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Predictive modeling in a VoIP system

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
An important problem one needs to deal with in a Voice over IP system is server overload. One way for preventing such problems is to rely on prediction techniques for the incoming traffic, namely as to proactively scale the available resources. Anticipating the computational load induced on processors by incoming requests can be used to optimize load distribution and resource allocation. In this study, the authors look at how the user profiles, peak hours or call patterns are shaped for a real system and, in a second step, at constructing a model that is capable of predicting trends.
Słowa kluczowe
Rocznik
Tom
Strony
32--40
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
  • Computer Science and Communications University of Luxembourg, Luxembourg
autor
  • Computer Science and Communications University of Luxembourg, Luxembourg
autor
  • Computer Science and Communications University of Luxembourg, Luxembourg
autor
  • MIXvoip S.a, Luxembourg
Bibliografia
  • [1] T. C.Wilcox Jr., “Dynamic load balancing of virtual machines hosted on Xen”, Master thesis, Dept. of Computer Science, Brigham Young University, USA, April 2009.
  • [2] Mixvoip Home page [Online]. Available: http://www.mixvoip.com/
  • [3] L. Madsen, R. Bryant, and J. V. Meggelen, Asterisk: The Definitive Guide, 3rd edition. O’Reilly Media, 2011.
  • [4] A. Ganapathi, C. Yanpei, A. Fox, R. Katz, D. Patterson, “Statisticsdriven workload modeling for the Cloud”, in Proc. IEEE 26th Int. Conf. Data Engin. Worksh. ICDEW 2010, Long Beach, CA, USA, 2010, pp. 87–92.
  • [5] S. Kim, J.-I. Koh, Y. Kim, and C. Kim, “A science Cloud resource provisioning model using statistical analysis of job history”, in Proc. 9th IEEE Int. Conf. Depend. Autonom. Sec. Comput. DASC 2011, Los Alamitos, CA, USA, 2011, pp. 792–793.
  • [6] M. Armbrust et al., “Above the clouds: A Berkeley view of cloud computing”, Tech. Report no. UCB/EECS-2009-28, Electrical Engineering and Computer Sciences University of California, Berkeley, USA, 2009.
  • [7] D. F. Parkhill, The challenge of the computer utility. Reading: Addison-Wesley, 1966.
  • [8] V. Stantchev and C. Schrpfer, “Negotiating and enforcing qos and slas in grid and cloud computing”, in Advances in Grid and Per- vasive Computing, N. Abdennadher and D. Petcu, Eds. LNCS, vol. 5529. Berlin-Heidelberg: Springer, 2009, pp. 25–35.
  • [9] Amazon Elastic Compute Cloud (Amazon EC2) [Online]. Available: http://aws.amazon.com/ec2/
  • [10] Google Cloud Platform [Online]. Available: https://cloud.google.com/
  • [11] J. Kołodziej and S. U. Khan, “Multi-level hierarchical genetic-based scheduling of independent jobs in dynamic heterogeneous grid environment”, Information Sciences, vol. 214, pp. 1–19, 2012.
  • [12] A. Mahjoub, J. E. Pecero S´anchez, and D. Trystram, “Scheduling with uncertainties on new computing platforms”, J. Comp. Opt. and Appl., vol. 48, no. 2, pp. 369–398, 2011.
  • [13] L. Ding, “Speech quality prediction in VoIP using the extended E-model”, in Proc. IEEE Global Telecom. Conf. GLOBECOM 2003, San Francisko, USA, 2003, vol. 7, pp. 3974–3978.
  • [14] A. Raake, Speech Quality of VoIP: Assessment and Prediction. Chichester: Wiley, 2006.
  • [15] M. AL-Akhras, H. Zedan, R. John, and I. ALMomani, “Non-intrusive speech quality prediction in VoIP networks using a neural network approach”, Neurocomput., vol. 72, iss. 10–12, pp. 2595–2608, 2009.
  • [16] L. Sun and E. C. Ifeachor, “Voice quality prediction models and their application in VoIP networks”, IEEE Trans. Multim., vol. 8, no. 4, pp. 809–820, 2006.
  • [17] R. Estepa, “Accurate prediction of VoIP traffic mean bit rate”, Elec. Lett., vol. 41, pp. 985–987, 2005.
  • [18] M. R. H. Mandjes, I. Saniee, and A. Stolyar, “Load characterization, overload prediction, and anomaly detection for voice over IP traffic”, in Proc. ACM SIGMETRICS Int. Conf. Measur. Model. Comp. Sys., Cambridge, MA, USA, 2001, pp. 326–327.
  • [19] P. Del Moral and A. Doucet, “Particle methods: An introduction with applications”, LNCS/LNAI Tutorial book no. 6368. Springer, 2010–2011.
  • [20] P. Del Moral, A.-A. Tantar, and E. Tantar, “On the foundations and the applications of evolutionary computing”, in EVOLVE – A Bridge between Probability, Set Oriented Numerics and Evolutionary Com- putation, E. Tantar et al., Eds. Studies in Computational Intelligence, vol. 447. Springer, 2013, pp. 3–89.
  • [21] S. W. Nydick, “The Wishart and Inverse Wishart Distributions, 2012 [Online]. Available: http://www.math.wustl.edu/_sawyer/hmhandouts/Wishart.pdf
  • [22] I. Aleksander and H. Morton, An Introduction to Neural Computing. London: Chapman and Hall, 1990.
  • [23] M. Budinich and E. Milotti, “Properties of feedforward neural networks”, J. Phys. A: Mathem. Gen., vol. 25, no. 7, 1992.
  • [24] D. Svozil, V. Kvasnicka, and J. Pospichal, “Introduction to multilayer feed-forward neural networks”, Chemometrics Intell. Lab. Sys., no. 39, pp. 43–62, 1997.
  • [25] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines. New York: Cambridge University Press, 2000.
  • [26] T. Joachims, “Text categorization with Support Vector Machines: Learning with many relevant features”, in Proc. 10th European Conf. Machine Learn. ECML’98, Lecture Notes in Computer Science, vol. 1398. Springer, 1998, pp. 137–142.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-67cdda8f-9498-4fd9-b306-85761fbbd887
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