Optimizing load balancing routing mechanisms with evolutionary computation

Link State routing protocols, such as Open Shortest Path First (OSPF), are widely applied to intra-domain routing in todays IP networks. They provide a good scalability without lost of simplicity. A router running OSPF distributes traf- fic uniformly over Equal-cost Multi-path (ECMP), enabling a better distribution of packets among the existent links. More recently, other load balancing strategies, that consider non even splitting of traffic, have been put forward. Such is the case of the Distributed Exponentially-weighted Flow SpliTting (DEFT), that enables traf- fic to be directed through non equal-cost multi-paths, while preserving the OSPF simplicity. As the optimal link weight computation is known to be NP-hard, intel- ligence heuristics are particularly suited to address this optimization problem. In this context, this work compares the solutions provided by Evolutionary Al- gorithms (EA) for the weight setting problem, considering both ECMP and DEFT load balancing alternatives. In addition to a single objective network congestion optimization problem, both load balancing schemes are also applied to a multi- objective optimization approach able to attain routing configurations resilient to traffic demand variations.COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e TecnologiaThis work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT -Fundação para a Ciência e Tecnologia within the ProjectScope: UID/CEC/00319/2013

An Enhanced Estimator to Multi-objective OSPF WeightSetting Problem

Abstract—Open Shortest Path First (OSPF) is a routing protocol which is widely used in the Industry. Its functionality mainly depends on the weights assigned to the links. Given the traffic demands on a network, setting weights such that congestion can be avoided is an NP-hard problem. Optimizing these link weights leads to efficient network utilization which is the main goal of traffic engineering. In this paper, Simulated Annealing iterative heuristic is applied to this problem. This will provide close-to-optimal solutions that can be used for network provisioning. For this problem, the cost function that has been used in the literature depends solely on the links utilization and therefore optimizes only the network utilization. In this paper, our goal is to optimize the number of congested links in the network in addition to the utilization. Therefore, we propose a new cost function that depends on the utilization and the extra load caused by congested links in the network. This provides the network designer with more flexibility to optimize desired parameters. Our results show less number of congested links and comparable extra load in the network when compared to results of using the existing cost function

An Enhanced Estimator to Multi-objective OSPF Weight Setting Problem

Open Shortest Path First (OSPF) is a routing protocol which is widely used in the Industry. Its functionality mainly depends on the weights assigned to the links. Given the traf�c demands on a network, setting weights such that congestion can be avoided is an NP-hard problem. Optimizing these link weights leads to ef�cient network utilization which is the main goal of traf�c engineering. In this paper, Simulated Annealing iterative heuristic is applied to this problem. This will provide close-to-optimal solutions that can be used for network provisioning. For this problem, the cost function that has been used in the literature depends solely on the links utilization and therefore optimizes only the network utilization. In this paper, our goal is to optimize the number of congested links in the network in addition to the utilization. Therefore, we propose a new cost function that depends on the utilization and the extra load caused by congested links in the network. This provides the network designer with more �exibility to optimize desired parameters. Our results show less number of congested links and comparable extra load in the network when compared to results of using the existing cost function

A Novel Optimal routing using Hop-by-Hop Adaptive linking

I am presenting the first of its kind project, the first link-state routing solution carrying traffic through packet-switched networks. At each node, for every other node, the algorithm independently and iteratively updates the fraction of traffic destined to that leaves on each of its outgoing links. At each iteration, the updates are calculated based on the shortest path to each destination as determined by the marginal costs of the network’s links. The marginal link costs used to find the shortest paths are in turn obtained from link-state updates that are flooded through the network after each iteration. For stationary input traffic, we prove that our project converges to the routing assignment that minimizes the cost of the network. Furthermore, I observe that our technique is adaptive, automatically converging to the new optimal routing assignment for quasi-static network changes. I also report numerical and experimental evaluations to confirm our theoretical predictions, explore additional aspects of the solution, and outline a proof-of-concept implementation of proposal

Hop-by-Hop Adaptive linking A Novel Approach for Finest routing

Using Hop-by-Hop Adaptive linking for achieving finest routing is an unprecedented approach. And it is the first link-state routing solution carrying traffic through packet-switched networks. At each node, for every other node, the algorithm independently and iteratively updates the fraction of traffic destined to that leaves on each of its outgoing links. At each iteration, the updates are calculated based on the shortest path to each destination as determined by the marginal costs of the network’s links. The marginal link costs used to find the shortest paths are in turn obtained from link-state updates that are flooded through the network after each iteration. For stationary input traffic, we prove that our project converges to the routing assignment that minimizes the cost of the network. Furthermore, I observe that our technique is adaptive, automatically converging to the new optimal routing assignment for quasi-static network changes. I also report numerical and experimental evaluations to confirm our theoretical predictions, explore additional aspects of the solution, and outline a proof-of-concept implementation of proposal

