autor*in: |
Tan, Liansheng [verfasserIn] Zhou, Haifeng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Annals of telecommunications - Paris : Lavoisier, 1946, 70(2014), 3-4 vom: 17. Apr., Seite 149-158 |
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| Übergeordnetes Werk: |
volume:70 ; year:2014 ; number:3-4 ; day:17 ; month:04 ; pages:149-158 |
| Links: |
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| DOI / URN: |
10.1007/s12243-014-0431-x |
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| Katalog-ID: |
SPR024888974 |
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| 245 | 1 | 0 | |a Tomofanout: a novel approach for large-scale IP traffic matrix estimation with excellent accuracy |
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| 520 | |a Abstract Traffic matrix (TM) plays an important role in many network engineering and management tasks. However, the accurate TM estimation is still a challenge because the problem is highly under-constrained. In this paper, we propose a considerably accurate approach, termed as Tomofanout, for the estimation of TM in large-scale IP network using the available link load data, routing matrix, and partial direct measurement data of TM. Firstly, we propose an edge link fanout model which defines each edge link’s fanout, i.e., each edge link’s fractions of traffic emitting from that edge link to other edge links. Secondly, benefited from the edge link fanout’s diurnal pattern and stability, we are able to compute the edge link baseline fanout to estimate the TM at the following days by multiplying it by the edge link loads at the corresponding time intervals. In such way, an initial link-to-link TM estimation result is calculated by the edge link fanout model. Further, by making the corresponding transformation to the link-to-link TM, the router-to-router TM estimation result is thus obtained. Thirdly, the solution is then refined by the basic model of the Tomography method to keep consistent with both the edge and the interior link loads for further improvement of accuracy in estimation. In particular, the expectation maximization (EM) iteration of the basic model of Tomography method is used for further refinement. As the iteration is running on, the edge link fanout model solution is gradually approaching to the final estimation result, which is compatible with both the edge and the interior link loads. Fourthly, a general algorithm is proposed for computing the edge link baseline fanout and the estimation of the TM. Finally, the Tomofanout approach is validated by simulation studies using the real data from the Abilene Network. The simulation results demonstrate that Tomofanout achieves extremely high accuracy: its spatial relative error (SRE) is less than one half of Tomogravity’s, while its temporal relative error (TRE) is less than one half of Fanout’s and is only one third of Tomogravity’s. | ||
| 650 | 4 | |a Traffic matrix estimation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Traffic engineering |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Network capacity planning and management |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Network optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a SNMP |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Zhou, Haifeng |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Annals of telecommunications |d Paris : Lavoisier, 1946 |g 70(2014), 3-4 vom: 17. Apr., Seite 149-158 |w (DE-627)547663234 |w (DE-600)2391943-7 |x 1958-9395 |7 nnns |
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10.1007/s12243-014-0431-x doi (DE-627)SPR024888974 (SPR)s12243-014-0431-x-e DE-627 ger DE-627 rakwb eng 620 ASE Tan, Liansheng verfasserin aut Tomofanout: a novel approach for large-scale IP traffic matrix estimation with excellent accuracy 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traffic matrix (TM) plays an important role in many network engineering and management tasks. However, the accurate TM estimation is still a challenge because the problem is highly under-constrained. In this paper, we propose a considerably accurate approach, termed as Tomofanout, for the estimation of TM in large-scale IP network using the available link load data, routing matrix, and partial direct measurement data of TM. Firstly, we propose an edge link fanout model which defines each edge link’s fanout, i.e., each edge link’s fractions of traffic emitting from that edge link to other edge links. Secondly, benefited from the edge link fanout’s diurnal pattern and stability, we are able to compute the edge link baseline fanout to estimate the TM at the following days by multiplying it by the edge link loads at the corresponding time intervals. In such way, an initial link-to-link TM estimation result is calculated by the edge link fanout