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A Augmented Lagrangian Approach for distributed Robust Estimation in Large-Scale Systems

IEEE SYSTEMS JOURNAL 2019年第3期13卷 2986-2997页

作者： Chan, Shing Chow Wu, Ho Chun Ho, Cheuk Hei Zhang, Li Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Shenzhen Univ Guangdong Prov Key Lab Biomed Instruments & Ultra Sch Biomed Engn Hlth Sci Ctr Shenzhen 518060 Peoples R China

Nonlinear estimation using maximum-likelihood estimation and maximum a posterior probability approaches is frequently employed for large-scale cyber-physical and complex systems in the big data era. Efficient distributed processing and robust estimation algorithms resilient to outliers are of great importance. This paper proposes a novel method for distributed solution of these robust estimation problems with equality constraints based on the augmented Lagrangian method (ALM). Specifically, a novel covariance normalization method and an automatic method for selecting regularization parameter with improved performance are proposed. Under the ALM framework, nonlinear equality constraints and nonsmooth L-1 regularization can be incorporated. The proposed method is illustrated with two emerging applications respectively in robust distributed power system state estimation (DSSE) with nonlinear zero injection constraints and gene regulatory network (GRN) identification. Experimental results in DSSE show that the covariance normalization method improves considerably the convergence speed over the alternating direction method of multipliers algorithm and the robust statistics employed effectively mitigates the adverse effects of extreme outliers. Zero-injection constraints can he effectively incorporated. For GRN identification, putative genes and connectivity for a yeast dataset with 1.57 million variables can be identified via sparsity and piecewise temporal continuity penalties and they generally align well with literature.

关键词： Complex systems cyber-physical systems distributed algorithms distributed power system state estimation gene regulatory networks (GRN) large-scale robust estimation time-course microarray data analysis

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KEIP: a distributed k-connectivity estimation algorithm based on independent paths for wireless sensor networks

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WIRELESS NETWORKS 2019年第8期25卷 4479-4491页

作者： Dagdeviren, Orhan Akram, Vahid Khalilpour Ege Univ Int Comp Inst Izmir Turkey

Maintaining the connectivity between all nodes is a challenging task in wireless sensor networks (WSNs) because failure of some nodes may divide the network to disconnected parts. A network is k-connected if we need to remove at least k nodes to disconnect it. There are at least k disjoint paths between each pair of nodes in a k-connected network which preserves the connectivity of the network after removing k-1 arbitrary nodes. Therefore, with high k values the network may tolerate more failures without losing its connectivity. Finding the k value of a given network can provide useful information about the robustness of the connectivity. The existing distributed k-connectivity estimation (detection) algorithms use local neighborhood information to find an approximated value for k. In this paper, we propose a new energy efficient distributed algorithm which finds the k value of the given WSN with more accuracy by detecting the minimum number of disjoint paths between the sink and all other nodes. We extend the definition of disjoint paths to independent paths, which are the disjoint paths with shared nodes, and use this concept to find the k value. The comprehensive simulation and testbed results show that the proposed algorithm is faster and has at least 20% higher correct detection ratio, lower mean square error ratio and also lower energy consumption than the existing algorithms.

关键词： k-connectivity Wireless sensor networks Fault tolerance Reliability distributed algorithms

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The Impact of Information in distributed Submodular Maximization

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2019年第4期6卷 1334-1343页

作者： Grimsman, David Ali, Mohd Shabbir Hespanha, Joao P. Marden, Jason R. Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA Orange Labs F-75001 Paris France

The maximization of submodular functions is an NP-Hard problem for certain subclasses of functions, for which a simple greedy algorithm has been shown to guarantee a solution whose quality is within 1/2 of the optimal. When this algorithm is implemented in a distributed way, agents sequentially make decisions based on the decisions of all previous agents. This work explores how limited access to the decisions of previous agents affects the quality of the solution of the greedy algorithm. Specifically, we provide tight upper and lower bounds on how well the algorithm performs, as a function of the information available to each agent. Intuitively, the results show that performance roughly degrades proportionally to the size of the largest group of agents that make decisions independently. Additionally, we consider the case where a system designer is given a set of agents and a global limit on the amount of information that can be accessed. Our results show that the best designs partition the agents into equally sized sets and allow agents to access the decisions of all previous agents within the same set.

关键词： distributed algorithms graph theory greedy algorithms multiagent systems optimization

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distributed Balancing With Constrained Integer Weights

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第6期64卷 2553-2558页

作者： Rikos, Apostolos I. Hadjicostis, Christoforos N. Univ Cyprus Dept Elect & Comp Engn CY-2112 Nicosia Cyprus

We consider the distributed integer-weight-balancing problem in networks of nodes that are interconnected via directed edges, each able to admit a positive integer weight (or flow) within a certain interval, captured by lower and upper limits. We propose and analyze distributed iterative algorithms for obtaining admissible and balanced integer weights, i.e., integer weights within the given interval constraints, such that, for each node, the sum of the weights of the incoming edges equals the sum of the weights of the outgoing edges. The proposed algorithms assume that communication among pairs of nodes that are interconnected is bidirectional and are shown to lead to a set of admissible and balanced integer weights after a finite number of time steps (as long as the necessary and sufficient integer circulation conditions are satisfied on the given digraph).

