Demand-Oblivious Routing: Distributed vs. Centralized Approaches

Until recent years, it was more or less undisputed common-sense that an accurate view on traffic demands is indispensable for optimizing the flow of traffic through a network. Lately, this premise has been questioned sharply: it was shown that setting just a single routing, the so called demand-oblivious routing, is sufficient to accommodate any admissible traffic matrix in the network with moderate link overload, so no prior information on demands is absolutely necessary for efficient traffic engineering. Demand-oblivious routing lends itself to distributed implementations, so it scales well. In this paper, we generalize demand-oblivious routing in a new way: we show that, in contrast to the distributed case, centralized demand-oblivious routing can eliminate link overload completely. What is more, our centralized scheme allows for optimizing the routes with respect to arbitrary linear or quadratic objective function. We realize, however, that a centralized scheme can become prohibitively complex, therefore, we propose a hybrid distributed-centralized algorithm, which, according to our simulations, strikes a good balance between efficiency, scalability and complexity.

[1]  Hans-Werner Braun,et al.  The National Science Foundation Network , 1992 .

[2]  Harald Räcke,et al.  Minimizing Congestion in General Networks , 2002, FOCS.

[3]  Gábor Rétvári,et al.  On optimal multipath rate-adaptive routing , 2010, The IEEE symposium on Computers and Communications.

[4]  Alberto Bemporad,et al.  The explicit linear quadratic regulator for constrained systems , 2003, Autom..

[5]  Dimitri P. Bertsekas,et al.  Dynamic behavior of shortest path routing algorithms for communication networks , 1982 .

[6]  Srikanth Kandula,et al.  Walking the tightrope: responsive yet stable traffic engineering , 2005, SIGCOMM '05.

[7]  Tor Arne Johansen,et al.  Approximate explicit constrained linear model predictive control via orthogonal search tree , 2003, IEEE Trans. Autom. Control..

[8]  Mohammad Taghi Hajiaghayi,et al.  Oblivious routing in directed graphs with random demands , 2005, STOC '05.

[9]  Subhash Suri,et al.  Profile-Based Routing: A New Framework for MPLS Traffic Engineering , 2001, QofIS.

[10]  Thomas Voice,et al.  Stability of end-to-end algorithms for joint routing and rate control , 2005, CCRV.

[11]  Edith Cohen,et al.  Optimal oblivious routing in polynomial time , 2004, J. Comput. Syst. Sci..

[12]  A. Robert Calderbank,et al.  Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures , 2007, Proceedings of the IEEE.

[13]  Jonathan Wellons,et al.  Oblivious Routing for Wireless Mesh Networks , 2008, 2008 IEEE International Conference on Communications.

[14]  Edith Cohen,et al.  Coping with network failures: routing strategies for optimal demand oblivious restoration , 2004, SIGMETRICS '04/Performance '04.

[15]  Mung Chiang,et al.  Towards Robust Multi-Layer Traffic Engineering: Optimization of Congestion Control and Routing , 2007, IEEE Journal on Selected Areas in Communications.

[16]  CohenEdith,et al.  Coping with network failures , 2004 .

[17]  Deep Medhi.  Multi-hour, multi-traffic class network design for virtual path-based dynamically reconfigurable wide-area ATM networks , 1995, TNET.

[18]  Anja Feldmann,et al.  REPLEX: dynamic traffic engineering based on wardrop routing policies , 2006, CoNEXT '06.

[19]  Tibor Cinkler,et al.  Fairness in Capacitated Networks: A Polyhedral Approach , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[20]  Edith Cohen,et al.  Making intra-domain routing robust to changing and uncertain traffic demands: understanding fundamental tradeoffs , 2003, SIGCOMM '03.

[21]  Yin Zhang,et al.  COPE: traffic engineering in dynamic networks , 2006, SIGCOMM 2006.

[22]  Mikkel Thorup,et al.  Traffic engineering with traditional IP routing protocols , 2002, IEEE Commun. Mag..

[23]  M. Morari,et al.  Geometric Algorithm for Multiparametric Linear Programming , 2003 .

[24]  G. Ziegler.  Lectures on Polytopes , 1994 .

[25]  Ratul Mahajan,et al.  Inferring link weights using end-to-end measurements , 2002, IMW '02.

[26]  William J. Dally,et al.  Worst-case Traffic for Oblivious Routing Functions , 2002, IEEE Comput. Archit. Lett..

[27]  Stephen P. Boyd,et al.  Throughput-centric routing algorithm design , 2003, SPAA '03.

[28]  Thomas P. Hayes,et al.  Minimizing average latency in oblivious routing , 2008, SODA '08.

[29]  Mikkel Thorup,et al.  Traffic engineering with estimated traffic matrices , 2003, IMC '03.

[30]  Donald F. Towsley,et al.  On optimal routing with multiple traffic matrices , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[31]  Adam Meyerson,et al.  Online oblivious routing , 2003, SPAA '03.

[32]  D. Towsley,et al.  Combining Multipath Routing and Congestion Control for Robustness , 2006, 2006 40th Annual Conference on Information Sciences and Systems.

[33]  KellyFrank,et al.  Stability of end-to-end algorithms for joint routing and rate control , 2005 .

[34]  Mario Gerla,et al.  Optimal Routing in a Packet-Switched Computer Network , 1974, IEEE Transactions on Computers.

[35]  Mikkel Thorup,et al.  Optimizing OSPF/IS-IS weights in a changing world , 2002, IEEE J. Sel. Areas Commun..

[36]  Janelle J. Harms,et al.  Stable and Robust Multipath Oblivious Routing for Traffic Engineering , 2007, International Teletraffic Congress.

[37]  Hao Che,et al.  Adaptive control algorithms for decentralized optimal traffic engineering in the Internet , 2004, IEEE/ACM Transactions on Networking.

[38]  Angela L. Chiu,et al.  Overview and Principles of Internet Traffic Engineering , 2002, RFC.