Optimization of Free Space Optical Wireless Network for Cellular Backhauling

With densification of nodes in cellular networks, free space optic (FSO) connections are becoming an appealing low cost and high rate alternative to copper and fiber as the backhaul solution for wireless communication systems. To ensure a reliable cellular backhaul, provisions for redundant, disjoint paths between the nodes must be made in the design phase. This paper aims at finding a cost-effective solution to upgrade the cellular backhaul with pre-deployed optical fibers using FSO links and mirror components. Since the quality of the FSO links depends on several factors, such as transmission distance, power, and weather conditions, we adopt an elaborate formulation to calculate link reliability. We present a novel integer linear programming model to approach optimal FSO backhaul design, guaranteeing $K$-disjoint paths connecting each node pair. Next, we derive a column generation method to a path-oriented mathematical formulation. Applying the method in a sequential manner enables high computational scalability. We use realistic scenarios to demonstrate our approaches efficiently provide optimal or near-optimal solutions, and thereby allow for accurately dealing with the trade-off between cost and reliability

1. Planning and Managing Networks in Unpredictable Traffic

The goal of the paper is two-fold. First, the paper formulates a set of optimization problems relevant for the design of optical WDM networks robust to failures, encompassing demand routing, wavelength assignment and link dimensioning. Two basic protection mechanisms are considered: path diversity and single backup path restoration. The design problems, taking directly into account a scenario of assumed failure situations, are formulated as mixed linear integer programming tasks. For small networks these tasks can be solved directly with the branch and bound approach available e.g. in the CPLEX optimization package. As the considered problems are NP-complete, for networks of realistic size heuristic methods are called for. Accordingly, the second goal of the paper is to demonstrate how to apply two proposed stochastic heuristic approaches, namely Simulated Annealing and Simulated Allocation, to the specified problems. It is shown using large network configurations that the latter approach, although not commonly known, turns out to be superior to the former, and yields good sub-optimal solutions in reasonable time. Our numerical results also show what extra spare capacity volume is required by the considered protection mechanisms. This can help solving the tradeoff between the reconfiguration complexity and the extra link and node capacity cost

VP reconfiguration through Simulated Allocation

VP reconfiguration is a powerful and flexible tool to cope with traffic changes and/or equipment failures in ATM networks. In the paper we present an application of a stochastic optimization algorithm called Simulated Allocation to the problem of VP reconfiguration in response to traffic shifts. The considered optimization task takes into account the cost of VPs reconfiguration imposed by changes in VP routing tables, rearrangement and possible loss of some calls in progress. Numerical results illustrating the effectiveness of the Simulated Allocation algorithm are given

Traffic Engineering in the Presence of Tunneling and Diversity Constraints: Formulation and Lagrangean Decomposition Approach

this paper, we present a mixed integer linear optimization formulation of a traffic engineering problem where we have captured restriction on tunnels for a router in terms of number of LSPs that can be supported on a specific link in the presence of multi-service traffic classes; further, we put restriction on demand flow on a path by introducing diversity constraints. Typically, diversity constraint is introduced to provide some level of survivability, in case one of the active LSPs is affected due to a link failure (see, for a different example, [2]). While we use MPLS and LSPs to explain the problem, the model can be applicable in other traffic engineering frameworks where the restriction on the number of tunnels is an issue. The rest of the paper is organized as follows. In section 2, we present the description, parameters and the formulation as an MIP problem. In section 3, we present a decomposition algorithm to solve the MIP problem with a series of continuous problems. In section 4, we present results for small and large networks (experimental and randomly generated). 2 Formulation We consider an aggregated-flow based network, where traffic data (packets) arriving to a source for a specific destination needs to be sent over one of the active LSPs between the source and the destination. Traffic data belongs to one of the service classes and hence can only be sent on the LSPs of its service class. Each service class maintains its own set of LSPs between source and destinations. The LSPs are assumed not to be shared between service classes since each service class can have its own stringent end-to-end requirement. The total LSPs chosen to be activated across the network are such that the total number of LSPs flowing through each link are restricted. The formulatio..

