Baijnath Kaushik

Simulation of Route Optimization with load balancing Using AntNet System

This paper is based on analysis of the performance of load balancing and route optimization in computerized networks. The complete system model shows the scenario of Packet distribution between nodes, and if congestion occurs due to traffic then Packet to be failed. The model used the AntNet system for simulate the network. The simulation runs on the ants behavior for the load balancing of the network. The ants travel across the network between alternative chosen pairs of nodes; as they travel they deposit pheromones from their source node, collect the information of the route and the traffic congestion encountered on their journey. They select their path at each next node according the distribution of pheromones at each node. Packets between nodes are routed of the pheromone distributions at each next node. The performance of the network is proportional to packets which are failed. This model also shows the adaptivity of the system; the nodes are removed from the network, system finds the alternative chosen paths without system degradation and controls the performance of routing. Keywords- AntNet Algorithm, Ant Colony Optimization (ACO), Routing, Load Balancing, Dynamic, Adaptive, Simulation, Communication Networks.

Review article: Achieving maximum reliability in fault tolerant network design for variable networks

The objective of this paper is to present a novel method to achieve maximum reliability for fault tolerant optimal network design when network has variable size. Reliability calculation is most important and critical component when fault tolerant optimal network design is required. A network must be supplied with certain parameters that guarantee proper functionality and maintainability under worse situations. Many alternative methods for measuring reliability have been stated in literature for optimal network design. Most of these methods mentioned in literature for evaluating reliability may be analytical and simulation based. These methods provide significant way to compute reliability when network has limited size. Also, significant computational effort is required for growing variable sized networks. Therefore, a novel neural network method is presented to achieve significant high reliability for fault tolerant optimal network design in highly growing variable networks. This paper computes reliability with improved learning rate gradient descent based neural network method. The result shows that improved optimal network design with maximum reliability is achievable by novel neural network at manageable computational cost.

Improved neural approach in maximising reliability for increasing networks

A method is presented to maximise reliability for increasing networks. A neural approach is combined with reliability values of each link obtained from minimal cuts in increasing network. The method simply evaluates minimal cuts from highly increasing networks and a two-dimensional combinatorial spectrum is obtained from an approximation formula for assigning reliability values for each link. These reliability values will be used in a neural approach as the upper-bound on reliability for improving reliability. An increasing network is considered with random failure in links and nodes. Evaluating minimal cuts in increasing networks requires significant computational effort, but, when approximated, computational time reduces significantly. The result shows significant improvement in reliability for increasing networks design when an approximated combinatorial spectrum is used as input to the neural networks. The approach reduces significantly the computational effort for reliability calculation.

Approach for improving reliability in optimal network design

This paper present an optimal artificial neural network approach for improving upper bound on link reliability in optimal network design. Improving reliability in a growing variable-sized network is an important parameter for optimal network design. Many alternative methods for improving reliability have been used for optimal network design. Most of these methods, mentioned in the literature are simulation-based. These methods provide simple ways for measuring reliability when networks have limited size. These methods require significant computational effort and time for growing variable-sized networks. An optimal neural network method is therefore proposed for reliability improvement in optimal network design. The proposed algorithm has two phases: experimental setup and optimal phase. Experimental setup phase scans all possible network topologies for reliability measures. And, optimal phase constructs optimal network design with improved reliability upper-bound. Both neural networks were studied with fixed and varying links. Results are grouped using cross-validation method showing that the optimised artificial neural network approach gives precise measures for significant reliability improvement than the upper-bound than heuristic-based approach. Results show that the optimised ANN produces optimal network designs and reliability measures at reasonable computational cost.