Network traffic recovery from link-load measurements using tensor triple decomposition strategy for third-order traffic tensors
Network traffic data is the pivot of input in many network tasks but its direct measurement can be insufferably costly. In this paper, we propose a network traffic recovery method which only requires the conveniently measurable link-load traffics. We arrange the traffic data as a third-order tensor...
Saved in:
| Published in: | Journal of computational and applied mathematics Vol. 447; p. 115901 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.09.2024
|
| Subjects: | |
| ISSN: | 0377-0427 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Network traffic data is the pivot of input in many network tasks but its direct measurement can be insufferably costly. In this paper, we propose a network traffic recovery method which only requires the conveniently measurable link-load traffics. We arrange the traffic data as a third-order tensor and utilize the triple decomposition technique proposed very recently by Qi et al. (2021). The studied model is a differentialble unconstrained minimization problem, which can be efficiently solved by a Barzilai–Borwein (BB) gradient algorithm. We prove that the generated iteration sequence can globally converge to a certain stationary point of the objective function. The numerical simulations on three open-source traffic datasets demonstrate the superiority of our method in comparison with other state-of-the-art algorithms. |
|---|---|
| AbstractList | Network traffic data is the pivot of input in many network tasks but its direct measurement can be insufferably costly. In this paper, we propose a network traffic recovery method which only requires the conveniently measurable link-load traffics. We arrange the traffic data as a third-order tensor and utilize the triple decomposition technique proposed very recently by Qi et al. (2021). The studied model is a differentialble unconstrained minimization problem, which can be efficiently solved by a Barzilai–Borwein (BB) gradient algorithm. We prove that the generated iteration sequence can globally converge to a certain stationary point of the objective function. The numerical simulations on three open-source traffic datasets demonstrate the superiority of our method in comparison with other state-of-the-art algorithms. |
| ArticleNumber | 115901 |
| Author | Qi, Liqun Xu, Yanwei Zhang, Liping Ming, Zhenyu Qin, Zhenzhi |
| Author_xml | – sequence: 1 givenname: Zhenyu orcidid: 0000-0001-7270-8022 surname: Ming fullname: Ming, Zhenyu organization: Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co., Ltd., Hong Kong – sequence: 2 givenname: Zhenzhi surname: Qin fullname: Qin, Zhenzhi organization: Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China – sequence: 3 givenname: Liping surname: Zhang fullname: Zhang, Liping email: lipingzhang@mail.tsinghua.edu.cn organization: Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China – sequence: 4 givenname: Yanwei surname: Xu fullname: Xu, Yanwei organization: Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co., Ltd., Hong Kong – sequence: 5 givenname: Liqun surname: Qi fullname: Qi, Liqun organization: Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong |
| BookMark | eNp9kLtOAzEQRV0EiSTwAXT-gV3sfdi7okIRLymCBmrLsWeDk107sp2gVPw6jhZRUKSaYubc0T0zNLHOAkI3lOSUUHa7yZUc8oIUVU5p3RI6QVNScp6RquCXaBbChhDCWlpN0fcrxC_ntzh62XVGYQ_KHcAfcefdgHtjt1nvpMYDyLD3MICNAe-DsWscwQbnE2l2PWCdwGHngonGWRxSXoR1ijldfBqvM-c1-L8_Ixyu0EUn-wDXv3OOPh4f3hfP2fLt6WVxv8xU0fKYKZ7iKZGl0lCouoWSd6DZisu60s1KFVXDViztGlkzxqlkpSRNXfGy7RpGVDlHdMxV3oXgoRM7bwbpj4IScbImNiJZEydrYrSWGP6PUSbKU71UwvRnybuRhFTpYMCLoAxYBdokv1FoZ87QP4rbj5k |
