Reroute sequence planning in telecommunication networks and compact vector summation

The paper deals with the reroute sequence planning in telecommunication networks. Initially, we are given communication requests between terminal pairs and a path system which is able to satisfy these demands while respecting the physical link capacities. A reconfiguration problem arises when the path set is recalculated by a global optimization method for achieving a better resource utilization. After the recalculation the paths in the routing have to be changed to the optimized ones in the working network. In this case, a sequence of one by one reroutings have to be found with the constraint that the capacities should not be violated and no one of the demands can become unsatisfied during the reroute process. Provided that the (initial and final) free capacities are large enough, such a permutation can be computed. The paper deals with theoretical results giving bounds for these free capacities, with vector-sum and discrepancy methods.     

Approximating connected facility location with buy-at-bulk edge costs via random sampling

In the connected facility location problem with buy-at-bulk edge costs we are given a set of clients with positive demands and a set of potential facilities with opening costs in an undirected graph with edge lengths obeying the triangle inequality. Moreover, we are given a set of access cable types, each with a cost per unit length and a capacity such that the cost per capacity decreases from small to large cables, and a core cable type of infinite capacity. The task is to open some facilities and to connect them by a Steiner tree using core cables, and to build a forest network using access cables such that the edge capacities suffice to simultaneously route all client demands unsplit to the open facilities. The objective is to minimize the total cost of opening facilities, building the core Steiner tree, and installing the access cables. In this paper, we devise a constant-factor approximation algorithm for this problem based on a random sampling technique.     

MC-based Algorithm for a telecommunication network under node and budget constraints

It is an important issue to design some performance indexes in order to measure the performance for a telecommunication network. Network analysis is an available approach to solve the performance problem for a real-life system. We construct a two-commodity stochastic-flow network with unreliable nodes (arcs and nodes all have several possible capacities and may fail) to model the telecommunication network. In which, all types of commodity are transmitted through the same network simultaneously and compete the capacities. This paper defines the system capacity as a 2-tuple vector, and then proposes a performance index, the probability that the upper bound of the system capacity equals a demand vector subject to the budget constraint. An upper boundary point is a vector representing the capacities of arcs and nodes, and is the maximal vector exactly meeting the demand vector. A simple algorithm based on minimal cuts (or named MC-based algorithm) is then presented to generate all upper boundary points in order to evaluate the performance index. The storage and computational time complexity of this algorithm are also analyzed. The performance evaluation for the multicommodity case can be extended easily.     

Network synthesis under survivability constraints

Telecommunication networks are subject to link and equipment failures. Since failures cannot be entirely avoided, networks have to be designed so as to survive failure situations. In this paper, we are interested in designing low cost survivable networks. Given point-to-point traffic demands and a cost/capacity function for each link, we aim at finding the minimum cost capacities satisfying the given demands and survivability requirements. A survivability model that reroutes interrupted traffic using all the available capacities on the network is presented and studied. In the proposed model, capacity and flow assignments for each network operating state are jointly optimized. We prove the -hardness of the optimisation problem defined by dual constraints. Then, we propose a polynomial relaxation along with a fast heuristic to compute a feasible solution of the problem from its relaxed optimal solution. Our solution approaches are tested on a set of problem instances.Received: September 2002, Revised: July 2003, AMS classification: 90C05     

Heuristics for Distribution Network Design in Telecommunication

A distribution network problem arises in a lower level of an hierarchical modeling approach for telecommunication network planning. This paper describes a model and proposes a lagrangian heuristic for designing a distribution network. Our model is a complex extension of a capacitated single commodity network design problem. We are given a network containing a set of sources with maximum available supply, a set of sinks with required demands, and a set of transshipment points. We need to install adequate capacities on the arcs to route the required flow to each sink, that may be an intermediate or a terminal node of an arborescence. Capacity can only be installed in discrete levels, i.e., cables are available only in certain standard capacities. Economies of scale induce the use of a unique higher capacity cable instead of an equivalent set of lower capacity cables to cover the flow requirements of any link. A path from a source to a terminal node requires a lower flow in the measure that we are closer to the terminal node, since many nodes in the path may be intermediate sinks. On the other hand, the reduction of cable capacity levels across any path is inhibited by splicing costs. The objective is to minimize the total cost of the network, given by the sum of the arc capacity (cables) costs plus the splicing costs along the nodes. In addition to the limited supply and the node demand requirements, the model incorporates constraints on the number of cables installed on each edge and the maximum number of splices at each node. The model is a NP-hard combinatorial optimization problem because it is an extension of the Steiner problem in graphs. Moreover, the discrete levels of cable capacity and the need to consider splicing costs increase the complexity of the problem. We include some computational results of the lagrangian heuristics that works well in the practice of computer aided distribution network design.     

