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检索条件"主题词=distributed algorithms"

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distributed Reverse DNS Geolocation

Distributed Reverse DNS Geolocation

引用

IEEE International Conference on Big Data (Big Data)

作者： Dan, Ovidiu Parikh, Vaibhav Davison, Brian D. Lehigh Univ Bethlehem PA 18015 USA Microsoft Bing Redmond WA 98052 USA

ISBN: (纸本)9781538650356

IP geolocation databases map IP addresses to their geographical locations. These databases are used in a variety of online services to serve local content to users. Here we present methods for extracting locations from the reverse DNS hostnames assigned to IP addresses. We summarize a machine learning based approach which, given a hostname, aims to extract and rank potential location candidates, which can then potentially be fused with other geolocation signals. We show that this approach significantly outperforms a state-of-the-art academic baseline, and it is competitive and complementary to commercial baselines. Since extracting locations from more than a billion reverse DNS hostnames at once poses a significant computational challenge, we develop a distributed version of our algorithm. We perform experiments on a cluster of 2,000 machines to demonstrate that our distributed implementation can scale. We show that compared to the single machine version, our distributed approach can achieve a speedup of more than 150X.

关键词： IP geolocation distributed algorithms MapReduce geographic targeting geotargeting geographic personalization reverse DNS hostname geolocation

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distributed Continuous-time Resource Allocation with Time-varying Resources under Quadratic Cost Functions 57

Distributed Continuous-time Resource Allocation with Time-va...

引用

57th IEEE Conference on Decision and Control (CDC)

作者： Bai, Lu Sun, Chao Feng, Zhi Hu, Guoqiang Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9781538613955

We developed distributed continuous-time algorithms to solve the resource allocation problem with quadratic cost functions and continuously time-varying resources. Since the resources are time-varying, the optimal solution is changing over time. The allocation decision variable should not only find but also track the optimal solution trajectory. In a distributed manner, the agents work collaboratively to find as well as track the optimal solution using local information. Without the local allocation feasibility constraints, a distributed algorithm is designed based on sign function and consensus protocols. The tracking error is proven to vanish in finite time. When the local allocation feasibility constraints are considered, a distributed algorithm based on singular perturbation theory and penalty function is developed. The tracking error is proven to be uniformly ultimately bounded.

关键词： Resource management Cost function distributed algorithms Economics Heuristic algorithms Linear programming

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Communication-free Massively distributed Graph Generation 32

Communication-free Massively Distributed Graph Generation

引用

27th International Heterogeneity in Computing Workshop in conjunction with 32nd IEEE International Parallel and distributed Processing Symposium (IPDPS)

作者： Funke, Daniel Lamm, Sebastian Sanders, Peter Schulz, Christian Strash, Darren von Looz, Moritz Karlsruhe Inst Technol Karlsruhe Germany Univ Vienna Vienna Austria Colgate Univ New York NY USA

ISBN: (纸本)9781538643686

Analyzing massive complex networks yields promising insights about our everyday lives. Building scalable algorithms to do so is a challenging task that requires a careful analysis and an extensive evaluation. However, engineering such algorithms is often hindered by the scarcity of publicly available datasets. Network generators serve as a tool to alleviate this problem by providing synthetic instances with controllable parameters. However, many network generators fail to provide instances on a massive scale due to their sequential nature or resource constraints. Additionally, truly scalable network generators are few and often limited in their realism. In this work, we present novel generators for a variety of network models commonly found in practice. By making use of pseudorandomization and divide-and-conquer schemes, our generators follow a communication-free paradigm. The resulting generators are often embarrassingly parallel and have a near optimal scaling behavior. This allows us to generate instances of up to 2 43 vertices and 2 47 edges in less than 22 minutes on 32768 cores. Therefore, our generators allow new graph families to be used on an unprecedented

关键词： graph generation communication-free distributed algorithms

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distributed Constraint-Coupled Optimization over Random Time-Varying Graphs via Primal Decomposition and Block Subgradient Approaches

Distributed Constraint-Coupled Optimization over Random Time...

