Efficient availability evaluation for transport backbone networks

The reliability of transport backbone networks is vital for the economy and security. Modern backbone networks use a mesh of fiber optic cables, which are, due to their ubiquitous deployment, prone to failures. The goal of this paper is to develop efficient computational methods for assessing the availability of such networks. We present both analytical and simulation approaches for this problem. Our analytical approach is based on cut set enumeration, while our simulation approach is based on Monte Carlo sampling techniques. To facilitate the computational process, we employ artificial intelligence methods based on genetic algorithms. We consider the expected loss of traffic (ELT) as the availability measure and show that ELT can be accurately estimated through an efficient procedure.

[1]  L. Jereb,et al.  Availability analysis of multi-layer optical networks , 2000 .

[2]  Michael Menth,et al.  Capacity overprovisioning for networks with resilience requirements , 2006, SIGCOMM.

[3]  Charles J. Colbourn,et al.  The Combinatorics of Network Reliability , 1987 .

[4]  Robert E. Tarjan,et al.  A quick method for finding shortest pairs of disjoint paths , 1984, Networks.

[5]  C. Singh,et al.  Adequacy Assessment of Power System Generation Using a Modified Simple Genetic Algorithm , 2002, IEEE Power Engineering Review.

[6]  Francesco Maffioli,et al.  Fishman's sampling plan for computing network reliability , 2001, IEEE Trans. Reliab..

[7]  Biswanath Mukherjee,et al.  Connection management for survivable wavelength-routed wdm mesh networks , 2001 .

[8]  Richard M. Karp,et al.  Monte-Carlo algorithms for enumeration and reliability problems , 1983, 24th Annual Symposium on Foundations of Computer Science (sfcs 1983).

[9]  Richard M. Karp,et al.  Monte-Carlo algorithms for the planar multiterminal network reliability problem , 1985, J. Complex..

[10]  L. Jereb.  Network Reliability: Models, Measures and Analysis , 1998 .

[11]  Vijay V. Vazirani,et al.  Approximation Algorithms , 2001, Springer Berlin Heidelberg.

[12]  L. Jereb,et al.  Adaptive statistical algorithms in network reliability analysis , 2002, Perform. Evaluation.

[13]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[14]  Richard M. Karp,et al.  Monte-Carlo Approximation Algorithms for Enumeration Problems , 1989, J. Algorithms.

[15]  Matthias Scheffel.  Adaptation of failure scenario based resilience schemes toward availabilityguarantees , 2006 .

[16]  David R. Karger,et al.  A randomized fully polynomial time approximation scheme for the all terminal network reliability problem , 1995, STOC '95.

[17]  Kishor S. Trivedi,et al.  A General Framework for Network Survivability Quantification , 2004, MMB.

[18]  C. Singh,et al.  A new method for composite system annualized reliability indices based on genetic algorithms , 2002, IEEE Power Engineering Society Summer Meeting,.

[19]  Mihalis Yannakakis,et al.  Suboptimal Cuts: Their Enumeration, Weight and Number (Extended Abstract) , 1992, ICALP.

[20]  Alice E. Smith,et al.  Efficient optimization of all-terminal reliable networks, using an evolutionary approach , 1997 .

[21]  Abdullah Konak,et al.  Network Reliability Optimization , 2006, Handbook of Optimization in Telecommunications.

[22]  S. Chaudhuri,et al.  OPTICAL MESH NETWORK MODELING: SIMULATION AND ANALYSIS OF RESTORATION PERFORMANCE , 2002 .

[23]  Lingfeng Wang,et al.  Role of Artificial Intelligence in the Reliability Evaluation of Electric Power Systems , 2008 .