An online dynamic traffic matrix completion method in software defined networks

Online temporal Traffic Matrix (TM) estimation is important for network management and traffic engineering. However, current estimation methods are insufficient in estimation accuracy and measurement cost. In this paper, by combining the low rank feature of the traffic matrix and the flow measuremen...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Li, Dongyang [verfasserIn] Xing, Changyou [verfasserIn] Zhang, Guomin [verfasserIn] Cao, Huaping [verfasserIn] Xu, Bo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Temporal traffic matrix estimation Software defined networking Matrix completion OpenFlow

Übergeordnetes Werk:	Enthalten in: Computer communications - Amsterdam [u.a.] : Elsevier Science, 1978, 145, Seite 43-53
Übergeordnetes Werk:	volume:145 ; pages:43-53

DOI / URN:	10.1016/j.comcom.2019.05.023

Katalog-ID:	ELV000010391

Internformat


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520			\|a Online temporal Traffic Matrix (TM) estimation is important for network management and traffic engineering. However, current estimation methods are insufficient in estimation accuracy and measurement cost. In this paper, by combining the low rank feature of the traffic matrix and the flow measurement capability in Software Defined Networks (SDN), we propose a novel online dynamic temporal traffic matrix completion mechanism DTMC. DTMC evaluates the impact of different Origin–Destination (OD) flows on improving the traffic matrix estimation accuracy from the perspective of uncertainty and temporal stability, selects an appropriate number of “most-informative” flows to construct the measurement set, and finally recovers the complete traffic matrix by using the partially measured OD flow information dynamically. The experiment results on two Internet measurement datasets show that DTMC can estimate the dynamic temporal traffic matrix accurately only by consuming a small amount of measurement resources.
650		4	\|a Temporal traffic matrix estimation
650		4	\|a Software defined networking
650		4	\|a Matrix completion
650		4	\|a OpenFlow
700	1		\|a Xing, Changyou \|e verfasserin \|4 aut
700	1		\|a Zhang, Guomin \|e verfasserin \|4 aut
700	1		\|a Cao, Huaping \|e verfasserin \|4 aut
700	1		\|a Xu, Bo \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Computer communications \|d Amsterdam [u.a.] : Elsevier Science, 1978 \|g 145, Seite 43-53 \|h Online-Ressource \|w (DE-627)270937900 \|w (DE-600)1478742-8 \|w (DE-576)078316790 \|x 0140-3664 \|7 nnns
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author_variant	d l dl c x cx g z gz h c hc b x bx
matchkey_str	article:01403664:2019----::nnieyaitafcarxopeinehdnot
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publishDate	2019
allfields	10.1016/j.comcom.2019.05.023 doi (DE-627)ELV000010391 (ELSEVIER)S0140-3664(19)30027-1 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Li, Dongyang verfasserin aut An online dynamic traffic matrix completion method in software defined networks 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online temporal Traffic Matrix (TM) estimation is important for network management and traffic engineering. However, current estimation methods are insufficient in estimation accuracy and measurement cost. In this paper, by combining the low rank feature of the traffic matrix and the flow measurement capability in Software Defined Networks (SDN), we propose a novel online dynamic temporal traffic matrix completion mechanism DTMC. DTMC evaluates the impact of different Origin–Destination (OD) flows on improving the traffic matrix estimation accuracy from the perspective of uncertainty and temporal stability, selects an appropriate number of “most-informative” flows to construct the measurement set, and finally recovers the complete traffic matrix by using the partially measured OD flow information dynamically. The experiment results on two Internet measurement datasets show that DTMC can estimate the dynamic temporal traffic matrix accurately only by consuming a small amount of measurement resources. Temporal traffic matrix estimation Software defined networking Matrix completion OpenFlow Xing, Changyou verfasserin aut Zhang, Guomin verfasserin aut Cao, Huaping verfasserin aut Xu, Bo verfasserin aut Enthalten in Computer communications Amsterdam [u.a.] : Elsevier Science, 1978 145, Seite 43-53 Online-Ressource (DE-627)270937900 (DE-600)1478742-8 (DE-576)078316790 0140-3664 nnns volume:145 pages:43-53 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 145 43-53
