A performance evaluation study of two proposed distributed deadlock detection algorithms, the central controller and distributed deadlock detection algorithms, is described. The author examines and reports the magnitu...
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A performance evaluation study of two proposed distributed deadlock detection algorithms, the central controller and distributed deadlock detection algorithms, is described. The author examines and reports the magnitude by which these algorithms provide an increase in the throughput, and an increase in the number of blocked, restarted transactions. A performance evaluation model is presented for a distributed database environment. A list of performance parameters and evaluation criteria is included.< >
Efficient and distributed adaptive mesh construction and editing pose several challenges, including selecting the appropriate distributed data structure, choosing strategies for distributing computational load, and ma...
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Efficient and distributed adaptive mesh construction and editing pose several challenges, including selecting the appropriate distributed data structure, choosing strategies for distributing computational load, and managing inter-processor communication. distributed Combinatorial Maps permit the representation and editing of distributed 3D meshes. This paper addresses computation load and expands communication aspects through volume transfer operation and repartitioning strategies. This work is the first one defining such transfer for cells of any topology. We demonstrate the benefits of our method by presenting a parallel adaptive hexahedral subdivision operation, involving fully generic volumes, in a process including a conversion to conformal mesh and surface fitting. Our experiments compare different strategies using multithreading and MPI implementations to highlight the benefits of volume transfer. Special attention has been paid to generic aspects and adaptability of the framework.
Much progress has been made in distributed computing in the areas of distribution structure, open computing, fault tolerance, and security. Yet, writing distributed applications remains difficult because the programme...
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Much progress has been made in distributed computing in the areas of distribution structure, open computing, fault tolerance, and security. Yet, writing distributed applications remains difficult because the programmer has to manage models of these areas explicitly. A major challenge is to integrate the four models into a coherent development platform. Such a platform should make it possible to cleanly separate an application's functionality from the other four concerns. Concurrent constraint programming, an evolution of concurrent logic programming, has both the expressiveness and the formal foundation needed to attempt this integration. As a first step, we have designed and built a platform that separates an application's functionality from its distribution structure. We have prototyped several collaborative tools with this platform, including a shared graphic editor whose design is presented in detail. The platform efficiently implements distributed Oz, which extends the Oz language with constructs to express the distribution structure and with basic primitives for open computing, failure detection and handling, and resource control. Oz appears to the programmer as a concurrent object-oriented language with dataflow synchronization. Oz is based on a higher-order, state-aware, concurrent constraint computation model.
In this paper, we address the problem of distributed reconfiguration of networked control systems upon the removal of misbehaving sensors and actuators. In particular, we consider systems with redundant sensors and ac...
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In this paper, we address the problem of distributed reconfiguration of networked control systems upon the removal of misbehaving sensors and actuators. In particular, we consider systems with redundant sensors and actuators cooperating to recover from faults. Reconfiguration is performed while minimizing a steady-state estimation error covariance and quadratic control cost. A model-matching condition is imposed on the reconfiguration scheme. It is shown that the reconfiguration and its underlying computation can be distributed. Using an average dwell-time approach, the stability of the distributed reconfiguration scheme under finite-time termination is analyzed. The approach is illustrated in a numerical example.
This article focuses on distributed convex optimization problems over an unbalanced directed multiagent (no central coordinator) network with inequality constraints. The goal is to cooperatively minimize the sum of al...
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This article focuses on distributed convex optimization problems over an unbalanced directed multiagent (no central coordinator) network with inequality constraints. The goal is to cooperatively minimize the sum of all locally known convex cost functions. Every single agent in the network only knows its local objective function and local inequality constraint, and is constrained to a privately known convex set. Furthermore, we particularly discuss the scenario in which the interactions among agents over the whole network are subjected to possible link failures. To collaboratively solve the optimization problem, we mainly concentrate on an epigraph form of the original constrained optimization to overcome the unbalancedness of directed networks, and propose a new distributed asynchronous broadcast-based optimization algorithm. The algorithm allows that not only the updates of agents are asynchronous in a distributed fashion, but also the step-sizes of all agents are uncoordinated. An important characteristic of the proposed algorithm is to cope with the constrained optimization problem in the case of unbalanced directed networks whose communications are subjected to possible link failures. Under two standard assumptions that the communication network is directly strongly connected and the subgradients of all local objective functions are bounded, we provide an explicit analysis for convergence of the algorithm. Simulation results obtained by three numerical experiments substantiate the feasibility of the algorithm and validate the theoretical findings.