model. Further, by making the corresponding transformation to the link-to-link TM, the router-to-router TM estimation result is thus obtained. Thirdly, the solution is then refined by the basic model of the Tomography method to keep consistent with both the edge and the interior link loads for further improvement of accuracy in estimation. In particular, the expectation maximization (EM) iteration of the basic model of Tomography method is used for further refinement. As the iteration is running on, the edge link fanout model solution is gradually approaching to the final estimation result, which is compatible with both the edge and the interior link loads. Fourthly, a general algorithm is proposed for computing the edge link baseline fanout and the estimation of the TM. Finally, the Tomofanout approach is validated by simulation studies using the real data from the Abilene Network. The simulation results demonstrate that Tomofanout achieves extremely high accuracy: its spatial relative error (SRE) is less than one half of Tomogravity’s, while its temporal relative error (TRE) is less than one half of Fanout’s and is only one third of Tomogravity’s. Traffic matrix estimation (dpeaa)DE-He213 Traffic engineering (dpeaa)DE-He213 Network capacity planning and management (dpeaa)DE-He213 Network optimization (dpeaa)DE-He213 SNMP (dpeaa)DE-He213 Zhou, Haifeng verfasserin aut Enthalten in Annals of telecommunications Paris : Lavoisier, 1946 70(2014), 3-4 vom: 17. Apr., Seite 149-158 (DE-627)547663234 (DE-600)2391943-7 1958-9395 nnns volume:70 year:2014 number:3-4 day:17 month:04 pages:149-158 https://dx.doi.org/10.1007/s12243-014-0431-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 70 2014 3-4 17 04 149-158 |
| spelling |
10.1007/s12243-014-0431-x doi (DE-627)SPR024888974 (SPR)s12243-014-0431-x-e DE-627 ger DE-627 rakwb eng 620 ASE Tan, Liansheng verfasserin aut Tomofanout: a novel approach for large-scale IP traffic matrix estimation with excellent accuracy 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traffic matrix (TM) plays an important role in many network engineering and management tasks. However, the accurate TM estimation is still a challenge because the problem is highly under-constrained. In this paper, we propose a considerably accurate approach, termed as Tomofanout, for the estimation of TM in large-scale IP network using the available link load data, routing matrix, and partial direct measurement data of TM. Firstly, we propose an edge link fanout model which defines each edge link’s fanout, i.e., each edge link’s fractions of traffic emitting from that edge link to other edge links. Secondly, benefited from the edge link fanout’s diurnal pattern and stability, we are able to compute the edge link baseline fanout to estimate the TM at the following days by multiplying it by the edge link loads at the corresponding time intervals. In such way, an initial link-to-link TM estimation result is calculated by the edge link fanout model. Further, by making the corresponding transformation to the link-to-link TM, the router-to-router TM estimation result is thus obtained. Thirdly, the solution is then refined by the basic model of the Tomography method to keep consistent with both the edge and the interior link loads for further improvement of accuracy in estimation. In particular, the expectation maximization (EM) iteration of the basic model of Tomography method is used for further refinement. As the iteration is running on, the edge link fanout model solution is gradually approaching to the final estimation result, which is compatible with both the edge and the interior link loads. Fourthly, a general algorithm is proposed for computing the edge link baseline fanout and the estimation of the TM. Finally, the Tomofanout approach is validated by simulation studies using the real data from the Abilene Network. The simulation results demonstrate that Tomofanout achieves extremely high accuracy: its spatial relative error (SRE) is less than one half of Tomogravity’s, while its temporal relative error (TRE) is less than one half of Fanout’s and is only one third of Tomogravity’s. Traffic matrix estimation (dpeaa)DE-He213 Traffic engineering (dpeaa)DE-He213 Network capacity planning and management (dpeaa)DE-He213 Network optimization (dpeaa)DE-He213 SNMP (dpeaa)DE-He213 Zhou, Haifeng verfasserin aut Enthalten in Annals of telecommunications Paris : Lavoisier, 1946 70(2014), 3-4 vom: 17. Apr., Seite 149-158 (DE-627)547663234 (DE-600)2391943-7 1958-9395 nnns volume:70 year:2014 number:3-4 day:17 month:04 pages:149-158 https://dx.doi.org/10.1007/s12243-014-0431-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 70 2014 3-4 17 04 149-158 |
