Philippe Mahey

A new proximal decomposition algorithm for routing in telecommunication networks

We present a new and much more efficient implementation of the proximal decomposition algorithm for routing in congested telecommunication networks. The routing model that we analyze is a static one intended for use as a subproblem in a network design context. After describing our new implementation of the proximal decomposition algorithm and reviewing the flow deviation algorithm, we compare the solution times for (1) the original proximal decomposition algorithm, (2) our new implementation of the proximal decomposition algorithm, and (3) the flow deviation algorithm. We report extensive computational comparisons of solution times using actual and randomly generated networks. These results show that our new proximal decomposition algorithm is substantially faster than the earlier proximal decomposition algorithm in every case. Our new proximal decomposition is also faster than the flow deviation algorithm if the network is not too congested and a highly accurate solution is desired, such as one within 0.1% of optimality. For moderate accuracy requirements, such as 1.0% optimality, and for congested networks, the flow deviation algorithm is faster. More importantly, solutions that we obtained from the proximal decomposition algorithm always involve flow on only a small number of routes between source-destination pairs. The flow deviation algorithm, however, can produce solutions with flows on a very large number of different routes between individual source-destination pairs.

Variable neighborhood descent with iterated local search for routing and wavelength assignment

In this work we treat the Routing and Wavelength Assignment (RWA) with focus on minimizing the number of wavelengths to route demand requests. Lightpaths are used to carry the traffic optically between origin-destination pairs. The RWA is subjected to wavelength continuity constraints, and a particular wavelength cannot be assigned to two different lightpaths sharing a common physical link. We develop a Variable Neighborhood Descent (VND) with Iterated Local Search (ILS) for the problem. In a VND phase we try to rearrange requests between subgraphs associated to subsets of a partition of the set of lightpath requests. In a feasible solution, lightpaths belonging to a subset can be routed with the same wavelength. Thus, the purpose is to eliminate one subset of the partition. When VND fails, we perform a ILS phase to disturb the requests distribution among the subsets of the partition. An iteration of the algorithm alternates between a VND phase and a ILS phase. We report computational experiments that show VND-ILS was able to improve results upon powerful methods proposed in the literature.

Simple bounds and greedy algorithms for decomposing a flow into a minimal set of paths

Given arbitrary source and target nodes s, t and an s–t-flow defined by its flow-values on each arc of a network, we consider the problem of finding a decomposition of this flow with a minimal number of s–t-paths. This problem is issued from the engineering of telecommunications networks for which the task of implementing a routing solution consists in integrating a set of end-to-end paths. We show that this problem is NP-hard in the strong sense and give some properties of an optimal solution. We then propose upper and lower bounds for the number of paths in an optimal solution. Finally we develop two heuristics based on the properties of a special set of solutions called saturating solutions.

Bounds for global optimization of capacity expansion and flow assignment problems

This paper provides new bounds related to the global optimization of the problem of mixed routing and bandwidth allocation in telecommunication systems. The combinatorial nature of the problem, related to arc expansion decisions, is embedded in a continuous objective function that encompasses congestion and investment line costs. It results in a non-convex multicommodity flow problem, but we explore the separability of the objective function and the fact that each associated arc cost function is piecewise-convex. Convexifying each arc cost function enables the use of efficient algorithms for convex multicommodity flow problems, and we show how to calculate sharp bounds for the approximated solutions.

Iterative improvement methods for a multiperiod network design problem

We address an important problem in telecommunications planning: the multiperiod network expansion problem. Our aim is to show that it can be efficiently solved using a new local search approach. To achieve our goal, we first show how to adapt the problems formulation to meet our local search programs requirements. We then describe GLIT (Generic Local Improvement Template), a new, generic auto-calibrating local search algorithm; the different neighbouring strategies that we designed specifically for this problem; as well as a Genetic algorithm for this problem. We compare and discuss the performance of these algorithms using extensive computational tests on a large range of instances with up to 7500 arcs. These experiments show that GLIT clearly outperforms competitive approaches, especially when it is coupled with Genetic algorithms. We also show that the hybrid algorithms Genetic/Tabu, Genetic/Simulated Annealing, and finally Genetic/GLIT all outperform both pure Genetic and pure local search algorithms.

Minimum convex-cost tension problems on series-parallel graphs

We present briefly some results we obtained with known methods to solve minimum cost tension problems, comparing their performance on non-specific graphs and on series-parallel graphs. These graphs are shown to be of interest to approximate many tension problems, like synchronization in hypermedia documents. We propose a new aggregation method to solve the minimum convex piecewise linear cost tension problem on series-parallel graphs in O(m 3 ) operations.

Local optimality conditions for multicommodity flow problems with separable piecewise convex costs

We consider here a multicommodity flow network optimization problem with non-convex but piecewise convex arc cost functions. We derive complete optimality conditions for local minima based on negative-cost cycles associated with each commodity. These conditions do not extend to the convex non-smooth case.

A minimum mean cycle cancelling method for nonlinear multicommodity flow problems

We propose a new method based on minimum mean cycle cancelling for multicommodity flow problems with separable convex cost ruling out saturated capacities. This method is inspired by the cycle cancelling method first worked out by Goldberg and Tarjan for minimum cost circulations (A.V. Goldberg, R.E. Tarjan, Finding minimum-cost circulation by cancelling negative cycles, JACM 36 (4), 1989, pp. 873–886). Convergence of the method is formally proved and a variant with a more flexible selection of cycles is proposed. Also, we report some computational experience on the message routing problem in telecommunication networks using actual and randomly generated networks.

Elastic time computation in QoS-driven hypermedia presentations

The development of hypermedia/multimedia systems requires the implementation of an element, usually known as formatter, which is in charge of receiving the specification of a document and controlling its presentation. Adjustments over the duration of media objects is one of the most important adaptation techniques that hypermedia formatters should implement in order to maintain document spatio-temporal relationships. Elastic time computation accomplishes this goal by stretching or shrinking the ideal duration of media objects. This paper presents new elastic time algorithms for adjusting hypermedia document presentations. The algorithms explore the flexibility offered by some hypermedia models in the definition of media-object durations, choosing objects to be stretched or shrunk in order to obtain the best possible quality of presentation. Our proposals are based on the “out-of-kilter” method for minimum-cost flow problems on temporal graphs. An aggregation procedure enhances the basic algorithm offering more flexibility in modeling real-life situations in comparison with other previous work based on linear programming.

Minimum convex piecewise linear cost tension problem on quasi-k series-parallel graphs

Abstract.Some hypermedia synchronization issues request the resolution of the minimum convex piecewise linear cost tension problem (CPLCT problem) on directed graphs that are close to two-terminal series-parallel graphs (TTSP-graphs), the so-called quasi-k series-parallel graphs (k-QSP graphs). An aggregation algorithm has already been introduced for the CPLCT problem on TTSP-graphs. We propose here a reconstruction method, based on the aggregation and the well-known out-of-kilter techniques, to solve the problem on k-QSP graphs. One of the main steps being to decompose a graph into TTSP-subgraphs, methods based on the recognition of TTSP-graphs are thoroughly discussed.