spelling	10.1016/j.comcom.2019.05.023 doi (DE-627)ELV000010391 (ELSEVIER)S0140-3664(19)30027-1 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Li, Dongyang verfasserin aut An online dynamic traffic matrix completion method in software defined networks 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online temporal Traffic Matrix (TM) estimation is important for network management and traffic engineering. However, current estimation methods are insufficient in estimation accuracy and measurement cost. In this paper, by combining the low rank feature of the traffic matrix and the flow measurement capability in Software Defined Networks (SDN), we propose a novel online dynamic temporal traffic matrix completion mechanism DTMC. DTMC evaluates the impact of different Origin–Destination (OD) flows on improving the traffic matrix estimation accuracy from the perspective of uncertainty and temporal stability, selects an appropriate number of “most-informative” flows to construct the measurement set, and finally recovers the complete traffic matrix by using the partially measured OD flow information dynamically. The experiment results on two Internet measurement datasets show that DTMC can estimate the dynamic temporal traffic matrix accurately only by consuming a small amount of measurement resources. Temporal traffic matrix estimation Software defined networking Matrix completion OpenFlow Xing, Changyou verfasserin aut Zhang, Guomin verfasserin aut Cao, Huaping verfasserin aut Xu, Bo verfasserin aut Enthalten in Computer communications Amsterdam [u.a.] : Elsevier Science, 1978 145, Seite 43-53 Online-Ressource (DE-627)270937900 (DE-600)1478742-8 (DE-576)078316790 0140-3664 nnns volume:145 pages:43-53 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 145 43-53
allfields_unstemmed	10.1016/j.comcom.2019.05.023 doi (DE-627)ELV000010391 (ELSEVIER)S0140-3664(19)30027-1 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Li, Dongyang verfasserin aut An online dynamic traffic matrix completion method in software defined networks 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online temporal Traffic Matrix (TM) estimation is important for network management and traffic engineering. However, current estimation methods are insufficient in estimation accuracy and measurement cost. In this paper, by combining the low rank feature of the traffic matrix and the flow measurement capability in Software Defined Networks (SDN), we propose a novel online dynamic temporal traffic matrix completion mechanism DTMC. DTMC evaluates the impact of different Origin–Destination (OD) flows on improving the traffic matrix estimation accuracy from the perspective of uncertainty and temporal stability, selects an appropriate number of “most-informative” flows to construct the measurement set, and finally recovers the complete traffic matrix by using the partially measured OD flow information dynamically. The experiment results on two Internet measurement datasets show that DTMC can estimate the dynamic temporal traffic matrix accurately only by consuming a small amount of measurement resources. Temporal traffic matrix estimation Software defined networking Matrix completion OpenFlow Xing, Changyou verfasserin aut Zhang, Guomin verfasserin aut Cao, Huaping verfasserin aut Xu, Bo verfasserin aut Enthalten in Computer communications Amsterdam [u.a.] : Elsevier Science, 1978 145, Seite 43-53 Online-Ressource (DE-627)270937900 (DE-600)1478742-8 (DE-576)078316790 0140-3664 nnns volume:145 pages:43-53 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 145 43-53
allfieldsGer	10.1016/j.comcom.2019.05.023 doi (DE-627)ELV000010391 (ELSEVIER)S0140-3664(19)30027-1 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Li, Dongyang verfasserin aut An online dynamic traffic matrix completion method in software defined networks 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online temporal Traffic Matrix (TM) estimation is important for network management and traffic engineering. However, current estimation methods are insufficient in estimation accuracy and measurement cost. In this paper, by combining the low rank feature of the traffic matrix and the flow measurement capability in Software Defined Networks (SDN), we propose a novel online dynamic temporal traffic matrix completion mechanism DTMC. DTMC evaluates the impact of different Origin–Destination (OD) flows on improving the traffic matrix estimation accuracy from the perspective of uncertainty and temporal stability, selects an appropriate number of “most-informative” flows to construct the measurement set, and finally recovers the complete traffic matrix by using the partially measured OD flow information dynamically. The experiment results on two Internet measurement datasets show that DTMC can estimate the dynamic temporal traffic matrix accurately only by consuming a small amount of measurement resources. Temporal traffic matrix estimation Software defined networking Matrix completion OpenFlow Xing, Changyou verfasserin aut Zhang, Guomin verfasserin aut Cao, Huaping verfasserin aut Xu, Bo verfasserin aut Enthalten in Computer communications Amsterdam [u.a.] : Elsevier Science, 1978 145, Seite 43-53 Online-Ressource (DE-627)270937900 (DE-600)1478742-8 (DE-576)078316790 0140-3664 nnns volume:145 pages:43-53 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 145 43-53
