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Near-Optimal Clustering in the k-machine model 18

Near-Optimal Clustering in the <i>k</i>-machine model

引用

19th international conference on distributed computing and networking (ICDCN)

作者： Bandyapadhyay, Sayan Inamdar, Tanmay Pai, Shreyas Pemmaraju, Sriram V. Univ Iowa Iowa City IA 52242 USA

ISBN: (纸本)9781450363723

The clustering problem, in its many variants, has numerous applications in operations research and computer science (e.g., in applications in bioinformatics, image processing, social network analysis, etc.). As sizes of data sets have grown rapidly, researchers have focused on designing algorithms for clustering problems in models of computation suited for large-scale computation such as MapReduce, Pregel, and streaming models. The k-machine model (Klauck et al., SODA 2015) is a simple, message-passing model for large-scale distributed graph processing. This paper considers three of the most prominent examples of clustering problems: the uncapacitated facility location problem, the p-median problem, and the p-center problem and presents O(1)-factor approximation algorithms for these problems running in (O) over tilde (n/k) rounds in the k-machine model. These algorithms are optimal upto polylogarithmic factors because this paper also shows (Omega) over tilde (n/k) lower bounds for obtaining poly(n)-factor approximation algorithms for these problems. These are the first results for clustering problems in the k-machine model. We assume that the metric provided as input for these clustering problems in only implicitly provided, as an edge-weighted graph and in a nutshell, our main technical contribution is to show that constant-factor approximation algorithms for all three clustering problems can be obtained by learning only a small portion of the input metric.

关键词： Clustering Facility location k-median k-center k-machine model large-scale clustering distributed clustering

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distributed Memory Sparse Inverse Covariance Matrix Estimation on High-Performance computing Architectures

Distributed Memory Sparse Inverse Covariance Matrix Estimati...

引用

international conference on High Performance computing, networking, Storage, and Analysis (SC)

作者： Eftekhari, Aryan Bollhoefer, Matthias Schenk, Olaf Univ Svizzera Italiana Inst Computat Sci Lugano Switzerland TU Braunschweig Inst Computat Math Braunschweig Germany

ISBN: (纸本)9781538683842

We consider the problem of estimating sparse inverse covariance matrices for high-dimensional datasets using the l(1)-regularized Gaussian maximum likelihood method. This task is particularly challenging as the required computational resources increase superlinearly with the dimensionality of the dataset. We introduce a performant and scalable algorithm which builds on the current advancements of second-order, maximum likelihood methods. The routine leverages the intrinsic parallelism in the linear algebra operations and exploits the underlying sparsity of the problem. The computational bottlenecks are identified and the respective subroutines are parallelized using an MPI-OpenMP approach. Experiments conducted on a 5;320 node Cray XC50 system at the Swiss National Supercomputing Center show that, in comparison to the state-of-the-art algorithms, the proposed routine provides significant strong scaling speedup with ideal scalability up to 128 nodes. The developed framework is used to estimate the sparse inverse covariance matrix of both synthetic and real-world datasets with up to 10 million dimensions.

关键词： inverse covariance matrix sparse matrices approximate matrix inverse high-performance computing

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WiBot! In-Vehicle Behaviour and Gesture Recognition Using Wireless Network Edge 38

<i>WiBot</i>! In-Vehicle Behaviour and Gesture Recognition U...

引用

38th IEEE international conference on distributed computing Systems (ICDCS)

作者： Raja, Muneeba Ghaderi, Viviane Sigg, Stephan Aalto Univ Commun & Networking Espoo Finland BMW Res Innovat Machine Intelligence Munich Germany

ISBN: (纸本)9781538668719

Recent advancements in vehicular technology have meant that integrated wireless devices such as Wi-Fi access points or bluetooth are deployed in vehicles at an increasingly dense scale. These vehicular network edge devices, while enabling in car wireless connectivity and infotainment services, can also be exploited as sensors to improve environmental and behavioural awareness that in turn can provide better and more personalised driver feedback and improve road safety. We present WiBot! a network-edge based behaviour recognition and gesture based personal assistant for cars. WiBot leverages the vehicular network edge to detect distracted behaviour based on unusual head turns and arm movements during driving by monitoring radio frequency fluctuation patterns in real-time. WiBot can recognise known gestures from natural arm movements while driving and use such gestures for passenger-car interaction. A key element of WiBot design is its impulsive windowing approach that allows start and end of gestures to be accurately identified in a continuous stream of data. We validate the system in a realistic driving environment by conducting a non-choreographed continuous recognition study with 40 participants at BMW Group Research, New Technologies and Innovation centre. By combining impulsive windowing with a unique selection of features from peaks and subcarrier analysis of RF CSI phase information, the system is able to achieve 94.5% accuracy for head- vs. arm movement separation. We can further confidently differentiate relevant gestures from random arm and head movements, head turns and idle movement with 90.5% accuracy.

