Distributed algorithm

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

Distributed database systems: where are we now?

Abstract: The authors explain what is meant by a distributed database system and discuss its characteristics. They survey the state of distributed database technology, focusing on how well products meet the goals of transparent management of distributed and replicated data, reliability through distributed transactions, better performance, and easier, more economical system expansion. They then consider unsolved problems with regard to network scaling, distribution design, distributed query processing, distributed transaction processing, integration with distributed operating systems, and distributed multidatabase systems. >

Elements of Distributed Algorithms: Modeling and Analysis with Petri Nets

Abstract: Distributed Computing is rapidly becoming the principal computing paradigm in diverse areas of computing, communication, and control. Processor clusters, local and wide area networks, and the information highway evolved a new kind of problems which can be solved with distributed algorithms.In this textbook a variety of distributed algorithms are presented independently of particular programming languages or hardware, using the graphically suggestive technique of Petri nets which is both easy to comprehend intuitively and formally rigorous. By means of temporal logic the author provides surprisingly simple yet powerful correctness proofs for the algorithms.The scope of the book ranges from distributed control and synchronization of two sites up to algorithms on any kind of networks. Numerous examples show that description and analysis of distributed algorithms in this framework are intuitive and technically transparent.

Distributed Link Scheduling with Constant Overhead

Abstract: This paper proposes a new class of simple, distributed algorithms for scheduling in wireless networks. The algorithms generate new schedules in a distributed manner via simple local changes to existing schedules. The class is parameterized by integers $k\geq 1$. We show that algorithm $k$ of our class achieves $k/(k+2)$ of the capacity region, for every $k\geq 1$. The algorithms have small and constant worst-case overheads: in particular, algorithm $k$ generates a new schedule using {\em (a)} time less than $4k+2$ round-trip times between neighboring nodes in the network, and {\em (b)} at most three control transmissions by any given node, for any $k$. The control signals are explicitly specified, and face the same interference effects as normal data transmissions. Our class of distributed wireless scheduling algorithms are the first ones guaranteed to achieve any fixed fraction of the capacity region while using small and constant overheads that do not scale with network size. The parameter $k$ explicitly captures the tradeoff between control overhead and scheduler throughput performance and provides a tuning knob protocol designers can use to harness this trade-off in practice.

Some simple distributed algorithms for sparse networks

Abstract: We give simple, deterministic, distributed algorithms for computing maximal matchings, maximal independent sets and colourings. We show that edge colourings with at most 2Δ-1 colours, and maximal matchings can be computed within O(log* n + Δ) deterministic rounds, where Δ is the maximum degree of the network. We also show how to find maximal independent sets and (Δ + 1)-vertex colourings within O(log* n + Δ2) deterministic rounds. All hidden constants are very small and the algorithms are very simple.

Compressed Wideband Sensing in Cooperative Cognitive Radio Networks

Abstract: In emerging cognitive radio (CR) networks with spectrum sharing, the first cognitive task preceding any dynamic spectrum access is the sensing and identification of spectral holes in wireless environments. This paper develops a distributed compressed spectrum sensing approach for (ultra-)wideband CR networks. Compressed sensing is performed at local CRs to scan the very wide spectrum at practical signal-acquisition complexity. Meanwhile, spectral estimates from multiple local CR detectors are fused to collect spatial diversity gain, which improves the sensing quality especially under fading channels. New distributed consensus algorithms are developed for collaborative sensing and fusion. Using only one-hop local communications, these distributed algorithms converge fast to the globally optimal solutions even for multi-hop CR networks, at low communication and computation load scalable to the network size.