引用

IEEE Annual Conference on Decision and Control

作者： Andrea Camisa Francesco Farina Ivano Notarnicola Giuseppe Notarstefano Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

In this paper, we consider a network of processors that want to cooperatively solve a large-scale, convex optimization problem. Each processor has knowledge of a local cost function that depends only on a local variable. The goal is to minimize the sum of the local costs, while making the variables satisfy both local constraints and a global coupling constraint. We propose a simple, fully distributed algorithm, that works in a random, time-varying communication model, where at each iteration multiple edges are randomly drawn from an underlying graph. The algorithm is interpreted as a primal decomposition scheme applied to an equivalent problem reformulation. Almost sure convergence to the optimal cost of the original problem is proven by resorting to approaches from block subgradient methods. Specifically, the communication structure is mapped to a block structure, where the blocks correspond to the graph edges and are randomly selected at each iteration. Moreover, an almost sure asymptotic primal recovery property, with no averaging mechanisms, is shown. A numerical example corroborates the theoretical analysis.

关键词： distributed algorithms Couplings Cost function Resource management Convergence Program processors

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distributed Continuous-Time Constrained Convex Optimization via Nonsmooth Analysis

Distributed Continuous-Time Constrained Convex Optimization ...

引用

IEEE International Conference on Real-time Computing and Robotics (IEEE RCAR)

作者： Li, Xiuxian Xie, Lihua Hong, Yiguang Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore Chinese Acad Sci Inst Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China

ISBN: (纸本)9781538668696

This paper investigates distributed continuous-time convex optimization problems for a network of agents subject to several constraints, i.e.. local feasible constraints, local equality and inequality constraints, in which all convex functions may he not differentiable. For this problem, a distributed algorithm is proposed by the projection method, and it can be shown that Caratheodory solutions always exist for the proposed algorithm and all agents can ultimately reach an optimal solution for the optimization problem. Additionally, one more fully distributed sufficient condition is presented. To validate the theoretical result, a numerical example is also provided.

关键词： Optimization Convex functions Linear programming distributed algorithms Standards Convergence Eigenvalues and eigenfunctions

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Trekking Based distributed Algorithm for Opportunistic Spectrum Access in Infrastructure-less Network 16

Trekking Based Distributed Algorithm for Opportunistic Spect...

引用

16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Kumar, Rohit Yadav, A. Darak, S. J. Hanawal, Manjesh K. NIT Delhi Dept Elect & Commun Engn Delhi 110040 India Texas A&M Univ Elect & Comp Engn College Stn TX 77843 USA IIIT Delhi Dept Elect & Commun Engn Delhi 110020 India Indian Inst Technol Ind Engn & Operat Res IEOR Grp Bombay 460076 Maharashtra India

ISBN: (纸本)9783903176003

An opportunistic spectrum access (OSA) in the infrastructure-less network has received significant attention in last few years due to their ability to improve spectrum utilization as well as usefulness in the infrastructure-less networks established for disaster relief and military applications. The main research problem for feasible implementation of such network is to achieve coordination among secondary users (SUs) (i.e. unlicensed users). Existing algorithms incur a significant number of collisions which in turn require retransmissions and hence, lead to inefficient use of battery power, spectrum and time. In this paper, we set-up the problem as a multi-player Bandit and develop a new distributed algorithm which allows SUs to select one of the top channels with a significantly fewer number of collisions. We show that the proposed algorithm has constant regret with high confidence. We validate our claims and the superiority of the proposed algorithm over existing state-of-theart algorithms through the exhaustive simulated experiments as well as a realistic USRP based experiments in the real radio environment.

关键词： Channel estimation Throughput Batteries distributed algorithms Heuristic algorithms Electronic mail Channel allocation

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Communication-efficient distributed Solutions to a System of Linear Equations with Laplacian Sparse Structure 57

Communication-efficient Distributed Solutions to a System of...

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57th IEEE Conference on Decision and Control (CDC)

作者： Wang, Peng Gao, Yuanqi Yu, Nanpeng Ren, Wei Lian, Jianming Wu, Di Pacific Northwest Natl Lab 902 Battelle Blvd Richland WA 99352 USA Univ Calif Riverside Dept Elect & Comp Engn 900 Univ Ave Riverside CA 92521 USA

ISBN: (纸本)9781538613955

Two communication-efficient distributed algorithms are proposed to solve a system of linear equations Ax = b with Laplacian sparse A. A system of linear equations with Laplacian sparse A can be found in many applications, e.g., the power flow problems and other network flow problems. The first algorithm is based on the gradient descent method in optimization and the agents only share two parts of the system state instead of that of the whole system state, which saves significant communication. The two parts shared by every agent through a communication link are the state information of its own and its neighbor connected by the communication link. The second method is obtained from an approximation to the Newton method, which converges faster. It requires twice as much communication as the first one but is still communication-efficient due to the low dimension of each part shared between agents. The convergence at a linear rate of both methods is proved.

