Large-Scale Characterization and Segmentation of Internet Path Delays with Infinite HMMs
| Autor: | Maxime Mouchet, Sandrine Vaton, Thierry Chonavel, Emile Aben, Jasper Den Hertog |
|---|---|
| Přispěvatelé: | Département Informatique (IMT Atlantique - INFO), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Lab-STICC_IMTA_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), Département Signal et Communications (IMT Atlantique - SC), RIPE NCC |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Networking and Internet Architecture (cs.NI)
FOS: Computer and information sciences hidden Markov models Machine Learning (stat.ML) Round-trip times anomaly detection Computer Science - Networking and Internet Architecture [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Statistics - Machine Learning RIPE Atlas lcsh:Electrical engineering. Electronics. Nuclear engineering nonparametric Bayesian models lcsh:TK1-9971 time series clustering |
| Zdroj: | IEEE Access IEEE Access, IEEE, 2020, 8, pp.16771-16784. ⟨10.1109/ACCESS.2020.2968380⟩ IEEE Access, Vol 8, Pp 16771-16784 (2020) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/ACCESS.2020.2968380⟩ |
| Popis: | Round-Trip Times are one of the most commonly collected performance metrics in computer networks. Measurement platforms such as RIPE Atlas provide researchers and network operators with an unprecedented amount of historical Internet delay measurements. It would be very useful to automate the processing of these measurements (statistical characterization of paths performance, change detection, recognition of recurring patterns, etc.). Humans are pretty good at finding patterns in network measurements but it can be difficult to automate this to enable many time series being processed at the same time. In this article we introduce a new model, the HDP-HMM or infinite hidden Markov model, whose performance in trace segmentation is very close to human cognition. This is obtained at the cost of a greater complexity and the ambition of this article is to make the theory accessible to network monitoring and management researchers. We demonstrate that this model provides very accurate results on a labeled dataset and on RIPE Atlas and CAIDA MANIC data. This method has been implemented in Atlas and we introduce the publicly accessible Web API. |
| Databáze: | OpenAIRE |
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