Browsing by Subject "Traffic engineering Electronic data processing."

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Open Access
Combined use of prioritized AIMD and flow-based traffic splitting for robust TCP load balancing
(2005) Alparslan, Onur
In this thesis, we propose a multi-path TCP load balancing traffic engineering methodology in IP networks. In this architecture, TCP traffic is split at the flow level between the primary and secondary paths in order to prevent the adverse effect of packet reordering on TCP performance occuring in packet-based load balancing schemes. Traffic splitting is done by using a random early rerouting algorithm that controls the queuing delay difference between the two alternative paths. We apply strict priority queuing in order to prevent the knock-on effect that arises when primary and secondary path queues have equal priority. Probe packets are used for getting congestion information from the output queues of links along the paths and AIMD (Additive Increase/Multiplicative Decrease) based rate control using this congestion information is applied to the traffic routed over these paths. We compare two queuing architectures, namely first-in-first-out (FIFO) and strict priority. We show through simulations that strict priority queuing has higher performance, it is relatively more robust than FIFO queuing and it eliminates the knock-on effect. We show that avoiding packet reordering by flow level splitting significantly improves the performance of long flows. The capabilities of ns-2 simulator is improved bu using optimizations in order to apply the simulator to relatively large networks. We show that incorporating a-priori knowledge of the traffic demand matrix into the proposed architecture can further improve its performance in terms of load balancing and byte rejection ratio.
Open Access
Robust network design under polyhedral traffic uncertainty
(2007) Altın, Ayşegül
In this thesis, we study the design of networks robust to changes in demand estimates. We consider the case where the set of feasible demands is defined by an arbitrary polyhedron. Our motivation is to determine link capacity or routing configurations, which remain feasible for any realization in the corresponding demand polyhedron. We consider three well-known problems under polyhedral demand uncertainty all of which are posed as semi-infinite mixed integer programming problems. We develop explicit, compact formulations for all three problems as well as alternative formulations and exact solution methods. The first problem arises in the Virtual Private Network (VPN) design field. We present compact linear mixed-integer programming formulations for the problem with the classical hose traffic model and for a new, less conservative, robust variant relying on accessible traffic statistics. Although we can solve these formulations for medium-to-large instances in reasonable times using off-the-shelf MIP solvers, we develop a combined branch-and-price and cutting plane algorithm to handle larger instances. We also provide an extensive discussion of our numerical results. Next, we study the Open Shortest Path First (OSPF) routing enhanced with traffic engineering tools under general demand uncertainty with the motivation to discuss if OSPF could be made comparable to the general unconstrained routing (MPLS) when it is provided with a less restrictive operating environment. To the best of our knowledge, these two routing mechanisms are compared for the first time under such a general setting. We provide compact formulations for both routing types and show that MPLS routing for polyhedral demands can be computed in polynomial time. Moreover, we present a specialized branchand-price algorithm strengthened with the inclusion of cuts as an exact solution tool. Subsequently, we compare the new and more flexible OSPF routing with MPLS as well as the traditional OSPF on several network instances. We observe that the management tools we use in OSPF make it significantly better than the generic OSPF. Moreover, we show that OSPF performance can get closer to that of MPLS in some cases. Finally, we consider the Network Loading Problem (NLP) under a polyhedral uncertainty description of traffic demands. After giving a compact multicommodity formulation of the problem, we prove an unexpected decomposition property obtained from projecting out the flow variables, considerably simplifying the resulting polyhedral analysis and computations by doing away with metric inequalities, an attendant feature of most successful algorithms on NLP. Under the hose model of feasible demands, we study the polyhedral aspects of NLP, used as the basis of an efficient branch-and-cut algorithm supported by a simple heuristic for generating upper bounds. We provide the results of extensive computational experiments on well-known network design instances.