关键词： Laplace equations Sparse matrices distributed algorithms Newton method Convergence Jacobian matrices Mathematical model

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A distributed Algorithm with Event-Triggered Communication for Resource Allocation Problem

A Distributed Algorithm with Event-Triggered Communication f...

引用

American Control Conference

作者： Xiasheng Shi Ronghao Zheng Tao Yang Zhiyun Lin Gangfeng Yan College of Electrical Engineering Zhejiang University Hangzhou China University of North Texas Denton USA School of Automation Hangzhou Dianzi University Hangzhou China Colleg

ISBN: (纸本)9781538679012;9781538679265

This paper focuses on the distributed resource allocation problem in continuous-time multi-agent systems over undirected networks, in which each agent has a local convex cost function only known by itself and all agents are required to converge to the global optimizer of the sum of the local cost functions under constraints. A gradient-based distributed algorithm is proposed for solving the problem. Furthermore, the step-size of gradient is a constant. A sufficient condition is given which guarantees that the proposed algorithm can achieve the global optimization. Moreover, in order to avoid continuous communication, an event-triggered communication control strategy is designed. Finally, an example of the economic dispatch problem in smart grid is presented to validate and illustrate the feasibility of the proposed algorithm.

关键词： continuous time systems convex programming distributed algorithms gradient methods multi-agent systems power engineering computing power generation dispatch resource allocation smart power grids

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distributed Pilot Design for Massive Connectivity in Cellular Networks

Distributed Pilot Design for Massive Connectivity in Cellula...

引用

IEEE Global Communications Conference

作者： Xihan Chen An Liu Wei Yu Hei Victor Cheng Kaiming Shen Min-Jian Zhao College of ISEE Zhejiang University Hangzhou China Department of Electrical and Computer Engineering University of Toronto Canada

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Massive connectivity is regarded as a key requirement for future networks to support new communication paradigms, where the human-type communications coexist with machine-type communications. Owing to the limited coherence time but the huge number of potential devices, it is impossible to allocate mutually orthogonal pilot sequence for all potential devices, which may impose severe interference on the device activity detection and channel estimation. Existing nonorthogonal pilot design methods for conventional cellular network are not suitable for the massive connectivity regime. To overcome this challenge, we first formulate the pilot sequences design as an optimization problem to minimize the average mean square error (MSE) of channel estimation under the individual power constraint. The proposed optimization problem is nonconvex and highly coupled. By exploiting some approximation techniques, we convert the problem into a more tractable form and subsequently develop a distributed algorithm based on the matrix fractional programming (FP) and the alternating direction method of multipliers (ADMM) methods. Simulations validates that the proposed scheme not only achieves significant gains in channel estimation over state-of-the-art baseline schemes, but also improves the device activity detection performance.

关键词： Channel estimation Optimization Performance evaluation Partial transmit sequences Coherence Covariance matrices distributed algorithms

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distributed algorithm for solving linear algebraic equations: An implicit gradient neural network approach

Distributed algorithm for solving linear algebraic equations...

引用

Control Technology and Applications (CCTA),

作者： Peijun Wang Wenwu Yu Guanghui Wen School of Mathematics Southeast University Nanjing China

This brief investigates the online solving problem for linear algebraic equation Ax = b by means of the principle of consensus in multi-agent systems, where A ∈ ℝ m×n and b ∈ Rn. To be specific, we choose m autonomous agents and agent i knows only the i-th row of [A b] under a fixed and connected undirected communication topology. Under local interactions, by designing an implicit gradient neural network based algorithm, it is shown that all the agents' states which starting from any different initial conditions can converge exponentially fast to one of the solutions to Ax = b, if the matrix A has full row rank. It is worth noting that the proposed algorithm is fully distributed. In addition, it is shown that the proposed algorithm is effective in obtaining least square solutions for no-solution cases. Finally, computer simulations verify and demonstrate the efficiency of the proposed methods for solving linear algebraic equations.

关键词： Mathematical model Neural networks Signal processing algorithms Convergence distributed algorithms Heuristic algorithms Iterative methods

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