We present fast distributed local control connected dominating set (CDS) algorithms for wireless ad hoc networks. We present two randomized distributed algorithms, CDSColor and CDSTop which take into account the effec...
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We present fast distributed local control connected dominating set (CDS) algorithms for wireless ad hoc networks. We present two randomized distributed algorithms, CDSColor and CDSTop which take into account the effect of wireless interference and the consequent loss of messages during the execution of the algorithm. These algorithms produce a CDS of constant size and constant stretch ratio with high probability, and converge in polylogarithmic running time. Specifically, algorithm CDSColor requires the nodes to know (estimates of) the maximum degree A and the size of the network n and converges in O (Delta log(2) n) time. Algorithm CDSTop requires the nodes to know their three-hop topology and (an estimate of) the network size n and converges in O (log(2) n) time. To the best of our knowledge, these are the first distributed interference-aware CDS algorithms for wireless ad hoc networks which break the linear running-time barrier. (C) 2007 Elsevier Inc. All rights reserved.
We develop distributed algorithms for efficient spectrum access strategies in cognitive radio relay networks. In our setup, primary users permit secondary users access to the resource (spectrum) as long as they consen...
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We develop distributed algorithms for efficient spectrum access strategies in cognitive radio relay networks. In our setup, primary users permit secondary users access to the resource (spectrum) as long as they consent to aiding the primary users as relays in addition to transmitting their own data. Given a pool of primary and secondary users, we desire to optimize overall network utility by determining the best configuration/pairing of secondary users with primary users. This optimization can be stated in a form similar to the maximum weighted matching problem. Given such formulation, we develop an algorithm based on affinity propagation technique that is completely distributed in its structure. We demonstrate the convergence of the developed algorithm and show that it performs close to the optimal centralized scheme.
distributed and parallel algorithms have been frequently investigated in the recent years, in particular in applications like machine learning. Nonetheless, only a small subclass of the optimization algorithms in the ...
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distributed and parallel algorithms have been frequently investigated in the recent years, in particular in applications like machine learning. Nonetheless, only a small subclass of the optimization algorithms in the literature can be easily distributed, for the presence, e.g., of coupling constraints that make all the variables dependent from each other with respect to the feasible set. Augmented Lagrangian methods are among the most used techniques to get rid of the coupling constraints issue, namely by moving such constraints to the objective function in a structured, well-studied manner. Unfortunately, standard augmented Lagrangian methods need the solution of a nested problem by needing to (at least inexactly) solve a subproblem at each iteration, therefore leading to potential inefficiency of the algorithm. To fill this gap, we propose an augmented Lagrangian method to solve convex problems with linear coupling constraints that can be distributed and requires a single gradient projection step at every iteration. We give a formal convergence proof to at least epsilon-approximate solutions of the problem and a detailed analysis of how the parameters of the algorithm influence the value of the approximating parameter epsilon. Furthermore, we introduce a distributed version of the algorithm allowing to partition the data and perform the distribution of the computation in a parallel fashion.
Novel network infrastructures require the distribution of computing and network resource control to meet stringent requirements in terms of latency, reliability and bitrate. 5G systems bring a key novelty in systems d...
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Novel network infrastructures require the distribution of computing and network resource control to meet stringent requirements in terms of latency, reliability and bitrate. 5G systems bring a key novelty in systems design that it the 'network slice'as a new resource provisioning entity. A network slice is meant to serve end-to-end services as a composition of different network and system resources as radio, link, storage and computing resources. Conventionally, each resource is managed by a distinct decision-maker, platform, provider, orchestrator or controller. Naturally, centralized slice orchestration approaches are proposed in the literature, where a multi-domain orchestrator allocates the resources, for instance using a multi-resource allocation rule. Nonetheless, while simplifying the algorithmic approach, centralization can come at the expense of scalability and performance. In this article, we propose new ways to distribute the slice multi-resource resource allocation problem, using cascade and parallel resource allocations that are functionally compatible with novel software platforms. We also show how to adapt the proposed algorithms to make them able to guarantee service level agreements on the minimum resource needed, and to take into account deadline priority policy scheduling. We provide an exhaustive analysis of the advantages and disadvantages of the different approaches, including a numerical analysis for a realistic setting.
This paper addresses the question of distributively computing over a strongly connected unidirectional data communication network. In unidirectional networks the existence of a communication link from one node to anot...