关键词： Wireless fidelity OFDM Automobiles Sensors Receivers Mathematical model

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Sequence Data Enhancement Method Based on Knowledge Graph

Sequence Data Enhancement Method Based on Knowledge Graph

引用

IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Huosheng Xie Wenda Chai Shufeng Lin College of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

To solve the problem of low recommendation accuracy caused by too little user behavior information in the current behavior recommendation system, an algorithm based on end-to-end data enhancement was proposed. In this paper, knowledge graph is constructed by learning and integrating structured knowledge network. Moreover, the characteristics of users with high preference similarity can be propagated through the inter-entity relations mapped by the knowledge map to reconstruct the preference vector of users. Through comparative experiments on open data sets, the AUC of RNN model, CNN model, RNN attention model and ATRank were improved by 3.28%, 2.35%, 2.89% and 1.30%, respectively.

关键词： 6G mobile communication distributed processing Big Data Cloud computing Social computing Channel hot electron injection

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distributed Maintenance: A Literature Analysis and Classification 9

Distributed Maintenance: A Literature Analysis and Classific...

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9th IFAC/IFIP/IFORS/IISE/INFORMS conference on Manufacturing Modelling, Management and Control (IFAC MIM)

作者： Attajer, Ali Darmoul, Saber Riane, Fouad Bouras, Abdelghani Ecole Cent Casablanca Casablanca 27182 Morocco Cent Supelec LGI EA 2606 Rue Joliot Curie F-91190 Gif Sur Yvette France

Recent technological advances, such as Wireless Sensor and Ad-hoc Networks, Internet of Things (IoT), Cloud computing, multi-agent systems, etc. led to the emergence of control paradigms and architectures, where information processing and decision making are distributed/decentralized over several smart production objects (machines, robots, products, etc.). Indeed, a growing research effort focused on developing distributed control architectures, where production equipment (machines, robots, etc.) are endowed with some kind of intelligence to carry production planning, control, and maintenance engineering tasks. However, only a limited number of works focused on developing smart/intelligent product approaches, where products play an active role in the decision processes. Within the smart/intelligent product community, only a few works are interested in endowing products with capabilities that make them able to deal with disturbances and problems related to maintenance engineering, and to influence maintenance decisions and strategies in an autonomous, adaptive and resilient way. This position paper investigates literature to shed light on maintenance distribution possibilities. Important questions are addressed, such as: What kinds of information and decisions are distributed in maintenance engineering problems? How is such distribution achieved so far? What active roles do smart/intelligent production objects, especially products, endorse in such architectures? What are the benefits of such distribution, and how does it impact maintenance tasks in smart factories of the future? And finally, what are the challenges and future research perspectives that are still worth investigating to foster developments in this field? (C) 2019, IFAC (international Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Distribution Maintenance engineering Classification Smart/Intelligent Products Architecture

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Pipe-Torch: Pipeline-Based distributed Deep Learning in a GPU Cluster with Heterogeneous networking

Pipe-Torch: Pipeline-Based Distributed Deep Learning in a GP...

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international conference on Advanced Cloud and Big Data (CBD)

作者： Jun Zhan Jinghui Zhang Southeast University NanJing China

Because training a deep neural network (DNN) takes arduous amounts of time and computation, often researchers expedite the training process via distributed parallel training on GPUs. On one hand, this lower computing-to-communication ratio makes traditional data parallelism difficult to scale, and traditional model parallelism leads to low GPU utilization. Both make it difficult to obtain a higher speedup. On the other hand, multi-GPU systems exhibit complex connectivity among GPUs. Overall, workload schedulers must consider hardware topology and workload communication requirements to allocate GPU resources for optimal execution time and improved utilization in GPU clusters with heterogeneous networking. Thus, in this paper, we introduce Pipe-torch, an improved pipeline-hybrid parallelism method (using both data and model parallelism) in a heterogeneous network environment. Ultimately, the framework's model partition algorithm aims to expedite pipeline-hybrid parallelism training between heterogeneous network-connected GPUs. Experiments with four different DNN models show that Pipetorch averages 1.4x speedup compared to data parallelism.

关键词： Graphics processing units Training Parallel processing Computational modeling Data models Pipelines Load modeling

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An Efficient Topology Refining Scheme for Apache Flink 9

An Efficient Topology Refining Scheme for Apache Flink

引用

9th international conference on Information and Communication Technology Convergence (ICTC)

作者： Hanif, Muhammad Lee, Choonhwa Hanyang Univ Div Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781538650417

In the past decade, there has been a boom in the volume of data and in the popularity of cloud applications with industry and academia keenly interested in big data analytics, streaming application, and social networking applications. This led to the emergence of real-time distributed stream processing systems such as Flink, Storm, Dataflow, and Samza. These systems process complex queries on streaming data sets to be distributed across multiple worker nodes in a cluster. Few of them provide adequate supports to adapt the topologies of stream processing tasks to changing input workload. We present an intelligent and efficient topology adjustment scheme which allow Flink framework to refine its topology on the basis of incoming workload. It is designed to increase the overall performance by making the refining of topology robust according to incoming workload streams on the fly, while maintaining SLA constraints. Apache Flink distributed processing engine is used as testbed in the paper. Our preliminary results indicate that the proposed system outperforms the existing default framework.

