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A social-relation-based game model for distributed clustering in cooperative wireless networks 19

A social-relation-based game model for distributed clusterin...

Proceedings of the 16th EAI international conference on Mobile and Ubiquitous Systems: computing, networking and Services

作者： Li Bowen Zhang Shunliang Shan Xu Wang Yongming Gao Zhenxiang UCAS Beijing China CAS Beijing China UConn Storrs

ISBN: (纸本)9781450372831

In this paper, a novel framework for cluster detection in co-operative wireless networks is proposed. This framework is modeled by a dynamic game with incomplete information, in which each player in the game aspires to improve its position in the network by forming cooperative groups. Instead of static systems, the attention we paid in this paper is highly dynamic networks, where the users' high mobility brings a huge challenge in clustering. In order to mitigate that impact, this paper is from the perspective of social relations to clustering, extracting users' social nature from their mobile patterns and designing distributed cluster strategy based on game model. The introduction of social nature with generally long-term characteristics makes clustering framework more predictive and stable. Simulations on real-world networks show that the proposed approach performs well in clustering in cooperative wireless networks.

关键词： 5G cooperative wireless networks distributed cluster game theory social relations

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Using simple PID-inspired controllers for online resilient resource management of distributed scientific workflows

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FUTURE GENERATION COMPUTER SYSTEMS-THE international JOURNAL OF ESCIENCE 2019年 95卷 615-628页

作者： da Silva, Rafael Ferreira Filgueira, Rosa Deelman, Ewa Pairo-Castineira, Erola Overton, Ian M. Atkinson, Malcolm P. Univ Southern Calif Informat Sci Inst Marina Del Rey CA 90292 USA British Geol Survey Lyell Ctr Edinburgh EH14 4AP Midlothian Scotland Univ Edinburgh Sch Informat Edinburgh EH8 9LE Midlothian Scotland Univ Edinburgh MRC Inst Genet & Mol Med Edinburgh Midlothian Scotland Univ Edinburgh Usher Inst Populat Hlth Sci & Informat Edinburgh Midlothian Scotland Queens Univ Belfast Ctr Canc Res & Cell Biol Belfast Antrim North Ireland

Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. Although the scientific community has addressed this challenge from both theoretical and practical approaches, failure prediction, detection, and recovery still raise many research questions. In this paper, we propose an approach inspired by the control theory developed as part of autonomic computing to predict failures before they happen, and mitigated them when possible. The proposed approach is inspired on the proportional-integral-derivative controller (PID controller) control loop mechanism, which is widely used in industrial control systems, where the controller will react to adjust its output to mitigate faults. PID controllers aim to detect the possibility of a non-steady state far enough in advance so that an action can be performed to prevent it from happening. To demonstrate the feasibility of the approach, we tackle two common execution faults of large scale data-intensive workflows-data storage overload and memory overflow. We developed a simulator, which implements and evaluates simple standalone PID-inspired controllers to autonomously manage data and memory usage of a data-intensive bioinformatics workflow that consumes/produces over 4.4 TB of data, and requires over 24 TB of memory to run all tasks concurrently. Experimental results obtained via simulation indicate that workflow executions may significantly benefit from the controller-inspired approach, in particular under online and unknown conditions. Simulation results show that nearly-optimal executions (slowdown of 1.01) can be attained when using our proposed method, and faults are detected and mitigated far in advance of their occurrence. (C) 2019 Elsevier B.V. All rights re

关键词： Scientific workflows Fault detection and handling Resilient Big Data workflows Autonomic computing

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An Architecture for distributed Ledger-Based M2M Auditing for Electric Autonomous Vehicles 33rd

An Architecture for Distributed Ledger-Based M2M Auditing fo...

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33rd international conference on Advanced Information networking and Applications, AINA 2019

作者： Strugar, Dragos Hussain, Rasheed Mazzara, Manuel Rivera, Victor Afanasyev, Ilya Lee, JooYoung Innopolis University Universitetskaya str. 1 Innopolis420500 Russia

ISBN: (纸本)9783030150341

Electric Autonomous Vehicles (EAVs) promise to be an effective way to solve transportation issues such as accidents, emissions and congestion, and aim at establishing the foundation of Machine-to-Machine (M2M) economy. For this to be possible, the market should be able to offer appropriate charging services without involving humans. The state-of-the-art mechanisms of charging and billing do not meet this requirement, and often impose service fees for value transactions that may also endanger users and their location privacy. This paper aims at filling this gap and envisions a new charging architecture and a billing framework for EAV which would enable M2M transactions via the use of distributed Ledger Technology (DLT). © 2019, Springer Nature Switzerland AG.