The average consensus problem of distributed inference in a wireless sensor network under Markovian communication topology of uncertain transition probability is studied. A sufficient condition for average consensus o...
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The average consensus problem of distributed inference in a wireless sensor network under Markovian communication topology of uncertain transition probability is studied. A sufficient condition for average consensus of linear distributed inference algorithm is presented. Based on linear matrix inequalities and numerical optimization, a design method of fast distributed inference is provided.
This paper introduces two new distributed learning algorithms : Incremental Particle Swarm Optimization (IPSO) and Diffusion Particle Swarm Optimization (DPSO). These algorithms are applied for distributed identificat...
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This paper introduces two new distributed learning algorithms : Incremental Particle Swarm Optimization (IPSO) and Diffusion Particle Swarm Optimization (DPSO). These algorithms are applied for distributed identification of nonlinear processes using cooperation among adaptive nodes. Identification of four standard nonlinear plants have been carried out through simulation to assess the performance of these algorithms. The results indicate better or identical identification performance offered by the proposed distributed algorithms compared to that offered by the conventional PSO based algorithm. The improvement is observed in terms of CPU time, accuracy in response matching and speed of convergence.
We describe approximation algorithms in Linial's classic LOCAL model of distributed computing to find maximum-weight matchings in a hypergraph of rank r. Our main result is a deterministic algorithm to generate a ...
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We describe approximation algorithms in Linial's classic LOCAL model of distributed computing to find maximum-weight matchings in a hypergraph of rank r. Our main result is a deterministic algorithm to generate a matching which is an O(r)-approximation to the maximum weight matching, running in Õ(r log Δ + log 2 Δ + log* n) rounds. (Here, the Õ() notations hides polyloglog Δ and polylog r factors). This is based on a number of new derandomization techniques extending methods of Ghaffari, Harris & Kuhn (2017). The first main application is to nearly-optimal algorithms for the long-studied problem of maximum-weight graph matching. Specifically, we get a (1+ε) approximation algorithm using Õ(log Δ/ε 3 + polylog(1/ε, log log n)) randomized time and Õ(log 2 Δ/ε 4 + log*n/ε) deterministic time. The second application is a faster algorithm for hypergraph maximal matching, a versatile subroutine introduced in Ghaffari et al. (2017) for a variety of local graph algorithms. This gives an algorithm for (2Δ - 1) -edge-list coloring in Õ(log 2 Δ log n) rounds deterministically or Õ((log log n) 3 ) rounds randomly. Another consequence (with additional optimizations) is an algorithm which generates an edge-orientation with out-degree at most ⌈(1+ε)λ⌉ for a graph of arboricity λ; for fixed ε this runs in Õ(log 6 n) rounds deterministically or Õ(log 3 n ) rounds randomly.
The authors present an approach to distributed workload analysis which can be used as a basis for the performance study of distributed deadlock detection algorithms. In particular, the expected number of times a deadl...
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The authors present an approach to distributed workload analysis which can be used as a basis for the performance study of distributed deadlock detection algorithms. In particular, the expected number of times a deadlock detection algorithm is locally initiated and the subsequent number of remote invocations are derived. Simulation work was done to validate the approach.< >
This article considers the problem of solving linear algebraic equations of the form Ax=b among multiagents, which seek a solution by using local information in presence of random communication topologies. The equatio...
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This article considers the problem of solving linear algebraic equations of the form Ax=b among multiagents, which seek a solution by using local information in presence of random communication topologies. The equation is solved by m agents where each agent only knows a subset of rows of the partitioned matrix [A,b]. The problem is formulated such that this formulation does not need the distribution of random interconnection graphs. Therefore, this framework includes asynchronous updates and/or unreliable communication protocols. The random Krasnoselskii-Mann iterative algorithm is applied that converges almost surely and in mean square to a solution of the problem for any matrices A and b and any initial conditions of agents' states. The algorithm is a totally asynchronous algorithm without requiring a priori B-connectivity and distribution dependency assumptions. The algorithm is able to solve the problem even if the weighted matrix of the graph is periodic and irreducible for synchronous protocol. It is demonstrated that the limit point to which the agents' states converge is determined by the unique solution of a convex optimization problem regardless of the distribution of random communication graphs. Finally, some numerical examples are given to show the results.
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