| CitedBy_id | crossref_primary_10_1016_j_sigpro_2025_109903 |
| Cites_doi | 10.1007/BF02289464 10.1016/j.apm.2021.03.036 10.1137/050644641 10.1109/TNET.2018.2797094 10.1017/S0305004100030401 10.1007/s10915-021-01574-0 10.1016/j.neucom.2021.09.065 10.1007/BF02310791 10.1109/TNET.2019.2940147 10.1109/TNET.2011.2169424 10.1016/j.neucom.2020.12.123 10.1137/20M1323266 10.1109/TNET.2005.857115 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier B.V. |
| Copyright_xml | – notice: 2024 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.cam.2024.115901 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Mathematics |
| ExternalDocumentID | 10_1016_j_cam_2024_115901 S0377042724001511 |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 12171271; 12271291 funderid: http://dx.doi.org/10.13039/501100001809 |
| GroupedDBID | --K --M -~X .~1 0R~ 0SF 1B1 1RT 1~. 1~5 29K 4.4 457 4G. 5GY 5VS 6I. 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAFTH AAFWJ AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXKI AAXUO ABAOU ABDPE ABEFU ABFNM ABJNI ABMAC ABTAH ABVKL ABWVN ABXDB ACDAQ ACGFS ACRLP ACRPL ADBBV ADEZE ADMUD ADNMO ADVLN AEBSH AEIPS AEKER AENEX AEXQZ AFJKZ AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AIEXJ AIGVJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ANKPU ARUGR ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC CS3 D-I DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HVGLF HZ~ IHE IXB J1W KOM LG9 M26 M41 MHUIS MO0 N9A NCXOZ NHB O-L O9- OAUVE OK1 OZT P-8 P-9 P2P PC. Q38 R2- RIG RNS ROL RPZ SDF SDG SDP SES SEW SPC SPCBC SSW SSZ T5K TN5 UPT WUQ XPP YQT ZMT ZY4 ~02 ~G- 9DU AATTM AAYWO AAYXX ACLOT ACVFH ADCNI AEUPX AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP APXCP CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c297t-c7dec10a3cde2c59e37fed6b7a54d8bc2486b6de28a56671a63a0854739f860c3 |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001226820900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0377-0427 |
| IngestDate | Sat Nov 29 06:39:24 EST 2025 Tue Nov 18 22:19:11 EST 2025 Sat Feb 08 15:52:27 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | 65K05 49M29 Network traffic recovery 68M10 15A03 BB gradient algorithm Triple decomposition |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c297t-c7dec10a3cde2c59e37fed6b7a54d8bc2486b6de28a56671a63a0854739f860c3 |
| ORCID | 0000-0001-7270-8022 |
| ParticipantIDs | crossref_primary_10_1016_j_cam_2024_115901 crossref_citationtrail_10_1016_j_cam_2024_115901 elsevier_sciencedirect_doi_10_1016_j_cam_2024_115901 |
| PublicationCentury | 2000 |
| PublicationDate | September 2024 2024-09-00 |
| PublicationDateYYYYMMDD | 2024-09-01 |
| PublicationDate_xml | – month: 09 year: 2024 text: September 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Journal of computational and applied mathematics |
| PublicationYear | 2024 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Ming, Zhang, Wu (b7) 2022; 467 Chen, Zhang, Qi (b1) 2021; 88 Roughan, Zhang, Willinger (b8) 2012; 20 Carroll, Chang (b10) 1970; 35 Ming, Zhang, Xu, Bakshi (b2) 2021; 96 R. Penrose, generalized inverse for matrices, in: Math. Proc. Camb. Philos. Soc, Vol. 51, 1955, pp. 406–413. Qi, Chen, Bakshi, Zhang (b9) 2021; 42 Tucker (b12) 1966; 31 Harshman (b11) 1970; 16 Pan, Ling, He (b14) 2024; 465 Xie, Wang, Wang (b5) 2019; 27 Yu, Luo, Qi, Xu (b13) 2021; 434 Bolte, Daniilidis, Lewis (b18) 2007; 17 Xie, Wang, Wang (b3) 2018; 26 