Alberto Garcia

Alternative multiple spanning tree protocol (AMSTP) for optical Ethernet backbones

The availability and affordable cost of Gigabit and 10 Gigabit Ethernet switches has impacted the deployment of metropolitan area networks (MAN) and campus networks. This paper presents a new protocol, the alternative multiple spanning tree protocol (AMSTP), that uses multiple source based spanning trees for backbones using Ethernet switches. It provides minimum paths and more efficient usage of optical backbone infrastructure than currently proposed protocols such as resilient packet ring and rapid spanning tree. The protocol exhibits features similar to MAC routing protocols like Link State Over MAC (LSOM) such as optimum path and effective infrastructure usage, without requiring MAC routing due to the use of the spanning tree protocol paradigm. AMSTP is not restricted to specific topologies such as ring or tree, but performs efficiently in arbitrary topologies. Among the application areas are optical backbones of campus and MANs.

An ontology-driven decision support system for high-performance and cost-optimized design of complex railway portal frames

Highlights? We model the design process of complex railway electrification structures. ? We provide ontology and rules to model railway engineers knowledge. ? Increasing automation will increase infrastructure quality and will reduce design and construction costs. ? Our tool reduces design time from days to minutes, getting optimized structures compliant with railway normative. Electrification structures design for railway systems is a crucial and complex process, since it compounds plenty of infrastructure elements, design decisions, and calculation conditions. In this paper, an ontology-driven decision support system for designing complex railway portal frames is presented and developed. A knowledge-rules database has been also developed relying on experts knowledge and complying with railway standards. Our system outperforms the current portal frames design methods by decreasing construction time and costs. As a result, an intelligent computer-aided design tool is provided, thus facilitating the task of seeking for the optimal portal frame, which is geometrically and structurally feasible, and cost-effective.

A model to obtain optimal designs of railway overhead knuckle junctions using simulation

Abstract The design of overhead knuckle junctions in railway electrification is a crucial and complex problem. Non-optimal overhead knuckle junction designs cause limitations in train speed and, most important, malfunctions and breakages. Most railway companies have regulations for the design of overhead knuckle junctions. Those regulations have been defined by the experience, but, as far as we know, there are no computerized software tools to help with the task of designing and testing optimal solutions for overhead knuckle junctions. In this paper we present a simulator that allows to look for optimal configurations of overhead knuckle junctions. The simulator starts from a model based on the description of the problem that includes all the significant elements that may affect the design process, in order to find an optimal solution in terms of reliability and safety. The obtained design will be the more reliable to face failures, such as excessive wire and pantograph wears, wrong geometry configurations of the catenary, or electricity supply notches. The simulator also allows to evaluate current designs, so as to prove their possible flaws. This paper describes the simulation algorithm developed, the input data needed to define the experiments, and the achieved results. As the simulator requires heavy computational resources, high productivity parallel issues have been included in the implementation to exploit current multi-core processors. The validation and performance evaluations were made in the paper through the simulation of overhead knuckle junction designs over real switches. Their analysis will show the feasibility and applicability of our simulator.

A Cloudification Methodology for Numerical Simulations

Many scientific areas make extensive use of computer simulations to study complex real-world processes. These computations are typically very resource-intensive and present scalability issues as experiments get larger, even in dedicated clusters since they are limited by their own hardware resources. Cloud computing raises as an option to move forward into the ideal unlimited scalability by providing virtually infinite resources, yet applications must be adapted to this new paradigm. We propose a generalist cloudification method based in the MapReduce paradigm to migrate numerical simulations into the cloud to provide greater scalability. We analysed its viability by applying it to a real-world simulation and running the resulting implementation on Hadoop YARN over Amazons EC2. Our tests show that the cloudified application is highly scalable and there is still a large margin to improve the theoretical model and its implementations, and also to extend it to a wider range of simulations.

Improving performance using computational compression through memoization

The objective of data compression is to avoid redundancy in order to reduce the size of the data to be stored or transmitted. In some scenarios, data compression may help to increase global performance by reducing the amount of data at a competitive cost in terms of global time and energy consumption. We have introduced computational compression as a technique for reducing redundant computation, in other words, to avoid carrying out the same computation with the same input to obtain the same output. In some scenarios, such as simulations, graphic processing, and so on, part of the computation is repeated using the same input in order to obtain the same output, and this computation could have an important cost in terms of global time and energy consumption. We propose applying computational compression by using memoization in order to store the results for future reuse and, in this way, minimize the use of the same costly computation. Although memoization was proposed for sequential applications in the 1980s, and there are some projects that have applied it in very specific domains, we propose a novel, domain-independent way of using it in high-performance applications, as a means of avoiding redundant computation.

A Multi-Objective Simulator for Optimal Power Dimensioning on Electric Railways using Cloud Computing

Power dimensioning and energy saving have been traditionally two main issues regarding the deployment of electric grids. Electric railways are also concerned about these issues, and simulators have been traditionally used to test such infrastructure deployments. The main goal of this paper is to present the Railway electric Power Consumption Simulator, a simulation model and tool for the railway energy provisioning problem. This simulator aims to propose electric railway infrastructure deployments, optimizing the quality of the electric flow supplied to train, as well as saving as much energy as possible. The paper describes the simulator structure, as well as the ontology used to translate railway infrastructure elements into an electric circuit. Because these two objectives are conflicting, a multi-objective optimization problem is formulated and solved. Finally, a standard railway scenario is used to illustrate the capabilities of the tool, trying to find the best electric substation placements in order to optimize such objectives. The evaluation shows how the tool can handle hundreds of simulated scenarios using Cloud Computing techniques.

A holistic approach to railway engineering design using a simulation framework

Simulators have become frequently used tools in railway infrastructure design. However, most of them could be improved by adding capabilities to increase their productivity. In this paper, we propose a simulation framework in the field of railway infrastructure design, which allows to increase the productivity of simulators by integrating as many aspects of the design process as possible. Also, we state that new generation simulators should be capable of generating and evaluating new solutions by themselves. The framework follows a holistic approach, focusing on four main issues: a) trade-off between accuracy and complexity; b) automatic generation and simulation of solutions; c) taking into account all parts in the design process (e.g. normative); and d) integrating experts knowledge and optimization metrics. A case study is provided through a real-world simulator of railway overhead air switches. The simulator is analyzed from the point of view of the proposed framework, indicating how the different layers are fulfilled. Finally, the usability and productivity of the simulator is demonstrated performing an evaluation using different study cases. The evaluation shows how a high number of scenarios are simulated, evaluated, and rated using optimization metrics, in order to find the best solution of the problems search space.