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检索条件"主题词=distributed algorithms"

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distributed estimation of node centrality with application to agreement problems in social networks 57

Distributed estimation of node centrality with application t...

引用

57th IEEE Conference on Decision and Control (CDC)

作者： Montijano, Eduardo Oliva, Gabriele Gasparri, Andrea Univ Zaragoza Inst Invest Ingn Aragon I3A Zaragoza Spain Univ Campus Biomed Roma Dept Engn Unit Automat Control Rome Italy Univ Roma Tre Dept Comp Sci & Automat Rome Italy

ISBN: (纸本)9781538613955

Measures of node centrality that describe the importance of a node within a network are crucial for understanding the behavior of social networks and graphs. In this paper, we address the problem of distributed node centrality identification. In particular, we focus our attention on alphacentrality, which can be seen as a generalization of eigenvector centrality, particularly suitable for graphs with asymmetric interactions. In this setting, our contribution is twofold: first we derive a distributed protocol where agents can locally compute their alpha-centrality index by means of local interactions;then we propose a novel consensus-algorithm running in parallel to the alpha-centrality estimator, which converges towards a weighted average of the initial conditions, where the weights are dictated by the alpha-centrality vector. The proposed algorithm finds application in social networks, where agreement protocols typically place more value on experts and influencers than on the rest of users. Simulations results are provided to corroborate the theoretical findings.

关键词： Estimation Social network services Convergence Eigenvalues and eigenfunctions Protocols Indexes distributed algorithms

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A distributed Approach for Bitrate Selection in HTTP Adaptive Streaming 18

A Distributed Approach for Bitrate Selection in HTTP Adaptiv...

引用

26th ACM Multimedia Conference (MM)

作者： Bentaleb, Abdelhak Begen, Ali C. Harous, Saad Zimmermann, Roger Natl Univ Singapore Singapore Singapore Ozyegin Univ Istanbul Turkey United Arab Emirates Univ Abu Dhabi U Arab Emirates

ISBN: (纸本)9781450356657

Past research has shown that concurrent HTTP adaptive streaming (HAS) players behave selfishly and the resulting competition for shared resources leads to underutilization or oversubscription of the network, presentation quality instability and unfairness among the players, all of which adversely impact the viewer experience. While coordination among the players, as opposed to all being selfish, has its merits and may alleviate some of these issues. A fully distributed architecture is still desirable in many deployments and better reflects the design spirit of HAS. In this study, we focus on and propose a distributed bitrate adaptation scheme for HAS that borrows ideas from consensus and game theory frameworks. Experimental results show that the proposed distributed approach provides significant improvements in terms of viewer experience, presentation quality stability, fairness and network utilization, without using any explicit communication between the players.

关键词： has resource allocation qoe bitrate selection game theory abr distributed algorithms consensus

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distributed Evolutionary Games Reaching Power Indexes: Navigability in a Social Network of Smart Objects 16

Distributed Evolutionary Games Reaching Power Indexes: Navig...

引用

European Control Conference (ECC)

作者： Barreiro-Gomez, Julian Tembine, Hamidou NYUAD Learning & Game Theory Lab Saadiyat CampusPOB 129188 Abu Dhabi U Arab Emirates

ISBN: (纸本)9783952426982

In the context of coalitional games, a power index allows to determine the magnitude of contributions associated to each player, i.e., a power index provides information about how influential or relevant a player is in a cooperative interaction. Nevertheless, if the number of involved players is big, then the computation of a power index might become intractable. In this paper, we show how to construct a full-potential game whose Nash equilibrium coincides with the Shapley or Banzhaf power index for a family of characteristic functions. Therefore, distributed non-cooperative algorithms can be used for cooperative-game purposes. As a consequence, both the computational time and the information requirements are reduced, allowing the use of power indexes in large-scale systems. As an illustrative example, we present a large-scale social network of smart objects where it is desired to enhance the navigability by means of local decisions.

关键词： Power indexes Nash equilibrium distributed algorithms large-scale systems

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distributed Online Resource Allocation using Congestion Game for 5G Virtual Network Services

Distributed Online Resource Allocation using Congestion Game...