Parameterized complexity of spare capacity allocation and the multicost Steiner subgraph problem

We study the computational complexity of the Spare Capacity Allocation problem arising in optical networks that use a shared mesh restoration scheme. In this problem we are given a network with edge capacities and point-to-point demands, and the goal is to allocate two edge-disjoint paths for each demand (a working path and a so-called restoration path, which is activated only if the working path fails) so that the capacity constraints are satisfied and the total cost of the used and reserved bandwidth is minimized. We focus on the setting where we deal with a group of demands together, and select their restoration paths simultaneously in order to minimize the total cost. We investigate how the computational complexity of this problem is affected by certain parameters, such as the number of restoration paths to be selected, or the treewidth of the network graph. To analyze the complexity of the problem, we introduce a generalization of the Steiner Forest problem that we call Multicost Steiner Subgraph. We study its parameterized complexity, and identify computationally easy and hard cases by providing hardness proofs as well as efficient (fixed-parameter tractable) algorithms.     

Solving survivable two-layer network design problems by metric inequalities

We address the problem of designing a multi-layer network with survivability requirements. We are given a two-layer network: the lower layer represents the potential physical connections that can be activated, the upper layer is made of logical connections that can be set up using physical links. We are given origin-destination demands (commodities) to be routed at the upper layer. We are also given a set of failure scenarios and, for every scenario, an associated subset of commodities. The goal is to install minimum cost integer capacities on the links of both layers in order to ensure that the commodities can be routed simultaneously on the network. In addition, in every failure scenario the routing of the associated commodities must be guaranteed. We consider two variants of the problem and develop a branch-and-cut scheme based on the capacity formulation. Computational results on instances derived from the SNDLib for single node failure scenarios are discussed.     

A composite very large-scale neighborhood structure for the capacitated minimum spanning tree problem

The capacitated minimum spanning tree (CMST) problem is to find a minimum cost spanning tree in a network where nodes have specified demands, with an additional capacity constraints on the subtrees incident to a given source node s. The capacitated minimum spanning tree problem arises as an important subproblem in many telecommunication network design problems. In a recent paper, Ahuja et al. (Math. Program. 91 (2001) 71) proposed two very large-scale neighborhood search algorithms for the capacitated minimum spanning tree problem. Their first node-based neighborhood structure is obtained by performing multi-exchanges involving several trees where each tree contributes a single node. Their second tree-based neighborhood structure is obtained by performing multi-exchanges where each tree contributes a subtree. The computational investigations found that node-based multi-exchange neighborhood gives the best performance for the homogenous demand case (when all nodes have the same demand), and the tree-based multi-exchange neighborhood gives the best performance for the heterogeneous demand case (when nodes may have different demands). In this paper, we propose a composite neighborhood structure that subsumes both the node-based and tree-based neighborhoods, and outperforms both the previous neighborhood search algorithms for solving the capacitated minimum spanning tree problem on standard benchmark instances. We also develop improved dynamic programming based exact algorithms for searching the composite neighborhood.     

Optimal expansion of capacitated transshipment networks

In this paper, we address the problem of allocating a given budget to increase the capacities of arcs in a transshipment network to minimize the cost of flow in the network. The capacity expansion costs of arcs are assumed to be piecewise linear convex functions. We use properties of the optimum solution to convert this problem into a parametric network flow problem. The concept of optimum basis structure is used which allows us to consider piecewise linear convex functions without introducing additional arcs. The resulting algorithm yields an optimum solution of the capacity expansion problem for all budget levels less than or equal to the given budget. For integer data, the algorithm performs almost all computations in integers. Detailed computational results are also presented.     