allfieldsSound	10.1016/j.comcom.2019.05.023 doi (DE-627)ELV000010391 (ELSEVIER)S0140-3664(19)30027-1 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Li, Dongyang verfasserin aut An online dynamic traffic matrix completion method in software defined networks 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online temporal Traffic Matrix (TM) estimation is important for network management and traffic engineering. However, current estimation methods are insufficient in estimation accuracy and measurement cost. In this paper, by combining the low rank feature of the traffic matrix and the flow measurement capability in Software Defined Networks (SDN), we propose a novel online dynamic temporal traffic matrix completion mechanism DTMC. DTMC evaluates the impact of different Origin–Destination (OD) flows on improving the traffic matrix estimation accuracy from the perspective of uncertainty and temporal stability, selects an appropriate number of “most-informative” flows to construct the measurement set, and finally recovers the complete traffic matrix by using the partially measured OD flow information dynamically. The experiment results on two Internet measurement datasets show that DTMC can estimate the dynamic temporal traffic matrix accurately only by consuming a small amount of measurement resources. Temporal traffic matrix estimation Software defined networking Matrix completion OpenFlow Xing, Changyou verfasserin aut Zhang, Guomin verfasserin aut Cao, Huaping verfasserin aut Xu, Bo verfasserin aut Enthalten in Computer communications Amsterdam [u.a.] : Elsevier Science, 1978 145, Seite 43-53 Online-Ressource (DE-627)270937900 (DE-600)1478742-8 (DE-576)078316790 0140-3664 nnns volume:145 pages:43-53 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 145 43-53
language	English
source	Enthalten in Computer communications 145, Seite 43-53 volume:145 pages:43-53
sourceStr	Enthalten in Computer communications 145, Seite 43-53 volume:145 pages:43-53
format_phy_str_mv	Article
bklname	Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Temporal traffic matrix estimation Software defined networking Matrix completion OpenFlow
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authorswithroles_txt_mv	Li, Dongyang @@aut@@ Xing, Changyou @@aut@@ Zhang, Guomin @@aut@@ Cao, Huaping @@aut@@ Xu, Bo @@aut@@
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author	Li, Dongyang
spellingShingle	Li, Dongyang ddc 004 bkl 54.00 misc Temporal traffic matrix estimation misc Software defined networking misc Matrix completion misc OpenFlow An online dynamic traffic matrix completion method in software defined networks
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topic_title	004 DE-600 54.00 bkl An online dynamic traffic matrix completion method in software defined networks Temporal traffic matrix estimation Software defined networking Matrix completion OpenFlow
topic	ddc 004 bkl 54.00 misc Temporal traffic matrix estimation misc Software defined networking misc Matrix completion misc OpenFlow
topic_unstemmed	ddc 004 bkl 54.00 misc Temporal traffic matrix estimation misc Software defined networking misc Matrix completion misc OpenFlow
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title	An online dynamic traffic matrix completion method in software defined networks
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title_full	An online dynamic traffic matrix completion method in software defined networks
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title_sort	an online dynamic traffic matrix completion method in software defined networks
title_auth	An online dynamic traffic matrix completion method in software defined networks