Xie, Wang, Wang (b4) 2015 Zhang, Roughan, Lund, Donoho (b15) 2005; 13 Zhou, Zhang, Xie (b6) 2015 Moore (b16) 1920; 26 Xie (10.1016/j.cam.2024.115901_b3) 2018; 26 Xie (10.1016/j.cam.2024.115901_b4) 2015 Bolte (10.1016/j.cam.2024.115901_b18) 2007; 17 Pan (10.1016/j.cam.2024.115901_b14) 2024; 465 Ming (10.1016/j.cam.2024.115901_b7) 2022; 467 Chen (10.1016/j.cam.2024.115901_b1) 2021; 88 Tucker (10.1016/j.cam.2024.115901_b12) 1966; 31 Harshman (10.1016/j.cam.2024.115901_b11) 1970; 16 Zhang (10.1016/j.cam.2024.115901_b15) 2005; 13 Roughan (10.1016/j.cam.2024.115901_b8) 2012; 20 Xie (10.1016/j.cam.2024.115901_b5) 2019; 27 Zhou (10.1016/j.cam.2024.115901_b6) 2015 Qi (10.1016/j.cam.2024.115901_b9) 2021; 42 10.1016/j.cam.2024.115901_b17 Carroll (10.1016/j.cam.2024.115901_b10) 1970; 35 Moore (10.1016/j.cam.2024.115901_b16) 1920; 26 Ming (10.1016/j.cam.2024.115901_b2) 2021; 96 Yu (10.1016/j.cam.2024.115901_b13) 2021; 434 |
| References_xml | – volume: 434 start-page: 295 year: 2021 end-page: 314 ident: b13 article-title: Slrta: A sparse and low-rank tensor-based approach to internet traffic anomaly detection publication-title: Neurocomputing – volume: 88 start-page: 65 year: 2021 ident: b1 article-title: A Barzilai–Borwein gradient algorithm for spatio-temporal internet traffic data completion via tensor triple decomposition publication-title: J. Sci. Comput. – volume: 31 start-page: 279 year: 1966 end-page: 311 ident: b12 article-title: Some mathematical notes on three-mode factor analysis publication-title: Psychometrika – volume: 467 start-page: 203 year: 2022 end-page: 213 ident: b7 article-title: An accurate and practical algorithm for internet traffic recovery problem publication-title: Neurocomputing – start-page: 2443 year: 2015 end-page: 2451 ident: b4 article-title: Sequential and adaptive sampling for matrix completion in network monitoring systems publication-title: IEEE INFOCOM – volume: 17 start-page: 1205 year: 2007 end-page: 1223 ident: b18 article-title: The Łojasiewicz inequality for nonsmooth subanalytic functions with applications to subgradient dynamical systems publication-title: SIAM J. Optim. – volume: 20 start-page: 662 year: 2012 end-page: 676 ident: b8 article-title: Spatio-temporal compressive sensing and internet traffic matrices (extended version) publication-title: IEEE/ACM Trans. Netw. – volume: 465 year: 2024 ident: b14 article-title: Low-rank and sparse enhanced tucker decomposition for tensor completion publication-title: Appl. Math. Comput. – volume: 26 start-page: 394 year: 1920 end-page: 395 ident: b16 article-title: On the reciprocal of the general algebraic matrix publication-title: Bull. Amer. Math. Soc. – volume: 27 start-page: 2222 year: 2019 end-page: 2235 ident: b5 article-title: Accurate recovery of missing network measurement data with localized tensor completion publication-title: IEEE/ACM Trans. Netw. – start-page: 1 year: 2015 end-page: 7 ident: b6 article-title: Spatio-temporal tensor completion for imputing missing internet traffic data publication-title: IEEE 34th IPCCC – volume: 42 start-page: 299 year: 2021 end-page: 329 ident: b9 article-title: Triple decomposition and tensor recovery of third order tensors publication-title: SIAM J. Matrix Anal. Appl. – volume: 35 start-page: 283 year: 1970 end-page: 319 ident: b10 article-title: Analysis of individual differences in multidimensional scaling via an n-way generalization of eckart-young decomposition publication-title: Psychometrika – volume: 96 start-page: 645 year: 2021 end-page: 656 ident: b2 article-title: An algorithm for