关键词： Digraphs distributed algorithms finite-time convergence flow balancing weight/flow balancing weight/flow constraints

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Efficient and distributed Generalized Canonical Correlation Analysis for Big Multiview Data

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2019年第12期31卷 2304-2318页

作者： Fu, Xiao Huang, Kejun Papalexakis, Evangelos E. Song, Hyun Ah Talukdar, Partha Sidiropoulos, Nicholas D. Faloutsos, Christos Mitchell, Tom Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32605 USA Univ Calif Riverside Comp Sci & Engn Dept 900 Univ Ave Riverside CA 92521 USA Carnegie Mellon Univ Machine Learning Dept 5000 Forbes Ave Pittsburgh PA 15213 USA Indian Inst Sci Dept Computat & Data Sci Bangalore 560012 Karnataka India Indian Inst Sci Dept Comp Sci & Automat Bangalore 560012 Karnataka India Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22904 USA

Generalized canonical correlation analysis (GCCA) integrates information from data samples that are acquired at multiple feature spaces (or 'views') to produce low-dimensional representations-which is an extension of classical two-view CCA. Since the 1960s, (G)CCA has attracted much attention in statistics, machine learning, and data mining because of its importance in data analytics. Despite these efforts, the existing GCCA algorithms have serious complexity issues. The memory and computational complexities of the existing algorithms usually grow as a quadratic and cubic function of the problem dimension (the number of samples / features), respectively-e.g., handling views with approximate to 1,000 features using such algorithms already occupies approximate to 10(6) memory and the per-iteration complexity is approximate to 10(9) flops-which makes it hard to push these methods much further. To circumvent such difficulties, we first propose a GCCA algorithm whose memory and computational costs scale linearly in the problem dimension and the number of nonzero data elements, respectively. Consequently, the proposed algorithm can easily handle very large sparse views whose sample and feature dimensions both exceed approximate to 100,000. Our second contribution lies in proposing two distributed algorithms for GCCA, which compute the canonical components of different views in parallel and thus can further reduce the runtime significantly if multiple computing agents are available. We provide detailed convergence analyses of the proposed algorithms and show that all the large-scale GCCA algorithms converge to a Karush-Kuhn-Tucker (KKT) point at least sublinearly. Judiciously designed synthetic and real-data experiments are employed to showcase the effectiveness of the proposed algorithms.

关键词： distributed algorithms Sparse matrices Correlation Machine learning algorithms Electronic mail Data mining Machine learning Generalized canonical correlation analysis multiview learning multilingual word embedding distributed GCCA

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A distributed and asynchronous approach for optimizing weighted graph matchings in wireless network services

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 119卷 73-89页

作者： Ileri, Can Umut Dagdeviren, Orhan Ege Univ Int Comp Inst Izmir Turkey

Weighted graph matching is one of the most fundamental graph theoretical problems used in network design, especially in wireless network services such as radio resource allocation, physical layer security, energy-efficient partner selection and optimizing storage capacity. In this paper we present a distributed heuristic which provides nearly-optimal weighted matchings in polynomial time. We also propose an algorithm to decrease the number of transmitted messages to provide energy-efficient operation. Our approaches assume the asynchronous communication model and small messages having O(log(n)) bits. These assumptions directly fit the battery powered wireless networks such as wireless ad hoc and sensor networks. To the best of our knowledge, we propose the first distributed weighted matching algorithm which benefits from augmentation of augmenting paths having size larger than 3 for these networks. We also provide results of our simulations on synthetic geometric graphs to model wireless networks. Extensive simulations reveal that our algorithm improves the approximation performances of the other weighted matching algorithms and closes the gap between the approximation ratio and the optimum up to 32%. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Graph matching Wireless networks distributed algorithms Graph theory

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A distributed Cluster-Based Approach for Pick-Up Services

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2019年第2期16卷 960-971页

作者： Abbatecola, Lorenzo Fanti, Maria Pia Pedroncelli, Giovanni Ukovich, Walter Univ Trieste Dept Engn & Architecture I-34127 Trieste Italy Polytech Univ Bari Dept Elect & Informat Engn I-70126 Bari Italy