abstract	Online temporal Traffic Matrix (TM) estimation is important for network management and traffic engineering. However, current estimation methods are insufficient in estimation accuracy and measurement cost. In this paper, by combining the low rank feature of the traffic matrix and the flow measurement capability in Software Defined Networks (SDN), we propose a novel online dynamic temporal traffic matrix completion mechanism DTMC. DTMC evaluates the impact of different Origin–Destination (OD) flows on improving the traffic matrix estimation accuracy from the perspective of uncertainty and temporal stability, selects an appropriate number of “most-informative” flows to construct the measurement set, and finally recovers the complete traffic matrix by using the partially measured OD flow information dynamically. The experiment results on two Internet measurement datasets show that DTMC can estimate the dynamic temporal traffic matrix accurately only by consuming a small amount of measurement resources.
abstractGer	Online temporal Traffic Matrix (TM) estimation is important for network management and traffic engineering. However, current estimation methods are insufficient in estimation accuracy and measurement cost. In this paper, by combining the low rank feature of the traffic matrix and the flow measurement capability in Software Defined Networks (SDN), we propose a novel online dynamic temporal traffic matrix completion mechanism DTMC. DTMC evaluates the impact of different Origin–Destination (OD) flows on improving the traffic matrix estimation accuracy from the perspective of uncertainty and temporal stability, selects an appropriate number of “most-informative” flows to construct the measurement set, and finally recovers the complete traffic matrix by using the partially measured OD flow information dynamically. The experiment results on two Internet measurement datasets show that DTMC can estimate the dynamic temporal traffic matrix accurately only by consuming a small amount of measurement resources.
abstract_unstemmed	Online temporal Traffic Matrix (TM) estimation is important for network management and traffic engineering. However, current estimation methods are insufficient in estimation accuracy and measurement cost. In this paper, by combining the low rank feature of the traffic matrix and the flow measurement capability in Software Defined Networks (SDN), we propose a novel online dynamic temporal traffic matrix completion mechanism DTMC. DTMC evaluates the impact of different Origin–Destination (OD) flows on improving the traffic matrix estimation accuracy from the perspective of uncertainty and temporal stability, selects an appropriate number of “most-informative” flows to construct the measurement set, and finally recovers the complete traffic matrix by using the partially measured OD flow information dynamically. The experiment results on two Internet measurement datasets show that DTMC can estimate the dynamic temporal traffic matrix accurately only by consuming a small amount of measurement resources.
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title_short	An online dynamic traffic matrix completion method in software defined networks
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author2	Xing, Changyou Zhang, Guomin Cao, Huaping Xu, Bo
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doi_str	10.1016/j.comcom.2019.05.023
up_date	2024-07-06T16:34:06.535Z
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However, current estimation methods are insufficient in estimation accuracy and measurement cost. In this paper, by combining the low rank feature of the traffic matrix and the flow measurement capability in Software Defined Networks (SDN), we propose a novel online dynamic temporal traffic matrix completion mechanism DTMC. DTMC evaluates the impact of different Origin–Destination (OD) flows on improving the traffic matrix estimation accuracy from the perspective of uncertainty and temporal stability, selects an appropriate number of “most-informative” flows to construct the measurement set, and finally recovers the complete traffic matrix by using the partially measured OD flow information dynamically. The experiment results on two Internet measurement datasets show that DTMC can estimate the dynamic temporal traffic matrix accurately only by consuming a small amount of measurement resources.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Temporal traffic matrix estimation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Software defined networking</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Matrix completion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">OpenFlow</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xing, Changyou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Guomin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" 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An online dynamic traffic matrix completion method in software defined networks

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