Abstract
Anomaly detection and characterization is a main topic for network managers. Although quality-of-service (QoS) indicators can help to infer problem occurrence, they do not provide immediate insight on the user’s perceived quality. Evolved service-level agreements (SLA) will likely be established in terms of quality of experience (QoE) indicators. QoS metrics composing the QoE indicators need to be monitored on a real-time basis by the SLA management tools in order to detect anomalies driving to contract violations. Monitoring SLA contracts may involve the surveillance of individual application sessions for several users. In this work, we address the problem of anomaly detection with impact on a relatively large number of users, either on one or on several types of applications simultaneously. We propose a method to characterize the state of the network, representing QoE indicators as time series and reducing the dimension of the data set. The singular spectrum analysis (SSA) method, using a combination of geometric and statistical methods, is proposed as an analysis tool in order to detect anomalies on QoE indicator evolution.
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References
Abarbanel H (1995) Analysis of observed chaotic data. Institute for Nonlinear Science, Springer
Ben-Gal I (2005) Outlier detection. Kluwer Academic, Dordrecht
Chen Z, Nakazato H (2005) A model for time-varying quality of speech services. In: IEEE Globecom 2005
Clark A (2001) Modeling the effects of burst packet loss and recency on subjective voice quality. In: Internet telephony workshop (IPtel)
Goljandina N, Nekrutkin V, Zhigljavsky A (2001) Analysis of time series structure: SSA and related techniques. Chapman and Hall, London
Gu Y, McCallum A, Towsley D (2005) Anomalies in network traffic using maximum entropy estimation. In: ACM internet measurement conference
Hassani H (2007) Singular spectrum analysis: methodology and comparison. J Data Sci (5):239–257
He AP, Wang J, Qin SJ (2004) A new fault diagnosis method using fault direction in fisher discriminant analysis. Tech. Rep. Tech. Report TWMCC-2004-05, Univ. Texas-Wisconsin
He AP, Wang J, Qin SJ (2005) Network anomography. In: ACM internet measurement conference
ITU-T Recommendation (2001) Dégradation de la transmission dues au traîtement vocal. G.113
ITU-T Recommendation (2005) Evaluation de la qualité de fonctionnement de bout en bout dans les réseaux IP pour les applications de transmission de données. G.1030
ITU-T Recommendation (2005) Temps de transmission dans un sens. G.114
Kantz H, Schreiber T (1997) Nonlinear time series analysis. Cambridge University Press, Cambridge
Kilkki K (2008) Quality of experience in communication ecosystems. J Univers Comput Sci 14(5):615–624
Lakhina A, Crovella M, Diot C (2004) Characterization of network-wide anomalies in traffic flows. In: Proc. of the 2004 conference on applications, technologies, architectures, and protocols for computer communications
Lakhina A, Crovella M, Diot C (2004) Mining anomalies using traffic feature distributions. ACM SIGCOMM Comput Commun Rev 35(4)
Markopoulos AP, Tobagi FA, Karam MJ (2003) Assessing the quality of voice communications over internet backbones. IEEE/ACM Trans Netw 11(5)
Moskvina V, Zhigljavsky A (2003) Change-point detection algorithm based on the singular-spectrum analysis. Commun Stat Simul Comput (32):319–352
Perkins ME, Evans K, Pascal D, Thorpe LA (1997) Characterizing the subjective performance of the ITU-T 8 kb/s speech coding algorithm - ITU-T G.729. IEEE Commun Mag
Reichel P (2007) From ‘quality-of-service’ and ‘quality-of-design’ to ‘quality-of-experience’: a holistic view on future interactive telecommunication services. In: IEEE ComSoc international conference on software, telecommunications and computer networks (SoftCOM’07), Split, Croatia
Renaud O, Starck J-L, Murtagh F (2005) Wavelet-based combined signal filtering and prediction. IEEE Trans Syst Man Cybern 35(6):1241–1251
Shensa MJ (1992) The discrete wavelet transform: wedding the à trous and mallat algorithms. IEEE Trans Signal Process 40(10):2464–2482
Tzagkarakis G, Papadopouli M, Tsakalides P (2007) Singular spectrum analysis of traffic workload in a large-scale wireless lan. In: 10-th ACM/IEEE international symposium on modeling, analysis and simulation of wireless and mobile systems, Chania
Yiou P, Sornette D, Gill M (2000) Data-adaptive wavelets and multiscale singular spectrum analysis. Physica D 142:254–290
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Guyard, F., Beker, S. Towards real-time anomalies monitoring for QoE indicators. Ann. Telecommun. 65, 59–71 (2010). https://doi.org/10.1007/s12243-009-0148-4
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DOI: https://doi.org/10.1007/s12243-009-0148-4