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This paper addresses the question of distributively computing over a strongly connected unidirectional data communication network. In unidirectional networks the existence of a communication link from one node to another does not imply the existence of a link in the opposite direction. The strong connectivity means that from every node there is a directed path to any other node. The authors assume an arbitrary topology network in which the strong connectivity is the only restriction. Four models are considered, synchronous and asynchronous, and for each node space availability, which grows as either O(1) bits or O(log n) bits per incident link, where n is the total number of nodes in the network, is considered. First algorithms for two basic problems in distributed computing in data communication networks, traversal, and election. are provided. Each of these basic protocols produces two directed spanning trees rooted at a distinguished node in the network, one called in-tree, leading to the root, and the other, out-tree. leading from the root. Given these trees, the authors efficiently transform bidirectional algorithms to run on unidirectional networks, and in particular solve other problems such as the broadcast and echo [E.J. CHANG, Decentralized algorithms in distributed Systems, Ph.D. thesis, University of Toronto. October 1979] in a may that is more efficient (O(n(2)) messages) than direct transformation (which yields O(nm) messages algorithm). The communication cost of the traversal and election algorithms is O(nm + n(2) log n) bits (O(nm) messages and time), where m is the total number of links in the network. The traversal algorithms for unidirectional networks of finite automata achieve the same cost (O(nm + n(2) log n) bits)in the asynchronous case, while in the synchronous case the communication cost of the algorithm is O(mn) bits.
We examine a distributed algorithm where the processors may fail in a random fashion. This results in a model with random communication delays. Convergence conditions are derived. Extensions of the analysis and result...
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We examine a distributed algorithm where the processors may fail in a random fashion. This results in a model with random communication delays. Convergence conditions are derived. Extensions of the analysis and results to cases where the random processor failures are perceived and corrected within random time intervals are possible. For the sake of simplicity, the analysis is presented for a two processor model for solving a system of linear equations.
We consider the problem of maintaining communication between the nodes of a data network and a central station in the presence of frequent topological changes as, for example, in mobile packet radio networks. We argue...
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We consider the problem of maintaining communication between the nodes of a data network and a central station in the presence of frequent topological changes as, for example, in mobile packet radio networks. We argue that flooding schemes have significant drawbacks for such networks, and propose a general class of distributed algorithms for establishing new loop-free routes to the station for any node left without a route due to changes in the network topology. By virtue of built-in redundancy, the algorithms are typically activated very infrequently and, even when they are, they do not involve any communication within the portion of the network that has not been materially affected by a topological change.
A distance-k dominating set D of a directed graph G is a set of vertices such that for every vertex v of G, there is a vertex u is an element of D and the distance between u and v is at most k. Minimum distance-k domi...
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A distance-k dominating set D of a directed graph G is a set of vertices such that for every vertex v of G, there is a vertex u is an element of D and the distance between u and v is at most k. Minimum distance-k dominating set is especially important in communication networks for distributed data structures and for server placement. In this paper, we show that there is a unique minimum distance-k dominating set for k = 1, 2 in a directed split-star, which has recently been developed as a new model of the interconnection network for parallel and distributed computing systems. Moreover, we shall present simple distributed algorithms for finding such sets. (C) 2003 Elsevier Science (USA). All rights reserved.
We study distributed algorithms for three graph-theoretic problems in weighted trees and weighted planar graphs. For trees, we present an efficient deterministic distributed algorithm which finds an almost exact appro...
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We study distributed algorithms for three graph-theoretic problems in weighted trees and weighted planar graphs. For trees, we present an efficient deterministic distributed algorithm which finds an almost exact approximation of a maximum-weight matching. In addition, in the case of trees, we show how to approximately solve the minimum-weight dominating set problem. For planar graphs, we present an almost exact approximation for the maximum-weight independent set problem. (C) 2005 Elsevier *** rights reserved.
Colonies of the arboreal turtle ant create networks of trails that link nests and food sources on the graph formed by branches and vines in the canopy of the tropical forest. Ants put down a volatile pheromone on the ...
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Colonies of the arboreal turtle ant create networks of trails that link nests and food sources on the graph formed by branches and vines in the canopy of the tropical forest. Ants put down a volatile pheromone on the edges as they traverse them. At each vertex, the next edge to traverse is chosen using a decision rule based on the current pheromone level. There is a bidirectional flow of ants around the network. In a previous field study, it was observed that the trail networks approximately minimize the number of vertices, thus solving a variant of the popular shortest path problem without any central control and with minimal computational resources. We propose a biologically plausible model, based on a variant of the reinforced random walk on a graph, which explains this observation and suggests surprising algorithms for the shortest path problem and its variants. Through simulations and analysis, we show that when the rate of flow of ants does not change, the dynamics converges to the path with the minimum number of vertices, as observed in the field. The dynamics converges to the shortest path when the rate of flow increases with time, so the colony can solve the shortest path problem merely by increasing the flow rate. We also show that to guarantee convergence to the shortest path, bidirectional flow and a decision rule dividing the flow in proportion to the pheromone level are necessary, but convergence to approximately short paths is possible with other decision rules.
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