Abstraction and constraint satisfaction techniques for planning bandwidth allocation

Communication networks are expected to offer a wide range of services to an increasingly large number of users, with a diverse range of quality of service. This calls for efficient control and management of these networks. We address the problem of quality-of-service routing, more specifically the planning of bandwidth allocation to communication demands. Shortest-path routing is the traditional technique applied to this problem. However, this can lead to poor network utilization and even congestion. We show how an abstraction technique combined with systematic search algorithms and heuristics derived from artificial intelligence make it possible to solve this problem more efficiently and in much tighter networks, in terms of bandwidth usage.

[1]  Fausto Giunchiglia,et al.  A Theory of Abstraction , 1992, Artif. Intell..

[2]  Boi Faltings,et al.  A Dynamic Hierarchy of Intelligent Agents for Network Management , 1999, IATA.

[3]  Gerald R. Ash,et al.  Dynamic Routing in Telecommunications Networks , 1997 .

[4]  Eugene C. Freuder,et al.  Interchangeability Supports Abstraction and Reformulation for Multi-Dimensional Constraint Satisfaction , 1997, AAAI/IAAI.

[5]  Eugene C. Freuder,et al.  for Multi-Dimensional Constraint Satisfaction , 1999 .

[6]  Berthe Y. Choueiry,et al.  A Decomposition Heuristic for Resource Allocation , 1994, ECAI.

[7]  Robert M. Haralick,et al.  Increasing Tree Search Efficiency for Constraint Satisfaction Problems , 1979, Artif. Intell..

[8]  Iraj Saniee,et al.  An optimization problem related to balancing loads on SONET rings , 1994, Telecommun. Syst..

[9]  R. Dechter.  to Constraint Satisfaction , 1991 .

[10]  M. Herzberg,et al.  Optimal resource allocation for path restoration in mesh-type self-healing networks , 1997 .

[11]  Boi Faltings,et al.  Structuring Techniques for Constraint Satisfaction Problems , 1997, IJCAI.

[12]  Peter C. Cheeseman,et al.  Where the Really Hard Problems Are , 1991, IJCAI.

[13]  Edward P. K. Tsang,et al.  Foundations of constraint satisfaction , 1993, Computation in cognitive science.

[14]  Jon Crowcroft,et al.  Quality-of-Service Routing for Supporting Multimedia Applications , 1996, IEEE J. Sel. Areas Commun..

[15]  Tsong-Ho Wu,et al.  Fiber Network Service Survivability , 1992 .

[16]  Mark S. Miller,et al.  An automated auction in ATM network bandwidth , 1996 .

[17]  Toby Walsh,et al.  Symposium on Abstraction, Reformulation, and Approximation (SARA-2000) , 2001, AI Mag..

[18]  Toby Walsh,et al.  Phase Transitions from Real Computational Problems , 1995 .

[19]  Patrick Prosser,et al.  HYBRID ALGORITHMS FOR THE CONSTRAINT SATISFACTION PROBLEM , 1993, Comput. Intell..

[20]  Toby Walsh,et al.  Scaling Effects in the CSP Phase Transition , 1995, CP.

[21]  S. Clearwater.  Market-based control: a paradigm for distributed resource allocation , 1996 .

[22]  S. Sitharama Iyengar,et al.  The Bandwidth Allocation Problem in the ATM Network Model is NP-Complete , 1998, Inf. Process. Lett..