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10.1007/s12243-014-0431-x doi (DE-627)SPR024888974 (SPR)s12243-014-0431-x-e DE-627 ger DE-627 rakwb eng 620 ASE Tan, Liansheng verfasserin aut Tomofanout: a novel approach for large-scale IP traffic matrix estimation with excellent accuracy 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traffic matrix (TM) plays an important role in many network engineering and management tasks. However, the accurate TM estimation is still a challenge because the problem is highly under-constrained. In this paper, we propose a considerably accurate approach, termed as Tomofanout, for the estimation of TM in large-scale IP network using the available link load data, routing matrix, and partial direct measurement data of TM. Firstly, we propose an edge link fanout model which defines each edge link’s fanout, i.e., each edge link’s fractions of traffic emitting from that edge link to other edge links. Secondly, benefited from the edge link fanout’s diurnal pattern and stability, we are able to compute the edge link baseline fanout to estimate the TM at the following days by multiplying it by the edge link loads at the corresponding time intervals. In such way, an initial link-to-link TM estimation result is calculated by the edge link fanout model. Further, by making the corresponding transformation to the link-to-link TM, the router-to-router TM estimation result is thus obtained. Thirdly, the solution is then refined by the basic model of the Tomography method to keep consistent with both the edge and the interior link loads for further improvement of accuracy in estimation. In particular, the expectation maximization (EM) iteration of the basic model of Tomography method is used for further refinement. As the iteration is running on, the edge link fanout model solution is gradually approaching to the final estimation result, which is compatible with both the edge and the interior link loads. Fourthly, a general algorithm is proposed for computing the edge link baseline fanout and the estimation of the TM. Finally, the Tomofanout approach is validated by simulation studies using the real data from the Abilene Network. The simulation results demonstrate that Tomofanout achieves extremely high accuracy: its spatial relative error (SRE) is less than one half of Tomogravity’s, while its temporal relative error (TRE) is less than one half of Fanout’s and is only one third of Tomogravity’s. Traffic matrix estimation (dpeaa)DE-He213 Traffic engineering (dpeaa)DE-He213 Network capacity planning and management (dpeaa)DE-He213 Network optimization (dpeaa)DE-He213 SNMP (dpeaa)DE-He213 Zhou, Haifeng verfasserin aut Enthalten in Annals of telecommunications Paris : Lavoisier, 1946 70(2014), 3-4 vom: 17. Apr., Seite 149-158 (DE-627)547663234 (DE-600)2391943-7 1958-9395 nnns volume:70 year:2014 number:3-4 day:17 month:04 pages:149-158 https://dx.doi.org/10.1007/s12243-014-0431-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 70 2014 3-4 17 04 149-158 |
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10.1007/s12243-014-0431-x doi (DE-627)SPR024888974 (SPR)s12243-014-0431-x-e DE-627 ger DE-627 rakwb eng 620 ASE Tan, Liansheng verfasserin aut Tomofanout: a novel approach for large-scale IP traffic matrix estimation with excellent accuracy 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traffic matrix (TM) plays an important role in many network engineering and management tasks. However, the accurate TM estimation is still a challenge because the problem is highly under-constrained. In this paper, we propose a considerably accurate approach, termed as Tomofanout, for the estimation of TM in large-scale IP network using the available link load data, routing matrix, and partial direct measurement data of TM. Firstly, we propose an edge link fanout model which defines each edge link’s fanout, i.e., each edge link’s fractions of traffic emitting from that edge link to other edge links. Secondly, benefited from the edge link fanout’s diurnal pattern and stability, we are able to compute the edge link baseline fanout to estimate the TM at the following days by multiplying it by the edge link loads at the corresponding time intervals. In such way, an initial link-to-link TM estimation result is calculated by the edge link fanout model. Further, by making the corresponding transformation to the link-to-link TM, the router-to-router TM estimation result is thus obtained. Thirdly, the solution is then refined by the basic model of the Tomography method to keep consistent with both the edge and the interior link loads for further improvement of accuracy in estimation. In particular, the expectation maximization (EM) iteration of the basic model of Tomography method is used for further refinement. As the iteration is running on, the edge link fanout model solution is gradually approaching to the final estimation result, which is compatible with both