Reinforcing Reachable Routes

This paper studies the evaluation of routing algorithms from the perspective of reachability routing, where the goal is to determine all paths between a sender and a receiver. Reachability routing is becoming relevant with the changing dynamics of the Internet and the emergence of low-bandwidth wireless/ad-hoc networks. We make the case for reinforcement learning as the framework of choice to realize reachability routing, within the confines of the current Internet infrastructure. The setting of the reinforcement learning problem offers several advantages,including loop resolution, multi-path forwarding capability, cost-sensitive routing, and minimizing state overhead, while maintaining the incremental spirit of current backbone routing algorithms. We identify research issues in reinforcement learning applied to the reachability routing problem to achieve a fluid and robust backbone routing framework. This paper also presents the design, implementation and evaluation of a new reachability routing algorithm that uses a model-based approach to achieve cost-sensitive multi-path forwarding; performance assessment of the algorithm in various troublesome topologies shows consistently superior performance over classical reinforcement learning algorithms. The paper is targeted toward practitioners seeking to implement a reachability routing algorithm

ACO-based routing algorithms for wireless mesh networks

The popularity of Wireless Mesh Networks (WMNs) is growing exponentially in recent years, due to their flexible deployment and compatible communication features. As a key technology for next-generation wireless networking, WMNs promise an attractive future to both academic and industrial world. However, current WMNs are short in optimal routing protocols. Instead, many WMNs use the routing algorithms from ad hoc networks, which have different network features. Thus, routing becomes the most urgent issue that needs to be solved. In this thesis, routing problems in WMNs are discussed in different aspects, and then several proposed solutions in state-of-the-art are introduced with their advantages and disadvantages. Ant-In-Mesh routing protocol and the enhanced version are proposed for WMNs, inspired by traditional Ant Colony Optimization (ACO) algorithm, to deal with new challenging characters of WMNs. Periodical Mesh update is performed between neighbors, to keep the network alive. With these updated information at all the hosts, various Ants can collect the fresh routing data while they are launched for different purposes, also, the per-hop and end-to-end routing metrics can be calculated. Upon new connection requests, route discovery is carried out. After the routes are set up, proactive route maintenance is performed on each route. Several popular routing protocols and our algorithms are simulated. and compared using Qualnet. The simulation results show that our algorithms outperform the others, in terms of packet delivery ratio and end-to-end delay, as the mobility and network size increase

Modeling and simulation of routing protocol for ad hoc networks combining queuing network analysis and ANT colony algorithms

The field of Mobile Ad hoc Networks (MANETs) has gained an important part of the interest of researchers and become very popular in last few years. MANETs can operate without fixed infrastructure and can survive rapid changes in the network topology. They can be studied formally as graphs in which the set of edges varies in time. The main method for evaluating the performance of MANETs is simulation. Our thesis presents a new adaptive and dynamic routing algorithm for MANETs inspired by the Ant Colony Optimization (ACO) algorithms in combination with network delay analysis. Ant colony optimization algorithms have all been inspired by a specific foraging behavior of ant colonies which are able to find, if not the shortest, at least a very good path connecting the colony’s nest with a source of food. Our evaluation of MANETs is based on the evaluation of the mean End-to-End delay to send a packet from source to destination node through a MANET. We evaluated the mean End-to-End delay as one of the most important performance evaluation metrics in computer networks. Finally, we evaluate our proposed ant algorithm by a comparative study with respect to one of the famous On-Demand (reactive) routing protocols called Ad hoc On-Demand Distance Vector (AODV) protocol. The evaluation shows that, the ant algorithm provides a better performance by reducing the mean End-to-End delay than the AODV algorithm. We investigated various simulation scenarios with different node density and pause times. Our new algorithm gives good results under certain conditions such as, increasing the pause time and decreasing node density. The scenarios that are applied for evaluating our routing algorithm have the following assumptions: 2-D rectangular area, no obstacles, bi-directional links, fixed number of nodes operate for the whole simulation time and nodes movements are performed according to the Random Waypoint Mobility (RWM) or the Boundless Simulation Area Mobility (BSAM) model. KEYWORDS: Ant Colony Optimization (ACO), Mobile Ad hoc Network (MANET), Queuing Network Analysis, Routing Algorithms, Mobility Models, Hybrid Simulation

