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IEEE International Symposium on Information Theory (ISIT)

作者： Srinivasavaradhan, Sundara Rajan Song, Linqi Fragouli, Christina Univ Calif Los Angeles Los Angeles CA 90095 USA

ISBN: (纸本)9781538647813

Trade-offs between distributed computation and communication are recently attracting significant interest;however, these works assume that all nodes that share the distributed computation task are within the same broadcast domain, and each can losslessly broadcast to every other node that takes part in the computation task. In this work, we dispose of this assumption, and consider the case where each node can broadcast to a subset of the nodes that take part in the computation task. We model the network via an Erdos-Renyi random graph model where a pair of nodes can communicate with each other with a probability p. We propose both uncoded and coded transmission schemes and give an achievable communication-computation tradeoff for large computational loads.

关键词： distributed computation Broadcasting Map-Reduce Communication-computation Trade-Off Random Connectivity

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STARLORD: Sliding window Temporal Accumulate-Retract Learning for Online Reasoning on Datastreams 17

STARLORD: Sliding window Temporal Accumulate-Retract Learnin...

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17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Axenie, Cristian Tudoran, Radu Bortoli, Stefano Hassan, Mohamad Al Hajj Foroni, Daniele Brasche, Goetz Huawei German Res Ctr Munich Germany Univ Trento Informat Engn & Comp Sci Dept Trento Italy

ISBN: (纸本)9781538668054

Nowadays, data sources, such as IoT devices, financial markets, and online services, continuously generate large amounts of data. Such data is usually generated at high frequencies and is typically described by non-stationary distributions. Querying these data sources brings new challenges for machine learning algorithms, which now need to be considered from the perspective of an evolving stream and not a static dataset. Under such scenarios, where data flows continuously, the challenge is how to transform the vast amount of data into information and knowledge, and how to adapt to data changes (i.e. drifts) and accumulate experience over time to support online decision-making. In this paper, we introduce STARLORD, a novel incremental computation method and system acting on data streams and capable of achieving low-latency (millisecond level) and high-throughput (thousands events/second/core) when learning from data streams. Moreover, the approach is able to adapt to data drifts and accumulate experience over time, and to use such knowledge to improve future learning and prediction performance, with resource usage guarantees. This is proven by our preliminary experiments where we built-in the framework in an open source stream engine (i.e. Apache Flink).

关键词： Streaming Data Online Machine Learning distributed computation Incremental computation

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Quantifying Long-Range Interactions and Coherent Structure in Multi-Agent Dynamics

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ARTIFICIAL LIFE 2017年第1期23卷 34-57页

作者： Cliff, Oliver M. Lizier, Joseph T. Wang, X. Rosalind Wang, Peter Obst, Oliver Prokopenko, Mikhail Univ Sydney Australian Ctr Field Robot Rose St Bldg J04 Sydney NSW 2006 Australia Univ Sydney Ctr Complex Syst Civil Engn Bldg J05 Sydney NSW 2006 Australia CSIRO Locked Bag 17 N Ryde NSW 1670 Australia Univ Western Sydney Sch Comp Engn & Math Ctr Res Math Locked Bag 1797 Penrith NSW 2751 Australia

We develop and apply several novel methods quantifying dynamic multi-agent team interactions. These interactions are detected information-theoretically and captured in two ways: via (i) directed networks (interaction diagrams) representing significant coupled dynamics between pairs of agents, and (ii) state-space plots (coherence diagrams) showing coherent structures in Shannon information dynamics. This model-free analysis relates, on the one hand, the information transfer to responsiveness of the agents and the team, and, on the other hand, the information storage within the team to the team's rigidity and lack of tactical flexibility. The resultant interaction and coherence diagrams reveal implicit interactions, across teams, that may be spatially long-range. The analysis was verified with a statistically significant number of experiments (using simulated football games, produced during RoboCup 2D Simulation League matches), identifying the zones of the most intense competition, the extent and types of interactions, and the correlation between the strength of specific interactions and the results of the matches.

