Topic
Distributed algorithm
About: Distributed algorithm is a research topic. Over the lifetime, 20416 publications have been published within this topic receiving 548109 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: The dR*-tree is introduced, a distributed spatial index structure in which the data is spread among multiple computers and the indexes of the data are replicated on every computer in the ‘shared-nothing’ architecture with multiple computers interconnected through a network.
Abstract: The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we present PDBSCAN, a parallel version of this algorithm. We use the ‘shared-nothing’ architecture with multiple computers interconnected through a network. A fundamental component of a shared-nothing system is its distributed data structure. We introduce the dRa-tree, a distributed spatial index structure in which the data is spread among multiple computers and the indexes of the data are replicated on every computer. We implemented our method using a number of workstations connected via Ethernet (10 Mbit). A performance evaluation shows that PDBSCAN offers nearly linear speedup and has excellent scaleup and sizeup behavior.
296 citations
TL;DR: In this work, the θ-logarithmic barrier-based method is employed to reformulate the economic dispatch problem, and the consensus-based approach is considered for developing fully distributed technology-enabled algorithms.
Abstract: To reduce information exchange requirements in smart grids, an event-triggered communication-based distributed optimization is proposed for economic dispatch. In this work, the $\theta$ -logarithmic barrier-based method is employed to reformulate the economic dispatch problem, and the consensus-based approach is considered for developing fully distributed technology-enabled algorithms. Specifically, a novel distributed algorithm utilizes the minimum connected dominating set (CDS), which efficiently allocates the task of balancing supply and demand for the entire power network at the beginning of economic dispatch. Further, an event-triggered communication-based method for the incremental cost of each generator is able to reach a consensus, coinciding with the global optimality of the objective function. In addition, a fast gradient-based distributed optimization method is also designed to accelerate the convergence rate of the event-triggered distributed optimization. Simulations based on the IEEE 57-bus test system demonstrate the effectiveness and good performance of proposed algorithms.
295 citations
22 Feb 1999
TL;DR: It is asserted that system software, not the programmer, should manage the task of distributed decomposition and presented Coign, an automatic distributed partitioning system that significantly eases the development of distributed applications.
Abstract: Although successive generations of middleware (such as RPC, CORBA, and DCOM) have made it easier to connect distributed programs, the process of distributed application decomposition has changed little: programmers manually divide applications into sub-programs and manually assign those sub-programs to machines. Often the techniques used to choose a distribution are ad hoc and create one-time solutions biased to a specific combination of users, machines, and networks. We assert that system software, not the programmer, should manage the task of distributed decomposition. To validate our assertion we present Coign, an automatic distributed partitioning system that significantly eases the development of distributed applications. Given an application (in binary form) built from distributable COM components, Coign constructs a graph model of the application's inter-component communication through scenario-based profiling. Later, Coign applies a graph-cutting algorithm to partition the application across a network and minimize execution delay due to network communication. Using Coign, even an end user (without access to source code) can transform a non-distributed application into an optimized, distributed application. Coign has automatically distributed binaries from over 2 million lines of application code, including Microsoft '5 PhotoDraw 2000 image processor. To our knowledge, Coign is the first system to automatically partition and distribute binary applications.
295 citations
TL;DR: This work presents an algorithm where users update their transmitter signature sequences sequentially, in a distributed fashion, by using available receiver measurements, and proves that the algorithm converges to a set of orthogonal signature sequences when the number of users is less than or equal to the processing gain.
Abstract: Optimum signature sequence sets that maximize the capacity of single-cell synchronous code division multiple access (CDMA) systems have been identified. Optimum signature sequences minimize the total squared correlation (TSC); they form a set of orthogonal sequences, if the number of users is less than or equal to the processing gain, and a set of Welch (1994) bound equality (WBE) sequences, otherwise. We present an algorithm where users update their transmitter signature sequences sequentially, in a distributed fashion, by using available receiver measurements. We show that each update decreases the TSC of the set, and produces better signature sequence sets progressively. We prove that the algorithm converges to a set of orthogonal signature sequences when the number of users is less than or equal to the processing gain. We observe and conjecture that the algorithm converges to a WBE set when the number of users is greater than the processing gain. At each step, the algorithm replaces one signature sequence from the set with the normalized minimum mean squared error (MMSE) receiver corresponding to that signature sequence. Since the MMSE filter can be obtained by a distributed algorithm for each user, the proposed algorithm is amenable to distributed implementation.
294 citations
TL;DR: A distributed version of GENITOR which uses many smaller distributed populations in place of a single large population is introduced, and is able to optimize a broad range of sample problems more accurately and more consistently than GENITor with a single population.
Abstract: GENITOR is a genetic algorithm which employs one-at-a-time reproduction and allocates reproductive opportunities according to rank to achieve selective pressure. Theoretical arguments and empirical evidence suggest that GENITOR is less vulnerable to some of the biases that degrade performance in standard genetic algorithms. A distributed version of GENITOR which uses many smaller distributed populations in place of a single large population is introduced. GENITOR II is able to optimize a broad range of sample problems more accurately and more consistently than GENITOR with a single population. GENITOR II also appears to be more robust than a single population genetic algorithm, yielding better performance without parameter tuning. We present some preliminary analyses to explain the performance advantage of the distributed algorithm. A distributed search is shown to yield improved search on several classes of problems, including binary encoded feedforward neural networks, the Traveling Salesman Pr...
294 citations