matrix recovery of high-loss-rate network traffic data publication-title: Appl. Math. Model. – volume: 16 start-page: 1 year: 1970 end-page: 84 ident: b11 article-title: Foundations of the parafac procedure: Models and conditions for an explanatory multi-modal factor analysis publication-title: UCLA Work Pap. Phonetics – volume: 13 start-page: 947 year: 2005 end-page: 960 ident: b15 article-title: Estimating point-to-point and point-tomultipoint traffic matrices: an information-theoretic approach publication-title: IEEE/ACM Trans. Netw. – volume: 26 start-page: 793 year: 2018 end-page: 806 ident: b3 article-title: Accurate recovery of internet traffic data: A sequential tensor completion approach publication-title: IEEE/ACM Trans. Netw. – reference: R. Penrose, generalized inverse for matrices, in: Math. Proc. Camb. Philos. Soc, Vol. 51, 1955, pp. 406–413. – volume: 31 start-page: 279 year: 1966 ident: 10.1016/j.cam.2024.115901_b12 article-title: Some mathematical notes on three-mode factor analysis publication-title: Psychometrika doi: 10.1007/BF02289464 – volume: 26 start-page: 394 year: 1920 ident: 10.1016/j.cam.2024.115901_b16 article-title: On the reciprocal of the general algebraic matrix publication-title: Bull. Amer. Math. Soc. – volume: 96 start-page: 645 year: 2021 ident: 10.1016/j.cam.2024.115901_b2 article-title: An algorithm for matrix recovery of high-loss-rate network traffic data publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2021.03.036 – volume: 17 start-page: 1205 year: 2007 ident: 10.1016/j.cam.2024.115901_b18 article-title: The Łojasiewicz inequality for nonsmooth subanalytic functions with applications to subgradient dynamical systems publication-title: SIAM J. Optim. doi: 10.1137/050644641 – volume: 465 year: 2024 ident: 10.1016/j.cam.2024.115901_b14 article-title: Low-rank and sparse enhanced tucker decomposition for tensor completion publication-title: Appl. Math. Comput. – volume: 26 start-page: 793 year: 2018 ident: 10.1016/j.cam.2024.115901_b3 article-title: Accurate recovery of internet traffic data: A sequential tensor completion approach publication-title: IEEE/ACM Trans. Netw. doi: 10.1109/TNET.2018.2797094 – ident: 10.1016/j.cam.2024.115901_b17 doi: 10.1017/S0305004100030401 – volume: 88 start-page: 65 year: 2021 ident: 10.1016/j.cam.2024.115901_b1 article-title: A Barzilai–Borwein gradient algorithm for spatio-temporal internet traffic data completion via tensor triple decomposition publication-title: J. Sci. Comput. doi: 10.1007/s10915-021-01574-0 – volume: 467 start-page: 203 year: 2022 ident: 10.1016/j.cam.2024.115901_b7 article-title: An accurate and practical algorithm for internet traffic recovery problem publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.09.065 – volume: 16 start-page: 1 year: 1970 ident: 10.1016/j.cam.2024.115901_b11 article-title: Foundations of the parafac procedure: Models and conditions for an explanatory multi-modal factor analysis publication-title: UCLA Work Pap. Phonetics – volume: 35 start-page: 283 year: 1970 ident: 10.1016/j.cam.2024.115901_b10 article-title: Analysis of individual differences in multidimensional scaling via an n-way generalization of eckart-young decomposition publication-title: Psychometrika doi: 10.1007/BF02310791 – volume: 27 start-page: 2222 year: 2019 ident: 10.1016/j.cam.2024.115901_b5 article-title: Accurate recovery of missing network measurement data with localized tensor completion publication-title: IEEE/ACM Trans. Netw. doi: 10.1109/TNET.2019.2940147 – volume: 20 start-page: 662 issue: 3 year: 2012 ident: 