Distributed algorithm

About: Distributed algorithm is a research topic. Over the lifetime, 20416 publications have been published within this topic receiving 548109 citations.

Pattern-Oriented Software Architecture, a Pattern Language for Distributed Computing

Abstract: Providing a guide to the best practices in key areas of distributed computing, this book describes a single pattern language that links many patterns relevant to distributed computing.

Particle swarm based Data Mining Algorithms for classification tasks

Abstract: Particle Swarm Optimisers are inherently distributed algorithms where the solution for a problem emerges from the interactions between many simple individual agents called particles. This article proposes the use of the Particle Swarm Optimiser as a new tool for Data Mining. In the first phase of our research, three different Particle Swarm Data Mining Algorithms were implemented and tested against a Genetic Algorithm and a Tree Induction Algorithm (J48). From the obtained results, Particle Swarm Optimisers proved to be a suitable candidate for classification tasks. The second phase was dedicated to improving one of the Particle Swarm optimiser variants in terms of attribute type support and temporal complexity. The data sources here used for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that Particle Swarm Data Mining Algorithms are competitive, not only with other evolutionary techniques, but also with industry standard algorithms such as the J48 algorithm, and can be successfully applied to more demanding problem domains.

An Optimal and Distributed Method for Voltage Regulation in Power Distribution Systems

Abstract: This paper addresses the problem of voltage regulation in power distribution networks with deep-penetration of distributed energy resources, e.g., renewable-based generation, and storage-capable loads such as plug-in hybrid electric vehicles. We cast the problem as an optimization program, where the objective is to minimize the losses in the network subject to constraints on bus voltage magnitudes, limits on active and reactive power injections, transmission line thermal limits and losses. We provide sufficient conditions under which the optimization problem can be solved via its convex relaxation. Using data from existing networks, we show that these sufficient conditions are expected to be satisfied by most networks. We also provide an efficient distributed algorithm to solve the problem. The algorithm adheres to a communication topology described by a graph that is the same as the graph that describes the electrical network topology. We illustrate the operation of the algorithm, including its robustness against communication link failures, through several case studies involving 5-, 34-, and 123-bus power distribution systems.

Crash Recovery in a Distributed Data Storage System

Abstract: An algorithm is described which guarantees reliable storage of data in a distributed system, even when different portions of the data base, stored on separate machines, are updated as part of a single transaction. The algorithm is implemented by a hierarchy of rather simple abstractions, and it works properly regardless of crashes of the client or servers. Some care is taken to state precisely the assumptions about the physical components of the system (storage, processors and communication).

Optical network design and restoration

Abstract: The explosion of data traffic and the availability of enormous bandwidth via dense wavelength division multiplexing (DWDM) and optical amplifier (OA) technologies make it important to study optical layer networking and restoration. This paper is concerned with fast distributed restoration and provisioning for generic mesh-based optical networks. We consider two problems of practical importance: determining the best restoration route for each wavelength demand, given the network topology and the capacities and primary routes of all demands, and determining primary and restoration routes for each wavelength demand to minimize network capacity and cost. The approach we propose for both problems is based on precomputing. For each problem, we describe specific algorithms used for computing routes. We also describe endpoint-based failure detection, message flows, and cross-connect actions for execution of fast restorations. Finally, we report test results for large carrier-scale networks that include both the computational performance of the optimization algorithms and the restoration speed obtained by simulation. Our results indicate that subsecond restoration, high capacity efficiency, and scalability can be achieved without fault isolation and with moderate processing. We also discuss methods for scaling algorithms to problems with very large numbers of demands. The wavelength routing and restoration algorithms, the failure detection, and the message exchange and activation architectures we propose are collectively known as WaveStar™ advanced routing platform.