Very large-scale neighborhood search algorithms for the design of service overlay networks

Abstract

Service Overlay Networks (SONs) allow virtual operators to create and deploy value-added Internet services with Quality of Service guarantees, while leaving the underlying network infrastructure unchanged. The deployment of a SON can be very expensive, and hence its planning requires careful decisions, including the overlay nodes' placement and the capacity provisioning of the access links that connect the end-users to the SON infrastructure.In this work we first propose a novel Integer Linear Programming (ILP) model for the overlay network design problem which selects the optimal number and position of overlay nodes, the capacity reserved on each overlay link, as well as the optimal routing of the incoming traffic demands.Since such model can be solved to the optimum only for small network instances, we further propose an efficient and novel tabu search based heuristic for the planning of SONs that combines polynomial size and very large-scale neighborhoods. The very large-scale neighborhood of the solution given by tabu search is explored efficiently to obtain in a short time a new one that is both far from the current solution and cost-decreasing.We provide numerical results of the proposed heuristic on a set of realistic, large-size instances, including real ISP topologies, and discuss the effect of different parameters on the characteristics of the planned networks. Furthermore, we compare such results with the bound obtained solving our ILP model in small network scenarios. We show that in the considered network topologies the proposed heuristic performs very close to the optimum with a short computation time, thus providing a promising framework for the design of SONs.

A generalized service deployment framework for OSPF networks using multi‐topology routing: Modeling and methodology

“…Generally, local search can be adopted to find link weights value for each logical topology. Local search algorithm begins with an initial solution and then explores neighborhood solutions that are generated by a transformation of the available solution.…”

Search in the universe of big networks and data

“…Within computer science, many efforts have been devoted to developing appropriate languages and data organisations that can help formulate requests, organise the data appropriately in advance of search to achieve fast data identification and extraction from the web [1], [2]. In packet and peer-topeer networks, effective algorithms have been studied for neighbourhood search [3], [4], and routing in opportunistic networks [5] while diffusion models have also been studied for sensor networks [6].…”

Tour Planning for Sightseeing with Time-Dependent Satisfactions of Activities and Traveling Times

“…Therefore, we may solve the problem using the network optimization method strictly or heuristic solution algorithm efficiently; branch-and-cut algorithm (Fischetti et al [21]), 2-opt or n-opt algorithm (Chao et al [22], Zhu et al [1]), LP relaxation (Kennington and Nicholso [23]), Genetic algorithm (Abbaspour and Samadzadegan [2]), Neural network (Wang et al [24]), Tabu-search (Gendreau et al [25], Tang and Miller-Hooks [26]), Ant Colony Optimization (Ke et al [27]), etc. Most recently, tabu search-based algorithms are widely used in various types of large-scale practical problems formulated as 0-1 programming problems, for instance, Job shop scheduling problem (Fattahi et al [28]), Airport gate assignment problem (Seker and Noyan [29]), Service overlay network in the telecommunication system (Elias et al [30]), etc. Therefore, we will solve large-scale problems of our proposed model by directly applying or improving these tabu search-based algorithms.…”