关键词： Big Data distributed computing Stream Processing Engine Topology Cloud computing

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Game-Theoretical Energy Management for Residential User and Micro Grid for Optimum Sizing of Photo Voltaic Battery Systems and Energy Prices 33rd

Game-Theoretical Energy Management for Residential User and ...

引用

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

作者： Naz, Aqdas Javaid, Nadeem Majeed Khan, Abdul Basit Iqbal, Muhammad Mudassar ur Rehman Hashmi, Muhammad Aqeel Abbasi, Raheel Ahmad COMSATS University Islamabad44000 Pakistan Abasyn University Islamabad Campus Islamabad44000 Pakistan Riphah International University Islamabad44000 Pakistan University of Engineering and Technology Taxila47050 Pakistan Sardar Bahadur Khan Women University Quetta87300 Pakistan

ISBN: (纸本)9783030150341

There is emerging trend in power system, i.e., energy internet that provides energy production, transmission, storage and utilization. Which is used to manage and control energy centrally by using information and communication technologies. In this paper, coordinated management of renewable and traditional energy is focused. In proposed work, storage system is embedded with renewable resources in microgrid, so that after satisfying users energy requirement, surplus energy can be stored in battery. Energy management is performed with storage capacity includes cost of renewable resources, depreciation cost of battery and bidirectional energy transmission. User and microgrid are two players that are involved in non cooperative game theory. In order to maximize the payoff of user as well as microgrid, the two stage non cooperative game theoretic method optimizes battery capacity and prices. Which are charged by micro grid from user and optimize user energy consumption. The distributed algorithm is proposed to explain nash equilibrium which ensures Pareto optimality in terms of increasing pay off of both stakeholder. Furthermore, forecasting algorithm back propagation (BP), Support Vector Machine (SVM) and Stacked Auto Encoder (SAE) are used for forecasting historical data related to solar power generation. Predicted data is, thus used by microgrid in defining energy prices and battery storage capacity. © 2019, Springer Nature Switzerland AG.

关键词： Game theory

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A Comparative Analysis of Hybrid Routing Schemes for SDN based Wireless Mesh Networks 20

A Comparative Analysis of Hybrid Routing Schemes for SDN bas...

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20th IEEE international conference on High Performance computing and Communications (HPCC) / 16th IEEE international conference on Smart City (SmartCity) / 4th IEEE international conference on Data Science and Systems (DSS)

作者： Bano, Mukhtiar Gilani, Syed Sherjeel A. Qayyum, Amir Fatima Jinnah Women Univ Software Engn Dept Rawalpindi Pakistan Riphah Int Univ Comp Sci Dept Islamabad Pakistan Capital Univ Sci & Tech Elect Engn Dept Islamabad Pakistan

ISBN: (纸本)9781538666142

Wireless Mesh Networks (WMNs) have been considered as a relatively stable network paradigm among the other wireless technologies. However, the hardware based implementation of WMN makes it less flexible and difficult to manage technological modifications. To make it more flexible, WMN needs to be made programmable by adopting Software Defined networking (SDN) technique. Moreover, introducing SDN along with transition from conventional distributed to programmable centralized network may create economical, operational and deployment issues. To mitigate these challenges and to create seamless interoperability between SDN and IP network, SDNs may be deployed in a systematic incremental and hybrid operational way. In this paper, a comparative analysis of various hybrid routing schemes has been undertaken by addressing the challenges of load balancing, traffic engineering and mobility management being raised by topological dynamicity that WMNs experience. Considering the implementation frameworks used by the hybrid schemes, paper focuses towards the study of Topology based Hybrid Routing and Switch Architecture based Hybrid Routing, the latter modifies architecture of switch by adding SDN and IP routing schemes at same switch.

关键词： Control Plane Data Plane Hybrid Software Defined networking Wireless Mesh Networks

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OMOPredictor: An Online Multi-Step Operator Performance Prediction Framework in distributed Streaming Processing

OMOPredictor: An Online Multi-Step Operator Performance Pred...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Weimin Mu Zongze Jin Fan Liu Weilin Zhu Weiping Wang Chinese Academy of Sciences Institute of Information Engineering School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Recently, with the development of distributed stream processing systems, the elastic resource scaling technique has been significantly improved. Many researchers focus on leveraging the approaches based on predicting the trend of data load to implement the elastic scaling. However, the existing predicting methods cannot track and predict the fluctuation of performance online accurately, and they need to utilize more dimensions of the raw data and resources to enhance the performance of prediction. To address these issues, we propose a framework named OMOPredictor to make an accurate prediction of operator performance online. The experimental results show that OMOPredictor can enhance the prediction of the operator performance on three real-world datasets.

关键词： Task analysis Prediction algorithms Kernel Predictive models Neural networks Performance evaluation

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