关键词： distributed ledger

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Design and Implementation of intelligent Learning Companion System Based on WeChat Mini Program

Design and Implementation of intelligent Learning Companion ...

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international conference on Artificial Intelligence and Education (ICAIE)

作者： Xue Zhang Xiaomei Yu Wenqian Sun School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory of Distributed Computing Software Jinan China

ISBN: (数字)9781728166599

ISBN: (纸本)9781728199207

During the period of COVID-19 epidemic situation, in order to ensure that students can study normally, the Ministry of Education has issued measures to “suspend classes without stopping learning”. Therefore, the traditional forms can't meet the needs of learners, more and more people like online education. The existing online learning platforms cannot perfectly meet the learning needs of students, so the students need an online learning system that can meet their individual needs and improve learning efficiency. This article introduces an intelligent learning companion system, the system based on the advantages of WeChat Mini Programs can help learners achieve online learning and meet personalized needs.

关键词： Learning (artificial intelligence) Videos Social networking (online) Message service Hafnium oxide Education conferences

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MESH: A Flexible distributed Hypergraph Processing System 7

MESH: A Flexible Distributed Hypergraph Processing System

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7th IEEE international conference on Cloud Engineering (IEEE IC2E)

作者： Heintz, Benjamin Hong, Rankyung Singh, Shivangi Khandelwal, Gaurav Tesdahl, Corey Chandra, Abhishek Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781728102184

With the rapid growth of large online social networks, the ability to analyze large-scale social structure and behavior has become critically important, and this has led to the development of several scalable graph processing systems. In reality, however, social interaction takes place not only between pairs of individuals as in the graph model, but rather in the context of multi-user groups. Research has shown that such group dynamics can be better modeled through a more general hypergraph model, resulting in the need to build scalable hypergraph processing systems. In this paper, we present MESH, a flexible distributed framework for scalable hypergraph processing. MESH provides an easy-to-use and expressive application programming interface that naturally extends the "think like a vertex" model common to many popular graph processing systems. Our framework provides a flexible implementation based on an underlying graph processing system, and enables different design choices for the key implementation issues of partitioning a hypergraph representation. We implement MESH on top of the popular GraphX graph processing framework in Apache Spark. Using a variety of real datasets and experiments conducted on a local 8-node cluster as well as a 65-node Amazon AWS testbed, we demonstrate that MESH provides flexibility based on data and application characteristics, as well as scalability with cluster size. We further show that it is competitive in performance to HyperX, another hypergraph processing system based on Spark, while providing a much simpler implementation (requiring about 5X fewer lines of code), thus showing that simplicity and flexibility need not come at the cost of performance.

关键词： Computational modeling Manganese Cluster computing Scalability Partitioning algorithms Sparks Social networking (online)

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Newton-ADMM: A distributed GPU-Accelerated Optimizer for Multiclass Classification Problems 20

Newton-ADMM: A Distributed GPU-Accelerated Optimizer for Mul...

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Supercomputing conference

作者： Chih-Hao Fang Sudhir B. Kylasa Fred Roosta Michael W. Mahoney Ananth Grama Computer Science Purdue University West Lafayette US Electrical and Computer Engineering Purdue University West Lafayette US School of Mathematics and Physics University of Queensland Brisbane Australia ICSI Berkeley US

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

First-order optimization techniques, such as stochastic gradient descent (SGD) and its variants, are widely used in machine learning applications due to their simplicity and low per-iteration costs. However, they often require larger numbers of iterations, with associated communication costs in distributed environments. In contrast, Newton-type methods, while having higher per-iteration computation costs, typically require a significantly smaller number of iterations, which directly translates to reduced communication costs. We present a novel distributed optimizer for classification problems, which integrates a GPU-accelerated Newton-type solver with the global consensus formulation of Alternating Direction of Method Multipliers (ADMM). By leveraging the communication efficiency of ADMM, a highly efficient GPUaccelerated inexact-Newton solver, and an effective spectral penalty parameter selection strategy, we show that our proposed method (i) yields better generalization performance on several classification problems; (ii) significantly outperforms state-of-the-art methods in distributed time to solution; and (iii) offers better scaling on large distributed platforms.

