Compressed network monitoring for ip and all-optical networks

ABSTRACT

We address the problem of efficient end-to-end network monitoring of path metrics in communication networks. Our goal is to minimize the number of measurements or monitors required to maintain an acceptable estimation accuracy. We present a framework based on diffusion wavelets and nonlinear estimation. Our procedure involves the development of a diffusion wavelet basis that is adapted to the monitoring problem. This basis exploits spatial and temporal correlations in the measured phenomena to provide a compressible representation of the path metrics. The framework employs nonlinear estimation techniques using l₁ minimization to generate estimates for the unmeasured paths. We describe heuristic approaches for the selection of the paths that should be monitored, or equivalently, where hardware monitors should be located. We demonstrate how our estimation framework can improve the efficiency of end-to-end delay estimation in IP networks and reduce the number of hardware monitors required to track bit-error rates in all-optical networks (networks with no electrical regenerators).

REFERENCES

• 1: G. Agrawal. Fiber-Optic Communications Systems. John Wiley & Sons, Inc., third edition, 2002.
• 2: E. Candès, J. Romberg, and T. Tao. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory, 52(2):489--509, Feb. 2006.
• 3: Yan Chen , David Bindel , Hanhee Song , Randy H. Katz, An algebraic approach to practical and scalable overlay network monitoring, Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications, August 30-September 03, 2004, Portland, Oregon, USA
• 4: I. Chlamtac, A. Ganz, and G. Karmi. Lightpath communications: a novel approach to high bandwidth optical WANs. IEEE Trans.Commun., 40(7):1171--1182, July 1992.
• 5: D. Chua, E. Kolaczyk, and M. Crovella. Efficient monitoring of end-to-end network properties. In Proc. Infocom, Miami, USA, Mar. 2005.
• 6: M. Coates, A. Hero, R. Nowak, and B. Yu. Internet tomography. IEEE Signal Processing Mag., May 2002.
• 7: R. Coifman and M. Maggioni. Diffusion wavelets. Applied and Computational Harmonic Analysis, 21(1):53--94, July 2006.
• 8: M. Crovella and E. Kolaczyk. Graph wavelets for spatial traffic analysis. In Proc. IEEE Infocom, San Francisco, USA, Mar. 2003.
• 9: Tao Deng , Suresh Subramaniam , Jinghao Xu, Crosstalk-Aware Wavelength Assignment in Dynamic Wavelength-Routed Optical Networks, Proceedings of the First International Conference on Broadband Networks, p.140-149, October 25-29, 2004 [doi>10.1109/BROADNETS.2004.26]
• 10: P. D. Dobbelaere, K. Falta, L. Fan, S. Gloekner, and S. Patra. Digital MEMS for optical switching. IEEE Commun. Mag., pages 88--95, Mar. 2002.
• 11: D. Donoho. Compressed sensing. IEEE Trans. Inform. Theory, 52(4):1289--1306, Apr. 2006.
• 12: E. Goldstein and L. Eskildsen. Scaling limitations in transparent optical networks due to low-level crosstalk. IEEE Photon. Technol. Lett., 7(1):93--94, Jan. 1995.
• 13: Gene H. Golub , Charles F. Van Loan, Matrix computations (3rd ed.), Johns Hopkins University Press, Baltimore, MD, 1996
• 14: J. Haupt and R. Nowak. Signal reconstruction from noisy random projections. IEEE Trans. Inform. Theory, 52(9):4036--4048, Sept. 2006.
• 15: Y. Pointurier, M. Brandt-Pearce, T. Deng, and S. Subramaniam. Fair QoS-aware adaptive Routing and Wavelength Assignment in all-optical networks. In Proc. IEEE ICC, June 2006.
• 16: B. Ramamurthy, D. Datta, H. Feng, J. Heritage, and B. Mukherjee. Impact of transmission impairments on the teletraffic performance of wavelength-routed optical networks. J. Lightwave Technol., 17(10):1713--1723, Oct. 1999.
• 17: Rajiv Ramaswami , Kumar N. Sivarajan, Optical networks: a practical perspective, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2001
• 18: R. Sinkhorn. A relationship between arbitrary positive matrices and double stochastic matrices. Ann. Mathematical Statistics, 35(2):876--879, June 1964.
• 19: J. Tenenbaum, V. de Silva, and J. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 5500(290):2319--2323, Dec. 2000.
• 20: Y. Vardi. Network tomography: Estimating source-destination traffic intensities from link data. J. American Statistical Assosciation, 91(433):365--377, Mar. 1996.
• 21: A. Willner, M. Cardakli, O. Adamczyk, Y.-W. Song, and D. Gurkan. Key building blocks for all-optical networks. IEICE Trans. Commun., E83-B:2166--2177, Oct. 2000.
• 22: B. Xu and M. Brandt-Pearce. Comparison of FWM- and XPM-induced crosstalk using the Volterra Series Transfer Function method. J. Lightwave Technol., 21(1):40--53, Jan. 2003.
• 23: B. Xu and M. Brandt-Pearce. Analysis of noise amplification by a CW pump signal due to fiber nonlinearity. IEEE Photon. Technol. Lett., 16(4):1062--1064, Apr. 2004.

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS
      C.2.3 Network Operations
          Subjects: Network monitoring

Additional Classification:
  E. Data
  E.4 CODING AND INFORMATION THEORY
      Subjects: Data compaction and compression

General Terms:
Algorithms, Measurement, Performance

Keywords:
compressed sensing, diffusion wavelets, network monitoring