the edge and the interior link loads. Fourthly, a general algorithm is proposed for computing the edge link baseline fanout and the estimation of the TM. Finally, the Tomofanout approach is validated by simulation studies using the real data from the Abilene Network. The simulation results demonstrate that Tomofanout achieves extremely high accuracy: its spatial relative error (SRE) is less than one half of Tomogravity’s, while its temporal relative error (TRE) is less than one half of Fanout’s and is only one third of Tomogravity’s. Traffic matrix estimation (dpeaa)DE-He213 Traffic engineering (dpeaa)DE-He213 Network capacity planning and management (dpeaa)DE-He213 Network optimization (dpeaa)DE-He213 SNMP (dpeaa)DE-He213 Zhou, Haifeng verfasserin aut Enthalten in Annals of telecommunications Paris : Lavoisier, 1946 70(2014), 3-4 vom: 17. Apr., Seite 149-158 (DE-627)547663234 (DE-600)2391943-7 1958-9395 nnns volume:70 year:2014 number:3-4 day:17 month:04 pages:149-158 https://dx.doi.org/10.1007/s12243-014-0431-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 70 2014 3-4 17 04 149-158 |
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10.1007/s12243-014-0431-x doi (DE-627)SPR024888974 (SPR)s12243-014-0431-x-e DE-627 ger DE-627 rakwb eng 620 ASE Tan, Liansheng verfasserin aut Tomofanout: a novel approach for large-scale IP traffic matrix estimation with excellent accuracy 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traffic matrix (TM) plays an important role in many network engineering and management tasks. However, the accurate TM estimation is still a challenge because the problem is highly under-constrained. In this paper, we propose a considerably accurate approach, termed as Tomofanout, for the estimation of TM in large-scale IP network using the available link load data, routing matrix, and partial direct measurement data of TM. Firstly, we propose an edge link fanout model which defines each edge link’s fanout, i.e., each edge link’s fractions of traffic emitting from that edge link to other edge links. Secondly, benefited from the edge link fanout’s diurnal pattern and stability, we are able to compute the edge link baseline fanout to estimate the TM at the following days by multiplying it by the edge link loads at the corresponding time intervals. In such way, an initial link-to-link TM estimation result is calculated by the edge link fanout model. Further, by making the corresponding transformation to the link-to-link TM, the router-to-router TM estimation result is thus obtained. Thirdly, the solution is then refined by the basic model of the Tomography method to keep consistent with both the edge and the interior link loads for further improvement of accuracy in estimation. In particular, the expectation maximization (EM) iteration of the basic model of Tomography method is used for further refinement. As the iteration is running on, the edge link fanout model solution is gradually approaching to the final estimation result, which is compatible with both the edge and the interior link loads. Fourthly, a general algorithm is proposed for computing the edge link baseline fanout and the estimation of the TM. Finally, the Tomofanout approach is validated by simulation studies using the real data from the Abilene Network. The simulation results demonstrate that Tomofanout achieves extremely high accuracy: its spatial relative error (SRE) is less than one half of Tomogravity’s, while its temporal relative error (TRE) is less than one half of Fanout’s and is only one third of Tomogravity’s. Traffic matrix estimation (dpeaa)DE-He213 Traffic engineering (dpeaa)DE-He213 Network capacity planning and management (dpeaa)DE-He213 Network optimization (dpeaa)DE-He213 SNMP (dpeaa)DE-He213 Zhou, Haifeng verfasserin aut Enthalten in Annals of telecommunications Paris : Lavoisier, 1946 70(2014), 3-4 vom: 17. Apr., Seite 149-158 (DE-627)547663234 (DE-600)2391943-7 1958-9395 nnns volume:70 year:2014 number:3-4 day:17 month:04 pages:149-158 https://dx.doi.org/10.1007/s12243-014-0431-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 70 2014 3-4 17 04 149-158 |
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Tan, Liansheng @@aut@@ Zhou, Haifeng @@aut@@ |