On a multicommodity stochastic-flow network with unreliable nodes subject to budget constraint

From the quality management and decision making view point, reliability and unreliability are important indices to measure the quality level for a stochastic-flow network. In a multicommodity stochastic-flow network with unreliable nodes, the branches and nodes all have several possible capacities and may fail. Different types of the commodity, which are transmitted through the same network simultaneously, compete the capacities of branches and nodes. In this paper we first define the system capacity as a vector for a multicommodity stochastic-flow network with unreliable nodes. Then we design a performance index which is the probability that the upper bound of the system capacity is a given pattern subject to the budget constraint. It can be applied to evaluate the quality level for such a network. A simple approach based on minimal cuts is thus presented to evaluate the performance index.     

A Heuristic Procedure for Leasing Channels in Telecommunications Networks

Rapid advances in computing and communications technology have made distributed computing an attractive alternative for geographically dispersed organizations. A telecommunication sub-network forms the backbone of these distributed systems. In general, this paper focuses on the assignment of communication channel capacities in the presence of time variant usage patterns. Specifically, we concentrate on long-range capacity planning for organizations that construct networks by leasing communication channels from telecommunication companies. We formulate the capacity assignment problem as a 0-1 integer program that seeks to minimize total leasing cost subject to communication delay restrictions. Unlike previous models that include a single-system wide-average delay constraint, our model allows the flexibility of specifying delay restrictions by communicating node pairs. We propose an efficient heuristic, and a Lagrangian relaxation based procedure to obtain performance guarantees on the solution obtained from the heuristic.     

Analysis of the MAP/G a ,b /1/N Queue

The Markovian arrival process (MAP) is used to represent the bursty and correlated traffic arising in modern telecommunication network. In this paper, we consider a single server finite capacity queue with general bulk service rule in which arrivals are governed by MAP and service times are arbitrarily distributed. The distributions of the number of customers in the queue at arbitrary, post-departure and pre-arrival epochs have been obtained using the supplementary variable and the embedded Markov chain techniques. Computational procedure has been given when the service time distribution is of phase type.     

Generate upper boundary vectors meeting the demand and budget for a p-commodity network with unreliable nodes

The system capacity for a single-commodity flow network is the maximum flow from the source to the sink. This paper discusses the system capacity problem for a p-commodity limited-flow network with unreliable nodes. In such a network, arcs and nodes all have several possible capacities and may fail. Different types of commodity, which are transmitted through the same network simultaneously, competes the capacities of arcs and nodes. In particular, the consumed capacity by different types of commodity varies from arcs and nodes. We first define the system capacity as a vector and then a performance index, the probability that the upper bound of the system capacity is a given pattern subject to the budget constraint, is proposed. Such a performance index can be easily computed in terms of upper boundary vectors meeting the demand and budget. A simple algorithm based on minimal cuts is thus presented to generate all upper boundary vectors. The manager can apply this performance index to measure the system capacity level for a supply-demand system.     

Multi-service multi-facility network design under uncertainty

The problem of designing high speed networks using different modules of link capacities, in the same model, in order to meet uncertain demands obtained from different probability distribution functions (PDF) is a very hard and challenging real network design problem. The novelty of the new model, compared to previous ones, is to allow installing more than one module per link having equal or different capacities. Moreover, the scenarios of traffic can be generated, according to practical observations, from the main classes of uncertain demands (multi-service) simulated from different PDFs, including heavy tailed ones. These classes of traffic are considered simultaneously for the scenario generation, different from related works in the literature that use only one probability distribution function to simulate the scenarios of traffic. In this work we present the problem formulation and report computational results using branch-and-bound and L-shaped decomposition solution approaches. We consider in the same model up to three different types of modular capacities (multi-facility), since it seems that using more than this can lead to an intractable model. The objective is to minimize penalty (in case of unmet demands) and investment costs. We obtain confidence intervals (with 95% of covering rate) on the expected optimal solution value for the resulting two-stage stochastic integer-modular problem and discuss when they are meaningful. Numerical experiments show that our model can handle up to medium real size instances.     