Congestion removal in the next generation internet

The ongoing development of new and demanding Internet applications requires the Internet to deliver better service levels that are significantly better than the best effort service that the Internet currently provides and was built for. These improved service levels include guaranteed delays, jitter and bandwidth. Through extensive research into Quality of Service and Differentiated Service (DiffServ) it has become possible to provide guaranteed services, however this turns out to be inadequate without the application of Traffic Engineering methodologies and principles. Traffic Engineering is an integral part of network operation. Its major goal is to deliver the best performance from an existing service provider's network resources and, at the same time, to enhance a customers' view of network performance. In this thesis, several different traffic engineering methods for optimising the operation of native IP and IP networks employing MPLS are proposed. A feature of these new methods is their fast run times and this opens the way to making them suitable for application in an online traffic engineering environment. For native IP networks running shortest path based routing protocols, we show that an LP-based optimisation based on the well known multi-commodity flow problem can be effective in removing network congestion. Having realised that Internet service providers are now moving towards migrating their networks to the use of MPLS, we have also formulated optimisation methods to traffic engineer MPLS networks by selecting suitable routing paths and utilising the feature of explicit routing contained in MPLS. Although MPLS is capable of delivering traffic engineering across different classes of traffic, network operators still prefer to rely on the proven and simple IP based routing protocols for best effort traffic and only use MPLS to route traffic requiring special forwarding treatment. Based on this fact, we propose a method that optimises the routing patterns applicable to different classes of traffic based on their bandwidth requirements. A traffic engineering comparison study that evaluates the performance of a neural network-based method for MPLS networks and LP-based weight setting approach for shortest path based networks has been performed using a well-known open source network simulator, called ns2. The comparative evaluation is based upon the packet loss probability. The final chapter of the thesis describes the software development of a network management application called OptiFlow which integrates techniques described in earlier chapters including the LP-based weight setting optimisation methodology; it also uses traffic matrix estimation techniques that are required as input to the weight setting models that have been devised. The motivation for developing OptiFlow was to provide a prototype set of tools that meet the congestion management needs of networking industries (ISPs and telecommunications companies - telcos)

Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem

The open shortest path first (OSPF) routing protocol is a well-known approach for routing packets from a source node to a destination node. The protocol assigns weights (or costs) to the links of a network. These weights are used to determine the shortest paths between all sources to all destination nodes. Assignment of these weights to the links is classified as an NP-hard problem. The aim behind the solution to the OSPF weight setting problem is to obtain optimized routing paths to enhance the utilization of the network. This paper formulates the above problem as a multi-objective optimization problem. The optimization metrics are maximum utilization, number of congested links, and number of unused links. These metrics are conflicting in nature, which motivates the use of fuzzy logic to be employed as a tool to aggregate these metrics into a scalar cost function. This scalar cost function is then optimized using a fuzzy particle swarm optimization (FPSO) algorithm developed in this paper. A modified variant of the proposed PSO, namely, fuzzy evolutionary PSO (FEPSO), is also developed. FEPSO incorporates the characteristics of the simulated evolution heuristic into FPSO. Experimentation is done using 12 test cases reported in literature. These test cases consist of 50 and 100 nodes, with the number of arcs ranging from 148 to 503. Empirical results have been obtained and analyzed for different values of FPSO parameters. Results also suggest that FEPSO outperformed FPSO in terms of quality of solution by achieving improvements between 7 and 31 %. Furthermore, comparison of FEPSO with various other algorithms such as Pareto-dominance PSO, weighted aggregation PSO, NSGA-II, simulated evolution, and simulated annealing algorithms revealed that FEPSO performed better than all of them by achieving best results for two or all three objectives.http://link.springer.com/journal/104892017-10-31hb2016Computer Scienc

Constrained shortest paths for QoS routing and path protection in communication networks.

The CSDP (k) problem requires the selection of a set of k > 1 link-disjoint paths with minimum total cost and with total delay bounded by a given upper bound. This problem arises in the context of provisioning paths in a network that could be used to provide resilience to link failures. Again we studied the LP relaxation of the ILP formulation of the problem from the primal perspective and proposed an approximation algorithm.We have studied certain combinatorial optimization problems that arise in the context of two important problems in computer communication networks: end-to-end Quality of Service (QoS) and fault tolerance. These problems can be modeled as constrained shortest path(s) selection problems on networks with each of their links associated with additive weights representing the cost, delay etc.The problems considered above assume that the network status is known and accurate. However, in real networks, this assumption is not realistic. So we considered the QoS route selection problem under inaccurate state information. Here the goal is to find a path with the highest probability that satisfies a given delay upper bound. We proposed a pseudo-polynomial time approximation algorithm, a fully polynomial time approximation scheme, and a strongly polynomial time heuristic for this problem.Finally we studied the constrained shortest path problem with multiple additive constraints. Using the LARAC algorithm as a building block and combining ideas from mathematical programming, we proposed a new approximation algorithm.First we studied the QoS single route selection problem, i.e., the constrained shortest path (CSP) problem. The goal of the CSP problem is to identify a minimum cost route which incurs a delay less than a specified bound. It can be formulated as an integer linear programming (ILP) problem which is computationally intractable. The LARAC algorithm reported in the literature is based on the dual of the linear programming relaxation of the ILP formulation and gives an approximate solution. We proposed two new approximation algorithms solving the dual problem. Next, we studied the CSP problem using the primal simplex method and exploiting certain structural properties of networks. This led to a novel approximation algorithm