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However, the accurate TM estimation is still a challenge because the problem is highly under-constrained. In this paper, we propose a considerably accurate approach, termed as Tomofanout, for the estimation of TM in large-scale IP network using the available link load data, routing matrix, and partial direct measurement data of TM. Firstly, we propose an edge link fanout model which defines each edge link’s fanout, i.e., each edge link’s fractions of traffic emitting from that edge link to other edge links. Secondly, benefited from the edge link fanout’s diurnal pattern and stability, we are able to compute the edge link baseline fanout to estimate the TM at the following days by multiplying it by the edge link loads at the corresponding time intervals. In such way, an initial link-to-link TM estimation result is calculated by the edge link fanout model. Further, by making the corresponding transformation to the link-to-link TM, the router-to-router TM estimation result is thus obtained. Thirdly, the solution is then refined by the basic model of the Tomography method to keep consistent with both the edge and the interior link loads for further improvement of accuracy in estimation. In particular, the expectation maximization (EM) iteration of the basic model of Tomography method is used for further refinement. As the iteration is running on, the edge link fanout model solution is gradually approaching to the final estimation result, which is compatible with both the edge and the interior link loads. Fourthly, a general algorithm is proposed for computing the edge link baseline fanout and the estimation of the TM. Finally, the Tomofanout approach is validated by simulation studies using the real data from the Abilene Network. The simulation results demonstrate that Tomofanout achieves extremely high accuracy: its spatial relative error (SRE) is less than one half of Tomogravity’s, while its temporal relative error (TRE) is less than one half of Fanout’s and is only one third of Tomogravity’s.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Traffic matrix estimation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Traffic engineering</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Network capacity planning and management</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Network optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SNMP</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Haifeng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Annals of telecommunications</subfield><subfield code="d">Paris : Lavoisier, 1946</subfield><subfield code="g">70(2014), 3-4 vom: 17. 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Tan, Liansheng |
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Tan, Liansheng ddc 620 misc Traffic matrix estimation misc Traffic engineering misc Network capacity planning and management misc Network optimization misc SNMP Tomofanout: a novel approach for large-scale IP traffic matrix estimation with excellent accuracy |
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Tomofanout: a novel approach for large-scale IP traffic matrix estimation with excellent accuracy |
| abstract |
Abstract Traffic matrix (TM) plays an important role in many network engineering and management tasks. However, the accurate TM estimation is still a challenge because the problem is highly under-constrained. In this paper, we propose a considerably accurate approach, termed as Tomofanout, for the estimation of TM in large-scale IP network using the available link load data, routing matrix, and partial direct measurement data of TM. Firstly, we propose an edge link fanout model which defines each edge link’s fanout, i.e., each edge link’s fractions of traffic emitting from that edge link to other edge links. Secondly, benefited from the edge link fanout’s diurnal pattern and stability, we are able to compute the edge link baseline fanout to estimate the TM at the following days by multiplying it by the edge link loads at the corresponding time intervals. In such way, an initial link-to-link TM estimation result is calculated by the edge link fanout model. Further, by making the corresponding transformation to the link-to-link TM, the router-to-router TM estimation result is thus obtained. Thirdly, the solution is then refined by the basic model of the Tomography method to keep consistent with both the edge and the interior link loads for further improvement of accuracy in estimation. In particular, the expectation maximization (EM) iteration of the basic model of Tomography method is used for further refinement. As the iteration is running on, the edge link fanout model solution is gradually approaching to the final estimation result, which is compatible with both the edge and the interior link loads. Fourthly, a general algorithm is proposed for computing the edge link baseline fanout and the estimation of the TM. Finally, the Tomofanout approach is validated by simulation studies using the real data from the Abilene Network. The simulation results demonstrate that Tomofanout achieves extremely high accuracy: its spatial relative error (SRE) is less than one half of Tomogravity’s, while its temporal relative error (TRE) is less than one half of Fanout’s and is only one third of Tomogravity’s. |
| abstractGer |