This paper deals with the routing problem of pick-up and delivery services considering time windows and capacitated vehicles. In order to consider large real problems, this paper proposes a distributed vehicle routing problem (VRP) with time windows, based on cluster first, route second methods. First, by using a graph partitioning solved by local integer linear programing problems, the vehicles autonomously generate a number of clusters equal to the number of available vehicles. Second, each vehicle solves a traveling salesman problem with time windows to compute its route in the assigned cluster. The method can be applied by the vehicles for both planning the workday of the pick-up services and adapting the routing plan to manage the ongoing requests. Some benchmark problems are generated and solved by the distributed algorithm and compared with the solution obtained by the exact approach. Moreover, a real case study involving a courier service shows the efficiency of the solution method. Note to Practitioners-The motivation of this paper is to propose a distributed heuristic method for solving real pick-up service problems in static and dynamic settings. The proposed approach provides solutions with twofold key features. First, the distributed and iterative algorithm of the proposed method enables vehicles to compute routes that are characterized by balanced workloads for the drivers. Second, the method is applicable to plan the daily workload of vehicles and to dynamically adapt the routing plan for servicing the ongoing requests. Future research aims at extending the method to other variants of VRPs and at including the solver into a decision support system, with a suitable use of the modern information and communication technologies.

关键词： distributed algorithms logistics vehicle routing problem (VRP)

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Resilient distributed optimal generation dispatch for lossy AC microgrids

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SYSTEMS & CONTROL LETTERS 2019年 123卷 47-54页

作者： Zholbaryssov, Madi Fooladivanda, Dariush Dominguez-Garcia, Alejandro D. Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

In this paper, we consider the problem of designing a distributed algorithm, resilient against communication delays and packet drops, for coordinating the response of distributed energy resources (DERs) in AC microgrids so as to minimize electrical line losses and generation cost, and to ensure that the microgrid network constraints are satisfied. The proposed algorithm can be utilized as a secondary or tertiary controller for frequency regulation in islanded microgrids, or to coordinate DERs for providing frequency regulation services to the bulk grid when microgrid is in grid-connected mode. We validate the practical usefulness of the theoretical results through numerical simulations involving the IEEE 39-bus system. (C) 2018 Elsevier B.V. All rights reserved.

关键词： distributed algorithms Economic dispatch AC microgrids distributed energy resources

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JSDoop and ***: Volunteer distributed Web Browser-Based Neural Network Training

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IEEE ACCESS 2019年 7卷 158671-158684页

作者： Morell, Jose A. Camero, Andres Alba, Enrique Univ Malaga Dept Lenguajes & Ciencias Computat E-29071 Malaga Spain

In 2019, around 57% of the population of the world has broadband access to the Internet. Moreover, there are 5.9 billion mobile broadband subscriptions, i.e., 1.3 subscriptions per user. So there is an enormous interconnected computational power held by users all around the world. Also, it is estimated that Internet users spend more than six and a half hours online every day. But in spite of being a great amount of time, those resources are idle most of the day. Therefore, taking advantage of them presents an interesting opportunity. In this study, we introduce JSDoop, a prototype implementation to profit from this opportunity. In particular, we propose a volunteer web browser-based high-performance computing library. JSdoop divides a problem into tasks and uses different queues to distribute the computation. Then, volunteers access the web page of the problem and start processing the tasks in their web browsers. We conducted a proof-of-concept using our proposal and *** to train a recurrent neural network that predicts text. We tested it in a computer cluster and with up to 32 volunteers. The experimental results show that training a neural network in distributed web browsers is feasible and accurate, has a high scalability, and it is an interesting area for research. © 2019 IEEE.

关键词： Artificial intelligence browsers collaborative work distributed algorithms distributed computing internet javascript jsdoop neural networks tensorflow volunteer computing

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distributed Data Center Bandwidth Allocation for Cloud-Based Streaming

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2019年第2期4卷 263-276页

作者： Kong, Fanxin Lu, Xingjian Liu, Xue Univ Penn Dept Comp & Informat Sci Philadelphia PA 19104 USA East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Smart City Collaborat Innovat Ctr Shanghai Peoples R China McGill Univ Sch Comp Sci Montreal PQ H3A 0G4 Canada

Cloud-based video streaming systems such as YouTube and Netflix are usually supported by the content delivery networks and data centers that can consume many megawatts of power. Most existing work independently studies the issues of improving quality of experience (QoE) for viewers and reducing the cost and emissions associated with the enormous energy usage of data centers. By contrast, this paper addresses them both, and jointly optimizes the QoE, the energy cost and emissions by intelligently allocating data center bandwidth among different client groups. Specially, we propose a distributed algorithm to achieve the optimal bandwidth allocation, given the prediction of future workload. The algorithm novelly decomposes the optimization process into separate ones, which are solved iteratively across data centers and clients. Further, the algorithm has robust performance guarantee in terms of the variance of the prediction error. We demonstrate its convergence and robustness by both proofs using theoretical analysis and validation based on trace-driven simulations. The results further show that the proposed algorithm converges very fast and achieves much better QoE-cost balance than existing approaches.

关键词： Cloud-based streaming dynamic streaming data centers energy cost quality of experience bandwidth allocation distributed algorithms

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