10.1016/j.cam.2024.115901_b8 article-title: Spatio-temporal compressive sensing and internet traffic matrices (extended version) publication-title: IEEE/ACM Trans. Netw. doi: 10.1109/TNET.2011.2169424 – volume: 434 start-page: 295 year: 2021 ident: 10.1016/j.cam.2024.115901_b13 article-title: Slrta: A sparse and low-rank tensor-based approach to internet traffic anomaly detection publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.12.123 – start-page: 2443 year: 2015 ident: 10.1016/j.cam.2024.115901_b4 article-title: Sequential and adaptive sampling for matrix completion in network monitoring systems – start-page: 1 year: 2015 ident: 10.1016/j.cam.2024.115901_b6 article-title: Spatio-temporal tensor completion for imputing missing internet traffic data – volume: 42 start-page: 299 year: 2021 ident: 10.1016/j.cam.2024.115901_b9 article-title: Triple decomposition and tensor recovery of third order tensors publication-title: SIAM J. Matrix Anal. Appl. doi: 10.1137/20M1323266 – volume: 13 start-page: 947 year: 2005 ident: 10.1016/j.cam.2024.115901_b15 article-title: Estimating point-to-point and point-tomultipoint traffic matrices: an information-theoretic approach publication-title: IEEE/ACM Trans. Netw. doi: 10.1109/TNET.2005.857115 |
| SSID | ssj0006914 |
| Score | 2.427033 |
| Snippet | Network traffic data is the pivot of input in many network tasks but its direct measurement can be insufferably costly. In this paper, we propose a network... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 115901 |
| SubjectTerms | BB gradient algorithm Network traffic recovery Triple decomposition |
| Title | Network traffic recovery from link-load measurements using tensor triple decomposition strategy for third-order traffic tensors |
| URI | https://dx.doi.org/10.1016/j.cam.2024.115901 |
| Volume | 447 |
| WOSCitedRecordID | wos001226820900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0377-0427 databaseCode: AIEXJ dateStart: 20211207 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0006914 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Jb9QwFLaGlgMcEKu6UOQDJ0ZBaeJ4OVaoqCA64lCkEZcosR061TStZimdXvqH-JE8r5m2DKIHLtHI8ZKZ9439_PL5ewi9hZ1xIQV4bpQJlhBNScKFqgHLBU8JY7yprM7sFzYY8OFQfO31foWzMBdj1rb88lKc_1dTQxkY2xydvYe5Y6dQAJ_B6HAFs8P1nww_cMRuk_zByEP0zZYXvsTCnSSxav3js0r1T7vo4LQ_tyEDw2Y3rMOJpRgqbfjmntTVnzoZ24XnJY4mKrGynXEc13i6wtuVNntEiDxahVjv_55G4djo3h_6RCvfj3W7mMfYrJM7MIVXx6M7EW-bhftHKB7O7eJStT_1aDmykZFI3YonuhhLTC6Q5dmaOIFOP9-CPytckztLgYtKnMA23wgOZOR9V_em7Pat5TCSFAP_7aSELkrTRem6eIDWM1YImEPX9z7tDz_HlZ8KpyUfnju8Rbd8wlvP8Wc_aMm3OXqKnngz4T0Hpmeop9vn6PFhZ5gX6NrDCntz4wArbGCFI6zwMqywhRV2yMAOVvgGrHCAFW5MjQ5WcRwPq5fo28f9ow8HiU_ekchMsFkiGXS3m1a5VDqThdA5a7SiNasKongtM8JpTeEer2BHwXYrmlfg_hOWi4bTVOav0Fp71uoNhGvog6hUFlndmBf1nFWKUSELRQvRFGQTpeGnLKVXtjcJVsblShNuonexybmTdflbZRLsU3q_1PmbJWBtdbOt-4yxjR51f4HXaG02mesd9FBezEbTyRsPtN97ZLV1 |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Network+traffic+recovery+from+link-load+measurements+using+tensor+triple+decomposition+strategy+for+third-order+traffic+tensors&rft.jtitle=Journal+of+computational+and+applied+mathematics&rft.au=Ming%2C+Zhenyu&rft.au=Qin%2C+Zhenzhi&rft.au=Zhang%2C+Liping&rft.au=Xu%2C+Yanwei&rft.date=2024-09-01&rft.issn=0377-0427&rft.volume=447&rft.spage=115901&rft_id=info:doi/10.1016%2Fj.cam.2024.115901&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_cam_2024_115901 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0377-0427&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0377-0427&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0377-0427&client=summon |