Backup Path Restoration Design Using Path Generation Technique

In this work, we present a flow restoration based network design problem where we assume the knowledge of possible failure situations. We use the idea of a situation disjoint path couple (nominal path, backup path) which are constructed in such a way that atleast one of them is operational in any given failure situation for a flow. We present a linear programming formulation of the problem and use path generation technique (based on column generation) to solve the same for large networks. The path generation approach is an iterative method that allows us to add path couples with greater discretion, based on dual Lagrangean multipliers in every iteration. We consider four different scenarios that differ in the way we compute and add new path couples at every iteration. We present the results of our approach for an example network. From our observations, we conclude that path generation approach is an effective method to solve the backup path restoration design problem

Optimizing primary and backup SDN controllers' placement resilient to node-targeted attacks

In Software Defined Networks (SDNs), a number of controllers are placed in a given data plane network. In a standard logically centralized control plane, each controller acts simultaneously as a primary controller for some switches and as a backup controller for other switches, and the controller placements must meet given switch-controller (SC) and controller-controller (CC) delay bounds. Then, the SDN should be resilient to network disruptions such as node-targeted attacks. To improve the SDN resilience to this kind of disruptions, we assume that some controllers are deployed only as backup controllers so that they take over the functions of primary controllers only in case of disruption. We propose an optimization model that solves a relevant primary and backup controller placement problem, where a minimum number of primary controllers minimizing the maximum SC delay is first established, and then a joint primary and backup controller placement maximizing the resilience of the SDN against a list of the most dangerous node-targeted attacks is determined. A numerical study illustrating the merits of the proposed optimization methodology is presented.publishe

Optimizing network load balancing: an hybridization approach of metaheuristics with column generation

Given a capacitated telecommunications network with single path routing and an estimated traffic demand matrix, we aim to determine the routing path of each traffic commodity such that the whole set of paths provide an optimal network load balancing. In a recent paper, we have proposed a column generation based heuristic where, in the first step, we use column generation to solve a linear programming relaxation of the original problem (obtaining, in this way, a lower bound and a set of paths for each commodity) and, in the second step, we apply a multi-start local search with path relinking heuristic on the solution space defined by the paths of the first step. Here, we propose a hybridization approach of the metaheuristic with column generation that can be seen as an enhanced version of the previous approach: we run column generation not only at the beginning (to define the initial search space) but also during the search. These additional column generation steps consist in solving a perturbed problem defined by the incumbent solution. In the previous paper, we have shown that the first approach is efficient in obtaining near optimal routing solutions within short running times. With the enhanced version, we show through computational results that the additional paths, introduced by the additional column generation steps, either improve the efficiency of the algorithm or show similar efficiency in the cases where the original algorithm is already very efficient

Optimization of link load balancing in multiple spanning tree routing networks

In telecommunication networks based on the current Ethernet technology, routing of traffic demands is based on multiple spanning trees: the network operator configures different routing spanning trees and assigns each demand to be routed in one of the selected spanning trees. A major optimization issue in this solution is the combined determination of (i) a set of appropriate spanning trees, and (ii) assignment of demands to the trees, in order to achieve an optimal load balancing on the links of the network. In this paper we consider models and solving techniques for lexicographical optimization of two load balancing objective functions. The first objective is the min-max optimization of the n worst link loads (with n up to the total number of network links), and the second objective is the minimization of the average link load (when n is smaller than the total number of network links). Besides exact methods, a heuristic technique that can compute both feasible solutions and lower bounds for the addressed optimization problem is proposed. Finally, we discuss effectiveness of different solution using results of a numerical study of realistic case studies.FCT - PTDC/ EIA/64772/2006Bolsa pos-doc - SFRH/ BPD/41581/200