Mark S. Withall

Network visualisation: a review

As communication networks increase in performance and complexity, and more dependence is placed upon them, it becomes ever more important that their behaviour is understood in an efficient and timely manner. Visualisation is an established technique for the presentation of the vast volume of data yielded in monitoring such networks. It is apparent, however, that much of the work in this area has been performed in isolation, and it is timely that a review of this research is conducted. The techniques for the visualisation of communication networks and related measurements are surveyed. The research is classified by the type of visualisation used and is separated into three classes: geographic visualisations, in which the data are presented with respect to the physical location of nodes in the network; abstract topological visualisations, in which the relationships between nodes are presented independently of physical location; and plot-based visualisation, in which the focus is a single point in the network, often presented with respect to time. The research in this area is reviewed and the techniques proposed are discussed in terms of these three classes.

An improved representation for evolving programs

A representation has been developed that addresses some of the issues with other Genetic Program representations while maintaining their advantages. This combines the easy reproduction of the linear representation with the inheritable characteristics of the tree representation by using fixed-length blocks of genes representing single program statements. This means that each block of genes will always map to the same statement in the parent and child unless it is mutated, irrespective of changes to the surrounding blocks. This method is compared to the variable length gene blocks used by other representations with a clear improvement in the similarity between parent and child. In addition, a set of list evaluation and manipulation functions was evolved as an application of the new Genetic Program components. These functions have the common feature that they all need to be 100% correct to be useful. Traditional Genetic Programming problems have mainly been optimization or approximation problems. The list results are good but do highlight the problem of scalability in that more complex functions lead to a dramatic increase in the required evolution time.

Allocating Railway Platforms Using A Genetic Algorithm

This paper describes an approach to automating railway station platform allocation. The system uses a Genetic Algorithm (GA) to find how a station’s resources should be allocated. Real data is used which needs to be transformed to be suitable for the automated system. Successful or ‘fit’ allocations provide a solution that meets the needs of the station schedule including platform re-occupation and various other constraints. The system associates the train data to derive the station requirements. The Genetic Algorithm is used to derive platform allocations. Finally, the system may be extended to take into account how further parameters that are external to the station have an effect on how an allocation should be applied. The system successfully allocates around 1000 trains to platforms in around 30 seconds requiring a genome of around 1000 genes to achieve this.

Challenges in the capture and dissemination of measurements from high-speed networks

The production of a large-scale monitoring system for a high-speed network leads to a number of challenges. These challenges are not purely technical but also socio-political and legal. The number of stakeholders in such monitoring activity is large including the network operators, the users, the equipment manufacturers and, of course, the monitoring researchers. The MASTS project (measurement at all scales in time and space) was created to instrument the high-speed JANET Lightpath network and has been extended to incorporate other paths supported by JANET(UK). Challenges the project has faced included: simple access to the network; legal issues involved in the storage and dissemination of the captured information, which may be personal; the volume of data captured and the rate at which these data appear at store. To this end, the MASTS system will have established four monitoring points each capturing packets on a high-speed link. Traffic header data will be continuously collected, anonymised, indexed, stored and made available to the research community. A legal framework for the capture and storage of network measurement data has been developed which allows the anonymised IP traces to be used for research purposes.

TRAIN TIMETABLE GENERATION USING GENETIC ALGORITHMS

The scheduling of railway trains has been a research problem for many years. Many of the choices required are not known a priori and require exploration of the problem to determine them. A modular Genetic system was designedmake the evaluation function and preparation of the timetable tractable. The Genetic system consists of a Genome, split into Chromosomes so the extra choices that become known throughout the evolution can be added to the Chromosomes. A weighted fitness function and a multiobjective non-dominated fitness function were tried, and then partial objective ranking was added. The system has tackled a mixture of problems has produced promising results.

A parallel approach to row-based VLSI layout using stochastic hill-climbing

Parallel algorithms based on stochastic hill-climbing and parallel algorithms based on simple elements of a genetic algorithm for the one-sided bipartite crossing number problem, used in row-based VLSI layout, were investigated. These algorithms were run on a PVM cluster. The experiments show that the parallel approach does not bring faster computation but it does, however, much more importantly, bring a better quality solution to the problem, i.e. it generates drawings with lower numbers of pairwise edge crossings.

Packet transmission optimisation using genetic algorithms

A Genetic Algorithm (GA) is used to optimise the parameters for a sequence of packets sent over the Internet. Only the parameters that a client machine can change are used and the fitness is based on the delay time returned by the Traceroute program. The GA performance is compared to a fixed packet size with no priority used to assess the status of the network. The GA generally performed to the same level as the control settings but in some cases significant improvements were made.

Honey Plotter and the Web of Terror

Honeypots are a useful tool for discovering the distribution of malicious traffic on the Internet and how that traffic evolves over time. In addition, they allow an insight into new attacks appearing. One major problem is analysing the large amounts of data generated by such honeypots and correlating between multiple honeypots. Honey Plotter is a web-based query and visualisation tool to allow investigation into data gathered by a distributed honeypot network. It is built on top of a relational database, which allows great flexibility in the questions that can be asked and has automatic generation of visualisations based on the results of queries. The main focus is on aggregate statistics but individual attacks can also be analysed. Statistical comparison of distributions is also provided to assist with detecting anomalies in the data; helping separate out common malicious traffic from new threats and trends. Two short case studies are presented to give an example of the types of analysis that can be performed.