On the Single-Source Unsplittable Flow Problem

be a capacitated directed graph with a source s and k terminals with demands , . We would like to concurrently route every demand on a single path from s to the corresponding terminal without violating the capacities. There are several interesting and important variations of this unsplittable flow problem. If the necessary cut condition is satisfied, we show how to compute an unsplittable flow satisfying the demands such that the total flow through any edge exceeds its capacity by at most the maximum demand. For graphs in which all capacities are at least the maximum demand, we therefore obtain an unsplittable flow with congestion at most 2, and this result is best possible. Furthermore, we show that all demands can be routed unsplittably in 5 rounds, i.e., all demands can be collectively satisfied by the union of 5 unsplittable flows. Finally, we show that 22.6% of the total demand can be satisfied unsplittably. These results are extended to the case when the cut condition is not necessarily satisfied. We derive a 2-approximation algorithm for congestion, a 5-approximation algorithm for the number of rounds and a -approximation algorithm for the maximum routable demand. Received: July 12, 1998     

Algorithms for the non-bifurcated network design problem

In this paper we consider the non-bifurcated network design problem where a given set of cities must be connected by installing on a given set of links integer multiples of some base capacity so that a set of commodity demands can be routed. Each commodity flow is constrained to run through a single path along the network. The objective is to minimize the sum of capacity installation and routing costs. We present an exact algorithm and four new heuristics. The first heuristic generates an initial feasible solution, then it improves it until a necessary condition for optimality is satisfied. Two heuristics are partial enumeration methods and the last one iteratively applies a tabu search method to different initial feasible solutions. Computational results over a set of test problems from the literature show the effectiveness of the proposed algorithms.     

Revisiting parametric multi-terminal problems: Maximum flows, minimum cuts and cut-tree computations

Given an undirected network, the multi-terminal network flows analysis consists in determining the all pairs maximum flow values. In this paper, we consider an undirected network in which some edge capacities are allowed to vary and we analyze the impact of such variations on the all pairs maximum flow values. We first provide an efficient algorithm for the single parametric capacity case, and then propose a generalization to the case of multiple parametric capacities. Moreover, we provide a study on Gomory–Hu cut-tree relationships.     

Network flow interdiction on planar graphs

The network flow interdiction problem asks to reduce the value of a maximum flow in a given network as much as possible by removing arcs and vertices of the network constrained to a fixed budget. Although the network flow interdiction problem is strongly NP-complete on general networks, pseudo-polynomial algorithms were found for planar networks with a single source and a single sink and without the possibility to remove vertices. In this work, we introduce pseudo-polynomial algorithms that overcome various restrictions of previous methods. In particular, we propose a planarity-preserving transformation that enables incorporation of vertex removals and vertex capacities in pseudo-polynomial interdiction algorithms for planar graphs. Additionally, a new approach is introduced that allows us to determine in pseudo-polynomial time the minimum interdiction budget needed to remove arcs and vertices of a given network such that the demands of the sink node cannot be completely satisfied anymore. The algorithm works on planar networks with multiple sources and sinks satisfying that the sum of the supplies at the sources equals the sum of the demands at the sinks. A simple extension of the proposed method allows us to broaden its applicability to solve network flow interdiction problems on planar networks with a single source and sink having no restrictions on the demand and supply. The proposed method can therefore solve a wider class of flow interdiction problems in pseudo-polynomial time than previous pseudo-polynomial algorithms and is the first pseudo-polynomial algorithm that can solve non-trivial planar flow interdiction problems with multiple sources and sinks. Furthermore, we show that the k-densest subgraph problem on planar graphs can be reduced to a network flow interdiction problem on a planar graph with multiple sources and sinks and polynomially bounded input numbers.     

Maximal Flow in Branching Trees and Binary Search Trees

A capacitated network is a tree with a non negative number, called capacity, associated to each edge. The maximal flow that can pass through a given path is the minimun capacity on the path. Antal and Krapivski (Phys Rev E 74:051110, 2006) study the distribution for the maximal flow from the root to a leaf in the case of a deterministic binary tree with independent and identically distributed random capacities. In this paper their result is extended to three classes of trees with a random number of children and dependent random capacities: binary trees with general capacities distribution, branching trees with exchangeable capacities and random binary search trees.     

On the minimum number of edges of two-connected graphs with given diameter

We answer the following question: what is the minimum number of edges of a 2-connected graph with a given diameter? This problem stems from survivable telecommunication network design with grade-of-service constraints. In this paper, we prove tight bounds for 2-connected graphs and for 2-edge-connected graphs. The bound for 2-connected graphs was a conjecture of B. Bollobás (AMH 75–80) [3].