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IEEE Global Communications Conference

作者： Yu Bi Monchai Bunyakitanon Navdeep Uniyal Anderson Bravalheri Abubakar Muqaddas Reza Nejabati Dimitra Simeonidou Faculty of Engineering High Performance Networks Group University of Bristol United Kingdom

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

To meet the challenge of flexible and dynamic resource provisioning for massive and various network services, we investigate the network function virtualization-resource allocation problem in the 5G network. For the first time, this problem is modelled as the congestion game to capture the effects of resource congestion on packet processing latency, optical-to-electronic and electronic-to-optical conversion latency. All the network service requests received at the same time are players trying to minimise their own end-to-end latency and resource consumption cost. A distributed online algorithm is designed and simulation results show that it can achieve 100% service acceptance ratio while the baseline algorithm cannot. If lower weighted resource consumption cost is set for 1ms and 5ms services, more such services will be routed to edge nodes and network operators will earn more. An experiment for network services with different packet sizes is carried out, and results prove that the proposed algorithm converges to Nash Equilibrium in 40 seconds and the latency requirements are all satisfied if the packet size is small.

关键词： Games Optical fiber networks 5G mobile communication Resource management High-speed optical techniques Heuristic algorithms distributed algorithms

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Asymptotically Optimal distributed Gateway Load-Balancing for the Internet of Things

Asymptotically Optimal Distributed Gateway Load-Balancing fo...

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International Conference on the Network of the Future (NOF)

作者： Ilai Bistritz Nicholas Bambos Department of Electrical Engineering Stanford University Stanford CA

ISBN: (数字)9781728144450

ISBN: (纸本)9781728144467

Consider a network of N sensors that collect data. Each sensor transmits this data to one out of G gateways. The gateways perform the preprocessing of the data and store it in the cloud. In order not to lose data, sensors need to route their packets to the gateway that has higher chances of successfully receiving them. However, this might overload some gateways that are the best gateway for many sensors, forcing them to drop packets. We design an asynchronous distributed algorithm where each sensor randomizes its destination, taking into account both the transmission success probabilities and the average input data rate of each gateway. Sensors estimate the transmission success probabilities online by sending pilot sequences and gateways broadcast the input data rates to all sensors. We show that our algorithm converges to a close to optimal solution. Specifically, when the number of sensors N approaches infinity, the ratio of the total throughput of our algorithm to the optimal throughput converges in probability to one.

关键词： Sensors Logic gates Throughput Load management distributed algorithms Bandwidth Internet of Things

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A BP-like distributed Algorithm for Weighted Average Consensus

A BP-like Distributed Algorithm for Weighted Average Consens...

引用

Asian Control Conference

作者： Zhaorong Zhang Kan Xie Qianqian Cai Minyue Fu School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW Australia School of Automation Guangdong University of Technology and Guangdong Key Laboratory of IoT Information Technology Guangzhou China

ISBN: (纸本)9781728102634

A novel distributed algorithm designed for weighted average consensus is presented. This algorithm is developed based on the well-known Belief Propagation (BP) algorithm for statistical learning. For a connected network without loops, the algorithm will converge in finite number of iteration and produces a correct average consensus value for every node in the network. For a network with loops, we convert the weighted average problem into an optimization problem with a relaxation factor whose solution approaches to the weighted average with appropriate relaxation factor. A modified algorithm is also proposed for loopy networks and consensus will be reached asymptotically with low computation complexity and fast convergence rate.

关键词： Signal processing algorithms Convergence distributed algorithms Nickel Statistical learning Consensus algorithm Belief propagation

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Achieving Acceleration in distributed Optimization via Direct Discretization of the Heavy-Ball ODE

Achieving Acceleration in Distributed Optimization via Direc...

引用

American Control Conference

作者： Jingzhao Zhang Cesar A. Uribe Aryan Mokhtari Ali Jadbabaie Systems and Society (IDSS) Laboratory for Information and Decision Systems (LIDS) The Institute for Data Massachusetts Institute of Technology 77 Massachusetts Ave Cambridge MA 02139

ISBN: (纸本)9781538679012;9781538679265

We develop a distributed algorithm for convex Empirical Risk Minimization, the problem of minimizing large but finite sum of convex functions over networks. The proposed algorithm is derived from directly discretizing the second-order heavy-ball differential equation and results in an accelerated convergence rate, i.e., faster than distributed gradient descent-based methods for strongly convex objectives that may not be smooth. Notably, we achieve acceleration without resorting to the well-known Nesterov's momentum approach. We provide numerical experiments and contrast the proposed method with recently proposed optimal distributed optimization algorithms.

