Link-cost Changes Research Articles

Neural network based optimal routing algorithm for communication networks

This paper presents the capability of the neural networks as a computational tool for solving constrained optimization problem, arising in routing algorithms for the present day communication networks. The application of neural networks in the optimum routing problem, in case of packet switched computer networks, where the goal is to minimize the average delays in the communication have been addressed. The effectiveness of neural network is shown by the results of simulation of a neural design to solve the shortest path problem. Simulation model of neural network is shown to be utilized in an optimum routing algorithm known as flow deviation algorithm. It is also shown that the model will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology.

Dynamic transition matrix generation for topology aggregation

In large networks nodes are clustered into groups for the purpose of simplifying routing. Each group has a set of ingress–egress nodes, and routing information is conveyed to the outside world in the form of a transition matrix that gives the cost of traversing the network between each ingress–egress node pair. In a dynamic environment where costs are subject to change, the cost for traversing a group, and consequently the transition matrix is affected. In this paper, we present a novel graph-coloring method for computing and consistently maintaining in a dynamic environment the transition matrix corresponding to a group of nodes. This method is applicable in the case where path selection is based on restrictive costs, such as bandwidth, and it considers the symmetric case. A key characteristic of the method is its ability to distinguish between topology or link-cost changes that necessarily leave the transition matrix unchanged and those that may not; that is, a correct transition matrix is maintained at all times without recomputing it every time a cost change occurs. Numerical results illustrate the efficiency of the method, expressed in terms of the percentage of time that the transition matrix needs to be recomputed.

RANDOMIZED MOBILE AGENT BASED ROUTING IN WIRELESS NETWORKS

We propose a novel approach for shortest path routing in wireless mobile networks. The approach makes use of n mobile agents initially launched from n mobile nodes forming the network. The agents move randomly from node to node and update routing information as they go. The approach is presented in this paper with two protocols. Both of them exhibit good performance in terms of the network and computing resource consumptions. The first protocol relies on independent mobile agents and imposes a minimum bandwidth requirement on individual mobile agents. Each agent carries the link state of its creator and this information remains unchanged except when the mobile agent returns to the home node. The second protocol is a refinement of the first protocol, with some form of interaction between the mobile agents. Each agent maintains the routing table of its creator instead of link state. The randomly walking agents spread the update information and compute the shortest paths via exchanging network state information between the routing tables they carry and the routing tables at the nodes they traverse. The correctness of the protocols is proven. Our analysis shows that the agent cooperation improves the system performance when dealing with topology and link cost changes.

Routing in computer networks using artificial neural networks

This paper proposes a heuristic approach based on Hopfield model of neural networks to solve the problem of routing which constitutes one of the key aspects of the topological design of computer networks. Adaptive to changes in link costs and network topology, the proposed approach relies on the utilization of an energy function which simulates the objective function used in network optimization while respecting the constraints imposed by the network designers. This function must converge toward a solution which, if not the best is at least as close as possible to the optimum. The simulation results reveal that the end-to-end delay computed according to this neural network approach is usually better than those determined by the conventional routing heuristics, in the sense that our routing algorithm realizes a better trade-off between end-to-end delay and running time, and consequently gives a better performance than many other well-known optimal algorithms.

A simple approximation to minimum-delay routing

The conventional approach to routing in computer networks consists of using a heuristic to compute a single shortest path from a source to a destination. Single-path routing is very responsive to topological and link-cost changes; however, except under light traffic loads, the delays obtained with this type of routing are far from optimal. Furthermore, if link costs are associated with delays, single-path routing exhibits oscillatory behavior and becomes unstable as traffic loads increase. On the other hand, minimum-delay routing approaches can minimize delays only when traffic is stationary or very slowly changing.We present a "near-optimal" routing framework that offers delays comparable to those of optimal routing and that is as flexible and responsive as single-path routing protocols proposed to date. First, an approximation to the Gallager's minimum-delay routing problem is derived, and then algorithms that implement the approximation scheme are presented and verified. We introduce the first routing algorithm based on link-state information that provides multiple paths of unequal cost to each destination that are loop-free at every instant. We show through simulations that the delays obtained in our framework are comparable to those obtained using the Gallager's minimum-delay routing. Also, we show that our framework renders far smaller delays and makes better use of resources than traditional single-path routing.

