Making intra-domain routing robust to changing and uncertain traffic demands: understanding fundamental tradeoffs

Intra-domain traffic engineering can significantly enhance the performance of large IP backbone networks. Two important components of traffic engineering are understanding the traffic demandsand configuring the routing protocols. These two components are inter-linked, as it is widely believed that an accurate view of traffic is important for optimizing the configuration of routing protocols and through that, the utilization of the network.This basic premise, however, never seems to have been quantified --How important is accurate knowledge of traffic demands for obtaining good utilization of the network? Since traffic demand values are dynamic and illusive, is it possible to obtain a routing that is "robust" to variations in demands? Armed with enhanced recent algorithmic tools we explore these questions on a diverse collection of ISP networks. We arrive at a surprising conclusion: it is possible to obtain a robust routing that guarantees a nearly optimal utilization with a fairly limited knowledge of the applicable traffic demands.

[1]  B. Yu,et al.  Time-varying network tomography: router link data , 2000, 2000 IEEE International Symposium on Information Theory (Cat. No.00CH37060).

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

[3]  Daniel O. Awduche,et al.  Requirements for Traffic Engineering Over MPLS , 1999, RFC.

[4]  Kavé Salamatian,et al.  Traffic matrix estimation: existing techniques and new directions , 2002, SIGCOMM '02.

[5]  Anja Feldmann,et al.  Deriving traffic demands for operational IP networks: methodology and experience , 2000, SIGCOMM.

[6]  Edith Cohen,et al.  Optimal oblivious routing in polynomial time , 2003, STOC '03.

[7]  Albert G. Greenberg,et al.  Experience in measuring backbone traffic variability: models, metrics, measurements and meaning , 2002, IMW '02.

[8]  Christophe Diot,et al.  Geographical and temporal characteristics of inter-POP flows: View from a single pop , 2002, Eur. Trans. Telecommun..

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

[10]  J. G. Pierce,et al.  Geometric Algorithms and Combinatorial Optimization , 2016 .

[11]  Nick G. Duffield,et al.  Trajectory sampling for direct traffic observation , 2000, TNET.

[12]  Narendra Karmarkar,et al.  A new polynomial-time algorithm for linear programming , 1984, Comb..

[13]  Debasis Mitra,et al.  A case study of multiservice, multipriority traffic engineering design for data networks , 1999, Seamless Interconnection for Universal Services. Global Telecommunications Conference. GLOBECOM'99. (Cat. No.99CH37042).

[14]  Ratul Mahajan,et al.  Measuring ISP topologies with Rocketfuel , 2004, IEEE/ACM Transactions on Networking.

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

[16]  Mikkel Thorup,et al.  Internet traffic engineering by optimizing OSPF weights , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).