Abstract Traffic matrix (TM) plays an important role in many network engineering and management tasks. However, the accurate TM estimation is still a challenge because the problem is highly under-constrained. In this paper, we propose a considerably accurate approach, termed as Tomofanout, for the estimation of TM in large-scale IP network using the available link load data, routing matrix, and partial direct measurement data of TM. Firstly, we propose an edge link fanout model which defines each edge link’s fanout, i.e., each edge link’s fractions of traffic emitting from that edge link to other edge links. Secondly, benefited from the edge link fanout’s diurnal pattern and stability, we are able to compute the edge link baseline fanout to estimate the TM at the following days by multiplying it by the edge link loads at the corresponding time intervals. In such way, an initial link-to-link TM estimation result is calculated by the edge link fanout model. Further, by making the corresponding transformation to the link-to-link TM, the router-to-router TM estimation result is thus obtained. Thirdly, the solution is then refined by the basic model of the Tomography method to keep consistent with both the edge and the interior link loads for further improvement of accuracy in estimation. In particular, the expectation maximization (EM) iteration of the basic model of Tomography method is used for further refinement. As the iteration is running on, the edge link fanout model solution is gradually approaching to the final estimation result, which is compatible with both the edge and the interior link loads. Fourthly, a general algorithm is proposed for computing the edge link baseline fanout and the estimation of the TM. Finally, the Tomofanout approach is validated by simulation studies using the real data from the Abilene Network. The simulation results demonstrate that Tomofanout achieves extremely high accuracy: its spatial relative error (SRE) is less than one half of Tomogravity’s, while its temporal relative error (TRE) is less than one half of Fanout’s and is only one third of Tomogravity’s. |
| abstract_unstemmed |
Abstract Traffic matrix (TM) plays an important role in many network engineering and management tasks. However, the accurate TM estimation is still a challenge because the problem is highly under-constrained. In this paper, we propose a considerably accurate approach, termed as Tomofanout, for the estimation of TM in large-scale IP network using the available link load data, routing matrix, and partial direct measurement data of TM. Firstly, we propose an edge link fanout model which defines each edge link’s fanout, i.e., each edge link’s fractions of traffic emitting from that edge link to other edge links. Secondly, benefited from the edge link fanout’s diurnal pattern and stability, we are able to compute the edge link baseline fanout to estimate the TM at the following days by multiplying it by the edge link loads at the corresponding time intervals. In such way, an initial link-to-link TM estimation result is calculated by the edge link fanout model. Further, by making the corresponding transformation to the link-to-link TM, the router-to-router TM estimation result is thus obtained. Thirdly, the solution is then refined by the basic model of the Tomography method to keep consistent with both the edge and the interior link loads for further improvement of accuracy in estimation. In particular, the expectation maximization (EM) iteration of the basic model of Tomography method is used for further refinement. As the iteration is running on, the edge link fanout model solution is gradually approaching to the final estimation result, which is compatible with both the edge and the interior link loads. Fourthly, a general algorithm is proposed for computing the edge link baseline fanout and the estimation of the TM. Finally, the Tomofanout approach is validated by simulation studies using the real data from the Abilene Network. The simulation results demonstrate that Tomofanout achieves extremely high accuracy: its spatial relative error (SRE) is less than one half of Tomogravity’s, while its temporal relative error (TRE) is less than one half of Fanout’s and is only one third of Tomogravity’s. |
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| container_issue |
3-4 |
| title_short |
Tomofanout: a novel approach for large-scale IP traffic matrix estimation with excellent accuracy |
| url |
https://dx.doi.org/10.1007/s12243-014-0431-x |
| remote_bool |
true |
| author2 |
Zhou, Haifeng |
| author2Str |
Zhou, Haifeng |
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| hochschulschrift_bool |
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| doi_str |
10.1007/s12243-014-0431-x |
| up_date |
2024-07-04T02:44:19.765Z |
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| score |
7.402237 |