Shortest path versus multi-hub routing in networks with uncertain demand

We study a class of robust network design problems motivated by the need to scale core networks to meet increasingly dynamic capacity demands. Past work has focused on designing the network to support all hose matrices (all matrices not exceeding marginal bounds at the nodes). This model may be too conservative if additional information on traffic patterns is available. Another extreme is the fixed demand model, where one designs the network to support peak point-to-point demands. We introduce a capped hose model to explore a broader range of traffic matrices which includes the above two as special cases. It is known that optimal designs for the hose model are always determined by single-hub routing, and for the fixed-demand model are based on shortest-path routing. We shed light on the wider space of capped hose matrices in order to see which traffic models are more shortest path-like as opposed to hub-like. To address the space in between, we use hierarchical multi-hub routing templates, a generalization of hub and tree routing. In particular, we show that by adding peak capacities into the hose model, the single-hub tree-routing template is no longer cost-effective. This initiates the study of a class of robust network design (RND) problems restricted to these templates. Our empirical analysis is based on a heuristic for this new hierarchical RND problem. We also propose that it is possible to define a routing indicator that accounts for the strengths of the marginals and peak demands and use this information to choose the appropriate routing template. We benchmark our approach against other well-known routing templates, using representative carrier networks and a variety of different capped hose traffic demands, parameterized by the relative importance of their marginals as opposed to their point-to-point peak demands. This study also reveals conditions under which multi-hub routing gives improvements over single-hub and shortest-path routings.

[1]  Amit Kumar,et al.  Algorithms for provisioning virtual private networks in the hose model , 2001, SIGCOMM.

[2]  Ellen W. Zegura,et al.  Avoiding Oscillations Due to Intelligent Route Control Systems , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[3]  Tim Roughgarden,et al.  Simpler and better approximation algorithms for network design , 2003, STOC '03.

[4]  Leslie G. Valiant,et al.  A Scheme for Fast Parallel Communication , 1982, SIAM J. Comput..

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

[6]  Yuval Rabani,et al.  ON THE HARDNESS OF APPROXIMATING MULTICUT AND SPARSEST-CUT , 2005, 20th Annual IEEE Conference on Computational Complexity (CCC'05).

[7]  Nick McKeown,et al.  Scaling internet routers using optics , 2003, SIGCOMM '03.

[8]  T. C. Hu.  Optimum Communication Spanning Trees , 1974, SIAM J. Comput..

[9]  David Peleg,et al.  Deterministic Polylog Approximation for Minimum Communication Spanning Trees , 1998, ICALP.

[10]  Neil Olver,et al.  Approximability of robust network design , 2010, SODA '10.

[11]  Sudipta Sengupta,et al.  Efficient and robust routing of highly variable traffic , 2005 .

[12]  Navin Goyal,et al.  The VPN Conjecture Is True , 2013, JACM.

[13]  N. McKeown,et al.  Designing a Predictable Internet Backbone Network , 2004 .

[14]  Albert G. Greenberg,et al.  Fast accurate computation of large-scale IP traffic matrices from link loads , 2003, SIGMETRICS '03.

[15]  Marina Thottan,et al.  Shortest Path Versus Multihub Routing in Networks With Uncertain Demand , 2015, IEEE/ACM Transactions on Networking.

[16]  Monia Ghobadi,et al.  Resource optimization algorithms for virtual private networks using the hose model , 2008, Comput. Networks.

[17]  Albert G. Greenberg,et al.  Resource management with hoses: point-to-cloud services for virtual private networks , 2002, TNET.

[18]  Anwar Elwalid,et al.  Exploiting parallelism to boost data-path rate in high-speed IP/MPLS networking , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

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

[20]  Navin Goyal,et al.  Dynamic vs. Oblivious Routing in Network Design , 2010, Algorithmica.

[21]  Subhash Suri,et al.  Designing Least-Cost Nonblocking Broadband Networks , 1997, J. Algorithms.

[22]  Satish Rao,et al.  A tight bound on approximating arbitrary metrics by tree metrics , 2003, STOC '03.

[23]  N. Linial,et al.  Expander Graphs and their Applications , 2006 .

[24]  Amit Kumar,et al.  Provisioning a virtual private network: a network design problem for multicommodity flow , 2001, STOC '01.

[25]  Satish Rao,et al.  Expander flows, geometric embeddings and graph partitioning , 2004, STOC '04.

[26]  Martin Skutella,et al.  A short proof of the VPN Tree Routing Conjecture on ring networks , 2008, Oper. Res. Lett..

[27]  Debasis Mitra,et al.  Randomized parallel communications on an extension of the omega network , 1987, JACM.

[28]  F. B. Shepherd,et al.  Selective randomized load balancing and mesh networks with changing demands , 2006 .

[29]  Zhi-Li Zhang,et al.  YouTube traffic dynamics and its interplay with a tier-1 ISP: an ISP perspective , 2010, IMC '10.

[30]  Walter Willinger,et al.  The many facets of internet topology and traffic , 2006, Networks Heterog. Media.