关键词： acceleration Convexity distributed algorithms Optimization algorithms Distribute Discretization accelerating convergence Resorts Convex function

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distributed Algorithm for Solving the Bottleneck Assignment Problem

Distributed Algorithm for Solving the Bottleneck Assignment ...

引用

IEEE Annual Conference on Decision and Control

作者： Mitchell Khoo Tony A. Wood Chris Manzie Iman Shames Department of Electrical and Electronic Engineering University of Melbourne

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Assignment problems are found in multiagent systems, where there is a need to allocate multiple tasks to agents. The bottleneck assignment problem (BAP) is an assignment problem where the objective is to minimise the worst individual cost in the assignment. distributed algorithms for assignments with other objectives have been proposed, yet to date no distributed algorithm for the BAP exists. This paper addresses this gap;we develop a novel distributed algorithm that solves the BAP optimally. The algorithm does not require a centralised decision-maker having access to all information from each agent, which is an advantage over existing algorithms for solving the BAP. We use numerical simulations to compare the optimality of the proposed algorithm against a greedy assignment-finding algorithm.

关键词： Task analysis distributed algorithms Resource management Greedy algorithms Bipartite graph Clocks Conferences

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Hierarchical distributed Voltage Regulation in Networked Autonomous Grids

Hierarchical Distributed Voltage Regulation in Networked Aut...

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American Control Conference

作者： Xinyang Zhou Zhiyuan Liu Wenbo Wang Changhong Zhao Fei Ding Lijun Chen National Renewable Energy Laboratory Golden CO 80401 USA College of Engineering and Applied Science University of Colorado Boulder CO 80309 USA New York University Brooklyn NY 11201 USA National Renewable Energy Laboratory Golden CO 80401

ISBN: (纸本)9781538679012;9781538679265

We propose a novel algorithm to solve optimal power flow (OPF) that aims at dispatching controllable distributed energy resources (DERs) for voltage regulation at minimum cost. The proposed algorithm features unprecedented scalability to large distribution networks by utilizing an information structure based on networked autonomous grids (AGs). Specifically, each AG is a subtree of a large distribution network that has a tree topology. The topology and line parameters of each AG are known only to a regional coordinator (RC) that is responsible for communicating with and dispatching the DERs within this AG. The reduced network, where each AG is treated as a node, is managed by a central coordinator (CC), which knows the topology and line parameters of the reduced network only and communicates with all the RCs. We jointly explore this information structure and the structure of the linearized distribution power flow (LinDistFlow) model to derive a hierarchical, distributed implementation of the primal-dual gradient algorithm that solves the OPF. The proposed implementation significantly reduces the computation burden compared to the centrally coordinated implementation of the primal-dual algorithm. Numerical results on a 4,521-node test feeder show that the proposed hierarchical distributed algorithm can achieve an improvement of more than tenfold in the speed of convergence compared to the centrally coordinated primal-dual algorithm.

关键词： distributed algorithms distributed control distributed power generation gradient methods load flow optimisation power distribution control trees (mathematics) voltage control

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ASY-SONATA: Achieving Linear Convergence in distributed Asynchronous Multiagent Optimization 56

ASY-SONATA: Achieving Linear Convergence in Distributed Asyn...

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56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

作者： Tian, Ye Sun, Ying Scutari, Gesualdo Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781538665961

This papers studies multi-agent (convex and nonconvex) optimization over static digraphs. We propose a general distributed asynchronous algorithmic framework whereby i) agents can update their local variables as well as communicate with their neighbors at any time, without any form of coordination;and ii) they can perform their local computations using (possibly) delayed, out-of-sync information from their neighbors. Delays need not be known to the agents or obey any specific profile, and can also be time-varying (but bounded). The algorithm builds on a tracking mechanism that is robust against asynchrony (in the above sense), whose goal is to estimate locally the sum of agents' gradients. When applied to strongly convex functions, we prove that it converges at an R-linear (geometric) rate as long as the step-size is sufficiently small. A sublinear convergence rate is proved, when nonconvex problems and/or diminishing, uncoordinated step-sizes are employed. To the best of our knowledge, this is the first distributed algorithm with provable geometric convergence rate in such a general asynchonous setting.

关键词： Delays Convergence Convex functions distributed algorithms Cost function Approximation algorithms

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