Self-organisation in ant-based peer-to-peer systems

Peer-to-peer systems are a highly decentralised form of distributed computing, which has ad¬ vantages of robustness and redundancy over more centralised systems. When the peer-to-peer system has a stable and static population of nodes, variations and bursts in traffic levels cause momentary levels of congestion in the system, which have to be dealt with by routing policies implemented within the peer-to-peer system in order to maintain efficient and effective routes.Peer-to-peer systems, however, are dynamic in nature, as they exhibit churn, i.e. nodes enter and leave the system during their use. This dynamic nature makes it difficult to identify consistent routing policies that ensure a reasonable proportion of traffic in the system is routed successfully to its destination. Studies have shown that chum in peer-to-peer systems is difficult to model and characterise, and further, is difficult to manage.The task of creating and maintaining efficient routes and network topologies in dynamic environments, such as those described above, is one of dynamic optimisation. Complex adap¬ tive systems such as ant colony optimisation and genetic algorithms have been shown to display adaptive properties in dynamic environments. Although complex adaptive systems have been applied to a small number of dynamic optimisation problems, their application to dynamic opti¬ misation problems is new in general and also application to routing in dynamic environments is new. Further, the problem characteristics and conditions under which these algorithms perform well, and the reasons for doing so, are not yet fully understood. The assessment of how good the complex adaptive systems are at creating solutions to the dynamic routing optimisation problem detailed above is dependent on the metrics used to make the measurements.A contribution of this thesis is the development of a theoretical framework within which we can analyse the behaviours and responses of any peer-to-peer system. We do this by considering a peer-to-peer system to be a graph generating algorithm, which has input parameters and has outputs which can be measured using topological metrics and statistics that characterise the traffic through the network. Specifically, we consider the behaviour of an ant-based peer-to-peer system and we have designed and implemented an ant-based peer-to-peer simulator to enable this.Recently methods for characterising graphs by their scaling properties have been developed and a small number of distinct categories of graphs have been identified (such as random graphs, lattices, small world graphs, and scale-free graphs). These graph characterisation methods have also enabled the creation of new metrics to enable measurements of properties of the graphs belonging to different categories.We use these new graph characterisation techniques mentioned above and the associated metrics to implement a systematic approach to the analysis of the behaviour of our ant peer-to-peer system. We present the results of a number of simulation runs of our system initiated with a range of values of key parameters. The resulting networks are then analysed from both the point of view of traffic statistics, and also topological metrics.Three sets of experiments have been designed and conducted using the simulator created during this project. The first set, equilibrium experiments, consider the behaviour of the system when the number of operational nodes in the system is constant and also the demand placed on the system is constant. The second set of experiments considers the changes that occur when there are bursts in traffic levels or the demand placed on the system. The final set considers the effect of churn in the system, where nodes enter and leave the system during its operation. In crafting the experiments we have been able to identify many of the major control parameters of the ant-based peer-to-peer system.A further contribution of this thesis is the results of the experiments which show that under conditions of network congestion the ant peer-to-peer system becomes very brittle. This is characterised by small average path lengths, a low proportion of ants successfully getting through to their destination node, and also a low average degree of the nodes in the network. This brittleness is made worse when nodes fail and also when the demand applied to the system changes abruptly.A further contribution of this thesis is the creation of a method of ranking the topology of a network with respect to a target topology. This method can be used as the basis for topological control (i.e. the distributed self-assembly of network topologies within a peer-to-peer system that have desired topological properties) and assessing how best to modify a topology in order to move it closer to the desired (or reference) topology. We use this method when measuring the outcome of our experiments to determine how far the resulting graph is from a random graph. In principle this method could be used to measure the distance of the graph of the peer-to-peer network from any reference topology (e.g. a lattice or a tree).A final contribution of this thesis is the definition of a distributed routing policy which uses a measure of confidence that nodes in the system are in an operational state when making calculations regarding onward routing. The method of implementing the routing algorithm within the ant peer-to-peer system has been specified, although this has not been implemented within this thesis. It is conjectured that this algorithm would improve the performance of the ant peer-to-peer system under conditions of churn.The main question this thesis is concerned with is how the behaviour of the ant-based peer-to-peer system can best be measured using a simulation-based approach, and how these measurables can be used to control and optimise the performance of the ant-based peer-to-peer system in conditions of equilibrium, and also non-equilibrium (specifically varying levels of bursts in traffic demand, and also varying rates of nodes entering and leaving the peer-to-peer system)

An improved MultiAnts-Aodv routing protocol for ad hoc wireless networks

Compared to the conventional table-driven and on-demand routing protocols, a hybrid routing protocol [71], which uses mobile agents and reactive route discovery, introduced a more realistic solution to this problem. However, the mobile agents were not fully exploited in this protocol. In this thesis research, we will propose an improved MultiAnts-AODV routing protocol based on ant-AODV The goal of our design is to reduce the end-to-end delay and route discovery latency. To achieve a better performance, the communication scheme among the agents is strengthened. We also present an improved navigation algorithm for mobile agents to update the routing tables more efficiently. We extend the routing table to reduce the latency of routing discovery in case of link failures. The simulation based comparisons among several navigation algorithms are also presented

Using swarm intelligence for distributed job scheduling on the grid

With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. Grids are playing an important and growing role in today networks. The huge amount of computations a Grid can fulfill in a specific time cannot be done by the best super computers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized by a good load balancing algorithm. The purpose of such algorithms is to make sure all nodes are equally involved in Grid computations. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One is based on ant colony optimization and is called AntZ, the other one is based on particle swarm optimization and is called ParticleZ. Distributed load balancing does not incorporate a single point of failure in the system. In the AntZ algorithm, an ant is invoked in response to submitting a job to the Grid and this ant surfs the network to find the best resource to deliver the job to. In the ParticleZ algorithm, each node plays a role as a particle and moves toward other particles by sharing its workload among them. We will be simulating our proposed approaches using a Grid simulation toolkit (GridSim) dedicated to Grid simulations. The performance of the algorithms will be evaluated using several performance criteria (e.g. makespan and load balancing level). A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches will also be provided. Experimental results show the proposed algorithms (AntZ and ParticleZ) can perform very well in a Grid environment. In particular, the use of particle swarm optimization, which has not been addressed in the literature, can yield better performance results in many scenarios than the ant colony approach