Distributed algorithm

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

Distributed Finite-Time Economic Dispatch of a Network of Energy Resources

Abstract: The physical power infrastructure is moving from the centralized structure to the distributed structure for enabling integration of distributed energy resources. Due to the large number of distributed energy resources, optimal resource allocation is an important and challenging problem. To solve this problem, a distributed and fast economic dispatch algorithm is provided to share the power generation task in an optimized fashion among a set of distributed energy resources, which can address both generation-demand equality and generation capacity inequality constraints. Different from most existing economic dispatch algorithms, the finite-time convergence to the optimal value is achieved, which makes more sense in real applications. Several case studies are discussed and tested to validate the proposed methods.

Optimal multipath congestion control and request forwarding in Information-Centric Networks

Abstract: The evolution of the Internet into a distributed Information access system calls for a paradigm shift to enable an evolvable future network architecture. Information-Centric Networking (ICN) proposals rethink the communication model around named data, in contrast with the host-centric transport view of TCP/IP. Information retrieval is natively pull-based, driven by user requests, point-to-multipoint and intrinsically coupled with in-network caching. In this paper, we tackle the problem of joint multipath congestion control and request forwarding in ICN for the first time. We formulate it as a global optimization problem with the twofold objective of maximizing user throughput and minimizing overall network cost. We solve it via decomposition and derive a family of optimal congestion control strategies at the receiver and of distributed algorithms for dynamic request forwarding at network nodes. An experimental evaluation of our proposal is carried out in different network scenarios to assess the performance of our design and to highlight the benefits of an ICN approach.

A Lower Bound on Convergence of a Distributed Network Consensus Algorithm

Abstract: This paper gives a lower bound on the convergence rate of a class of network consensus algorithms. Two different approaches using directed graphs as a main tool are introduced: one is to compute the "scrambling constants" of stochastic matrices associated with "neighbor shared graphs" and the other is to analyze random walks on a sequence of graphs. Both approaches prove that the time to reach consensus within a dynamic network is logarithmic in the relative error and is in worst case exponential in the size of the network.

Distributed diagnosis of discrete-event systems using Petri nets

Abstract: The problem of detecting and isolating fault events in dynamic systems modeled as discrete-event systems is considered. The modeling formalism adopted is that of Petri nets with labeled transitions, where some of the transitions are labeled by different types of unobservable fault events. The Diagnoser Approach for discrete-event systems modeled by automata developed in earlier work is adapted and extended to on-line fault diagnosis of systems modeled by Petri nets, resulting in a centralized diagnosis algorithm based on the notion of "Petri net diagnosers". A distributed version of this centralized algorithm is also presented. This distributed version assumes that the Petri net model of the system can be decomposed into two place-bordered Petri nets satisfying certain conditions and that the two resulting Petri net diagnosers can exchange messages upon the occurrence of observable events. It is shown that this distributed algorithm is correct in the sense that it recovers the same diagnostic information as the centralized algorithm. The distributed algorithm provides an approach for tackling fault diagnosis of large complex systems.

Hyperbolic embedding of internet graph for distance estimation and overlay construction

Abstract: Estimating distances in the Internet has been studied in the recent years due to its ability to improve the performance of many applications, e.g., in the peer-to-peer realm. One scalable approach to estimate distances between nodes is to embed the nodes in some d dimensional geometric space and to use the pair distances in this space as the estimate for the real distances. Several algorithms were suggested in the past to do this in low dimensional Euclidean spaces. It was noted in recent years that the Internet structure has a highly connected core and long stretched tendrils, and that most of the routing paths between nodes in the tendrils pass through the core. Therefore, we suggest in this work, to embed the Internet distance metric in a hyperbolic space where routes are bent toward the center. We found that if the curvature, that defines the extend of the bending, is selected in the adequate range, the accuracy of Internet distance embedding can be improved. We demonstrate the strength of our hyperbolic embedding with two applications: selecting the closest server and building an application level multicast tree. For the latter, we present a distributed algorithm for building geometric multicast trees that achieve good trade-offs between delay (stretch) and load (stress). We also present a new efficient centralized embedding algorithm that enables the accurate embedding of short distances, something that have never been done before.