A loop-free routing protocol for large-scale internets using distance vectors

The loop-free path-finding algorithm (LPA) has been shown to maintain loop-free routing tables and to obtain correct routing tables after topological and link-cost changes faster and with less processing and communication overhead than link-state algorithms and prior loop-free routing algorithms based on vectors of distances. The limitation of LPA and prior routing algorithms based on routing trees has been the fact that, because LPA operates by communicating shortest-path routing trees incrementally among neighbor nodes, it forces routers to maintain more ‘host routes’ than would be needed in a traditional distance vector algorithm. Maintaining such information incurs additional storage overhead and may not be feasible in some cases (e.g. due to subnetting). To remedy this, and to provide a scalable Internet routing solution, we propose a new hierarchical routing algorithm that combines LPA with the area-based hierarchical routing scheme first proposed by McQuillan for distance-vector algorithms. The new algorithm, which we call the hierarchical information path-based routing (HIPR) algorithm, accommodates an arbitrary number of aggregation levels and can be viewed as a distributed version of Dijkstra's algorithm running over a hierarchical graph. HIPR is verified to be loop-free and correct. Simulations are used to show that HIPR is much more efficient than OSPF in terms of speed, communication and processing overhead required to converge to correct routing tables. HIPR constitutes the basis for future Internet routing protocols that are as simple as RIPv2, but with no looping and better performance than protocols based on link-states.

A protocol for scalable loop-free multicast routing

In network multimedia applications such as multiparty teleconferencing, users often need to send the same information to several (but not necessarily all) other users. To manage such one-to-many or many-to-many communication efficiently in wide-area internetworks, it is imperative to support and perform multicast routing. Multicast routing sends a single copy of a message from a source to multiple receivers over a communication link that is shared by the paths to the receivers. Loop-freedom is an especially important consideration in multicasting because applications using multicasting tend to be multimedia and bandwidth intensive, and loops in multicast routing duplicate looping packets. We present and verify a new multicast routing protocol, called multicast Internet protocol (MIP), which offers a simple and flexible approach to constructing both group-shared and shortest-paths multicast trees. MIP can be sender-initiated or receiver-initiated or both; therefore, it can be tailored to the particular nature of an application's group dynamics and size. MIP is independent of the underlying unicast routing algorithms used. MIP is robust and adapts under dynamic network conditions (topology or link cost changes) to maintain loop-free multicast routing. Under stable network conditions, MIP has no maintenance or control message overhead. We prove that MIP is loop-free at every instant, and that it is deadlock-free and obtains multicast routing trees within a finite time after the occurrence of an arbitrary sequence of topology or unicast changes.

Neural networks for shortest path computation and routing in computer networks

The application of neural networks to the optimum routing problem in packet-switched computer networks, where the goal is to minimize the network-wide average time delay, is addressed. Under appropriate assumptions, the optimum routing algorithm relies heavily on shortest path computations that have to be carried out in real time. For this purpose an efficient neural network shortest path algorithm that is an improved version of previously suggested Hopfield models is proposed. The general principles involved in the design of the proposed neural network are discussed in detail. Its computational power is demonstrated through computer simulations. One of the main features of the proposed model is that it will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology.

A new responsive distributed shortest-path rounting algorithm

Distributed shortest-path routing algorithms based on the Ford-Fulkerson method are simple to implement but they suffer from the cost-dependent looping problem: when link costs increase, routing table loops may form and convergence to correct paths may be too slow, depending on link costs. This problem can be eliminated if constraints are imposed on the order in which routing tables are updated at different nodes but the resulting internode protocols tend to be relatively complex. Furthermore, update constraints may restrict a node's ability to obtain alternate paths quickly in an environment where topology changes are frequent. In this paper, a new distributed shortest-path routing scheme based on the Ford-Fulkerson method is presented. Under the new scheme, each node uses partial topology information to eliminate the cost-dependent looping problem. No update constraints are imposed and no assumptions are made regarding link costs. In the worst case, the new scheme responds to link cost changes in O(D) update steps, where D is the diameter of the network after the occurrence of the changes.