关键词： Multi-agent dynamics distributed computation implicit communication information storage information transfer

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distributed monitoring for the prevention of cascading failures in operational power grids

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INTERNATIONAL JOURNAL OF CRITICAL INFRASTRUCTURE PROTECTION 2017年 17卷 15-27页

作者： Warnier, Martijn Dulman, Stefan Koc, Yakup Pauwels, Eric Delft Univ Technol Fac Technol Policy & Management Syst Engn Jaffalaan 5 NL-2628 BX Delft Netherlands CWI Intelligent Syst Grp Sci Pk 123 NL-1098 XG Amsterdam Netherlands Stedin Risk & Informat Management Blaak 8 NL-3011 TA Rotterdam Netherlands

Electrical power grids are vulnerable to cascading failures that can lead to large blackouts. The detection and prevention of cascading failures in power grids are important problems. Currently, grid operators mainly monitor the states (loading levels) of individual components in a power grid. The complex architecture of a power grid, with its many interdependencies, makes it difficult to aggregate the data provided by local components in a meaningful and timely manner. Indeed, monitoring the resilience of an operational power grid to cascading failures is a major challenge. This paper attempts to address this challenge. It presents a robustness metric based on the topology and operative state of a power grid to quantify the robustness of the grid. Also, it presents a distributed computation method with self-stabilizing properties that can be used for near real-time monitoring of grid robustness. The research thus provides insights into the resilience of a dynamic operational power grid to cascading failures during real-time in a manner that is both scalable and robust. computations are pushed to the power grid network, making the results available at each node and enabling automated distributed control mechanisms to be implemented. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Power Grids Cascading Failures Robustness Real-Time Monitoring distributed computation

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Sparse representations based distributed attribute learning for person re-identification

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第23期76卷 25015-25037页

作者： Cheng, Keyang Hui, Kaifa Zhan, Yongzhao Li, Maozhen Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Brunel Univ Sch Engn & Design Uxbridge UB8 3PH Middx England

Searching for specific persons from surveillance videos captured by different cameras, known as person re-identification, is a key yet under-addressed challenge. Difficulties arise from the large variations of human appearance in different poses, and from the different camera views that may be involved, making low-level descriptor representation unreliable. In this paper, we propose a novel Sparse Representations based distributed Attribute Learning Model (SRDAL) to encode targets into semantic topics. Compared to other models such as ELF, our model performs best by imposing semantic restrictions onto the generation of human specific attributes and employing Deep Convolutional Neural Network to generate features without supervision for attributes learning model. Experimental results show that our method achieves state-of-the-art performance.

关键词： Person re-identification Sparse representations Attribute learning Deep convolutional neural network distributed computation

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Big Data Clustering with Kernel k-Means: Resources, Time and Performance

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2018年第4期27卷

作者： Tsapanos, Nikolaos Tefas, Anastasios Nikolaidis, Nikolaos Pitas, Ioannis Aristotle Univ Thessaloniki Dept Informat Univ CampusBox 54 124 Thessaloniki Greece

Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. A classic clustering algorithm is the so-called k-Means. It is very popular, however, it is also unable to handle cases in which the clusters are not linearly separable. Kernel k-Means is a state of the art clustering algorithm, which employs the kernel trick, in order to perform clustering on a higher dimensionality space, thus overcoming the limitations of classic k-Means regarding the non-linear separability of the input data. With respect to the challenges of Big Data research, a field that has established itself in the last few years and involves performing tasks on extremely large amounts of data, several adaptations of the Kernel k-Means have been proposed, each of which has different requirements in processing power and running time, while also incurring different trade-offs in performance. In this paper, we present several issues and techniques involving the usage of Kernel k-Means for Big Data clustering and how the combination of each component in a clustering framework fares in terms of resources, time and performance. We use experimental results, in order to evaluate several combinations and provide a recommendation on how to approach a Big Data clustering problem.

关键词： Big data kernel k-means data clustering approximate kernel k-means Apache Spark distributed computation

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Solving long haul airline disruption problem caused by groundings using a distributed fixed-point computational approach to integer programming

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NEUROCOMPUTING 2017年 269卷 232-255页

作者： Wu, Zhengtian Li, Benchi Dang, Chuangyin Hu, Fuyuan Zhu, Qixin Fu, Baochuan Suzhou Univ Sci & Technol Suzhou Jiangsu Peoples R China City Univ Hong Kong Hong Kong Hong Kong Peoples R China