关键词： Scalability Neural networks Optimization methods Machine learning Convex functions Newton method Standards

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distributed processor load balancing based on multi-objective extremal optimization 1

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12th international conference on Internet and distributed computing Systems, IDCS 2019

作者： De Falco, Ivanoe Laskowski, Eryk Olejnik, Richard Scafuri, Umberto Tarantino, Ernesto Tudruj, Marek Institute of High Performance Computing and Networking CNR Naples Italy Institute of Computer Science Polish Academy of Sciences Warsaw Poland Université Lille—CRISTAL CNRS Lille France Polish-Japanese Academy of Information Technology Warsaw Poland

ISBN: (数字)9783030349141

ISBN: (纸本)9783030349134

The paper proposes and discusses distributed processor load balancing algorithms which are based on nature inspired approach of multi-objective Extremal Optimization. Extremal Optimization is used for defining task migration aiming at processor load balancing in execution of graph-represented distributed programs. The analysed multi-objective algorithms are based on three or four criteria selected from the following four choices: the balance of computational loads of processors in the system, the minimal total volume of application data transfers between processors, the number of task migrations during program execution and the influence of task migrations on computational load imbalance and the communication volume. The quality of the resulting load balancing is assessed by simulation of the execution of the distributed program macro data flow graphs, including all steps of the load balancing algorithm. It is done following the event-driven model in a simulator of a message passing multiprocessor system. The experimental comparison of the multi-objective load balancing to the single objective algorithms demonstrated the superiority of the multi-objective approach. © Springer Nature Switzerland AG 2019.

关键词： Multiobjective optimization

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MedRec: A Network for Personal Information Distribution

MedRec: A Network for Personal Information Distribution

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international conference on computing, networking and Communications (ICNC)

作者： Nchinda, Nchinda Cameron, Agnes Retzepi, Kallirroi Lippman, Andrew MIT Media Lab Cambridge MA 02139 USA

ISBN: (纸本)9781538692233

MedRec is a simple, distributed system for personal control of identity and distribution of personal information. The work is done in the context of a medical information distribution system where patients retain control over who can access their data. We present a new architecture for the MedRec project, creating a network of trusted data repositories, the access to which are determined by a set of "smart contracts". These contracts are stored on a distributed ledger maintained by those who generate data. The distributed nature of the system allows unified access from diverse sources in a single application with no intermediary. This increases patient control while retaining a measure of privacy of both data content and source. MedRec is amenable to extensions for decentralized messaging and distribution of information to third parties such as medical researchers, healthcare proxies, and other institutions. The system is based on a blockchain that contains smart contracts defining user identity and distribution specifics.

关键词： blockchain medical records healthcare distributed ethereum

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Convolutional Neural Network Training with distributed K-FAC 20

Convolutional Neural Network Training with Distributed K-FAC

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Supercomputing conference

作者： J. Gregory Pauloski Zhao Zhang Lei Huang Weijia Xu Ian T. Foster University of Texas at Austin Texas Advanced Computing Center University of Chicago & Argonne National Laboratory

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

Training neural networks with many processors can reduce time-to-solution; however, it is challenging to maintain convergence and efficiency at large scales. The Kroneckerfactored Approximate Curvature (K-FAC) was recently proposed as an approximation of the Fisher Information Matrix that can be used in natural gradient optimizers. We investigate here a scalable K-FAC design and its applicability in convolutional neural network (CNN) training at scale. We study optimization techniques such as layer-wise distribution strategies, inverse-free second-order gradient evaluation, and dynamic K-FAC update decoupling to reduce training time while preserving convergence. We use residual neural networks (ResNet) applied to the CIFAR10 and ImageNet-1k datasets to evaluate the correctness and scalability of our K-FAC gradient preconditioner. With ResNet-50 on the ImageNet-1k dataset, our distributed K-FAC implementation converges to the 75.9% MLPerf baseline in 18-25% less time than does the classic stochastic gradient descent (SGD) optimizer across scales on a GPU cluster.

关键词： Training Scalability Neural networks Convolutional neural networks Optimization Convergence Residual neural networks

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Virtual Edge computing Using Vehicular Micro Clouds

Virtual Edge Computing Using Vehicular Micro Clouds

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international conference on computing, networking and Communications (ICNC)

作者： Dressler, Falko Pannu, Gurjashan Singh Hagenauer, Florian Gerla, Mario Higuchi, Takamasa Altintas, Onur Paderborn Univ Heinz Nixdorf Inst Paderborn Germany Paderborn Univ Dept Comp Sci Paderborn Germany Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA Toyota InfoTechnol Ctr USA Mountain View CA USA

ISBN: (纸本)9781538692233

We will discuss the challenges and opportunities of the connected cars vision in relation to the need for distributed data management solutions ranging from the vehicle to the mobile edge and to the data centers. As a novel concept, vehicular micro clouds have been proposed that bridge the gap between fully distributed vehicular networks based on short range vehicle to vehicle communication and cellular based infrastructure for centralized solutions. We will discuss the need for vehicular micro clouds, followed by the architecture, formation of micro clouds, and feasibility of micro clouds. Furthermore, we will cover aspects of efficient data upload and download between cars and a data center facilitated by our micro cloud concept.

关键词： Vehicular Cloud Mobile Edge computing Virtual Cloud Architecture Vehicular networking

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