Disruptions are prevalent phenomenons that prevent airline from operating as original scheduled. This paper adopts the iterative fixed-point method for integer programming proposed by Dang and Ye [1] to generate feasible flight routes that are used to construct an aircraft reassignment in response to the grounding of one aircraft. Two division methods are proposed with which the solution space can be divided into several independent segments and implemented a distributed computation. The second division method is emphasized in this paper for the good performance of partial feasible flight routes which are generated by this division approach. Comparison with CPLEX CP Optimizer [2] shows that less partial feasible flight routes which are generated by Dang's algorithm [1] are required to find an aircraft reassignment when disruptions happen, and this division method is more promising when dealing with long haul airline disruption problem. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Airline disruption management Irregular operation Fixed-point method Integer programming distributed computation Lexicographical order MPI

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Lock-Step Simulation Is Child's Play (Experience Report)

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PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL 2017年第ICFP期1卷 1-15页

作者： Breitner, Joachim Smith, Chris Univ Penn Philadelphia PA 19104 USA Google Mountain View CA 94043 USA

Implementing multi-player networked games by broadcasting the player's input and letting each client calculate the game state - a scheme known as lock-step simulation - is an established technique. However, ensuring that every client in this scheme obtains a consistent state is infamously hard and in general requires great discipline from the game programmer. The thesis of this report is that in the realm of functional programming - in particular with Haskell's purity and static pointers - this hard problem becomes almost trivially easy. We support this thesis by implementing lock-step simulation under very adverse conditions. We extended the educational programming environment CodeWorld, which is used to teach math and programming to middle school students, with the ability to create and run interactive, networked multi-user games. Despite providing a very abstract and high-level interface, and without requiring any discipline from the programmer, we can provide consistent lock-step simulation with client prediction.

关键词： teaching lock-step simulation distributed computation

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MUSCLE-HPC: A new high performance API to couple multiscale parallel applications

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 67卷 72-82页

作者： Ben Belgacem, Mohamed Chopard, Bastien Univ Geneva Dept Comp Sci Batiment ARoute Drize 7 CH-1227 Geneva Switzerland

Multiscale, multi-physics applications are central to solve the increasing number of important scientific challenges. computationally speaking, the difficulty is to combine high performance computing with the need to couple various codes or solvers, each representing a different scale or a different physical process. In this paper, we present MUSCLE-HPC a new HPC implementation of MUSCLE-2, a previously developed Multiscale Coupling Library and Environment. We present its design and implementation and we demonstrate its advantages compared to MUSCLE-2. We conduct a performance comparison through a tightly coupled MPI application use-case. Our results indicate that using MUSCLE-HPC to couple submodels within the same HPC cluster can lead to better computing performance comparable to a native MPI execution and can, thus, reduce the coupling overhead. (C) 2016 Elsevier B.V. All rights reserved.

关键词： distributed computation HPC infrastructure Multiscale coupling

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Network Flows That Solve Linear Equations

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2017年第6期62卷 2659-2674页

作者： Shi, Guodong Anderson, Brian D. O. Helmke, Uwe Australian Natl Univ Coll Engn & Comp Sci Res Sch Engn Canberra ACT 0200 Australia Australian Natl Univ Coll Engn & Comp Sci Res Sch Engn NICTA Canberra ACT 0200 Australia Univ Wurzburg Inst Math D-97074 Wurzburg Germany

We study distributed network flows as solvers in continuous time for the linear algebraic equationz = Hy. Each node i has access to a row hT i of the matrix H and the corresponding entry zi in the vector z. The first "consensus + projection" flow under investigation consists of two terms, one from standard consensus dynamics and the other contributing to projection onto each affine subspace specified by the hi and zi. The second "projection consensus" flow on the other hand simply replaces the relative state feedback in consensus dynamics with projected relative state feedback. Without dwell-time assumption on switching graphs, we prove that all node states converge to a common solution of the linear algebraic equation, if there is any. The convergence is global for the "consensus + projection" flow while local for the "projection consensus" flow in the sense that the initial values must lie on the affine subspaces. If the linear equation has no exact solutions, we show that the node states can converge to a ball around the least-squares solution whose radius can be made arbitrarily small through selecting a sufficiently large gain for the "consensus + projection" flow for a fixed bidirectional graph. Semi-global convergence to approximate least-squares solutions is also demonstrated for switching balanced directed graphs under suitable conditions. It is also shown that the "projection consensus" flow drives the average of the node states to the least-squares solution with a complete graph. Numerical examples are provided as illustrations of the established results.

关键词： Networks distributed computation linear algebraic equations

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