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检索条件"任意字段=12th Euromicro Conference on Parallel, Distributed and Network-Based Processing"

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Probabilistic Management of Late Arrival of Events 18

Probabilistic Management of Late Arrival of Events

引用

12th ACM International conference on distributed and Event-based Systems (DEBS)

作者： Rivetti, Nicolo Zacheilas, Nikos Gal, Avigdor Kalogeraki, Vana Technion Israel Inst Technol Haifa Israel Athens Univ Econ & Business Athens Greece

ISBN: (纸本)9781450357821

In a networked world, events are transmitted from multiple distributed sources into CEP systems, where events are related to one another along multiple dimensions, e.g., temporal and spatial, to create complex events. the big data era brought with it an increase in the scale and frequency of event reporting. Internet of things adds another layer of complexity with multiple, continuously changing event sources, not all of which are perfectly reliable, often suffering from late arrivals. In this work we propose a probabilistic model to deal with the problem of reduced reliability of event arrival time. We use statistical theories to fit the distributions of inter-generation at the source and network delays per event type. Equipped with these distributions we propose a predictive method for determining whether an event belonging to a window has yet to arrive. Given some user-defined tolerance levels (on quality and timeliness), we propose an algorithm for dynamically determining the amount of time a complex event time-window should remain open. Using a thorough empirical analysis, we compare the proposed algorithm against state-of-the-art mechanisms for delayed arrival of events and show the superiority of our proposed method.

关键词： Complex Event processing Sliding Window Late arrivals Proba-bilistic Prediction

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A Blockchain-based Decentralized Data Storage and Access Framework for PingER 17

A Blockchain-based Decentralized Data Storage and Access Fra...

引用

17th IEEE International conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom) / 12th IEEE International conference on Big Data Science and Engineering (IEEE BigDataSE)

作者： Ali, Saqib Wang, Guojun White, Bebo Cottrell, Roger Leslie Guangzhou Univ Sch Comp Sci & Technol Guangzhou 510006 Guangdong Peoples R China Stanford Linear Accelerator Ctr POB 4349 Palo Alto CA 94309 USA Univ Agr Faisalabad Dept Comp Sci Faisalabad 38000 Pakistan

ISBN: (纸本)9781538643877

the blockchain is an innovative technology which opened doors to new applications for solving numerous problems in distributed environments. In this work, we design a blockchain-based data storage and access framework for PingER (worldwide end-to-end Internet performance measurement project) to remove its total dependence on a centralized repository. We use the permissioned blockchain and distributed Hash Tables (DHT) for this purpose. In the proposed framework, metadata of the files are stored on the blockchain whereas the actual files are stored off-chain through DHT at multiple locations using a peer-to-peer network of PingER Monitoring Agents. this will provide decentralized storage, distributed processing, and efficient lookup capabilities to the PingER framework.

关键词： Permissioned blockchain distributed ledger technology PingER Decentralized system

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Multimodal Complex Event processing on Mobile Devices 18

Multimodal Complex Event Processing on Mobile Devices

引用

12th ACM International conference on distributed and Event-based Systems (DEBS)

作者： Graubner, Pablo thelen, Christoph Koerber, Michael Sterz, Artur Salvaneschi, Guido Mezini, Mira Seeger, Bernhard Freisleben, Bernd Philipps Univ Marburg Marburg Germany Tech Univ Darmstadt Darmstadt Germany Tech Univ Darmstadt Dept Elect Engn & Informat Technol D-64293 Darmstadt Germany

ISBN: (纸本)9781450357821

Mobile devices are increasingly being used in edge and fog computing environments to process contextual data collected by sensors. Although complex event processing (CEP) is a suitable approach for realizing context-aware services on mobile devices in these environments, existing mobile CEP engines do not leverage the full potential of modern mobile hardware/software architectures. In this paper, we present multimodal CEP, a novel approach to process streams of events on-device in user space (user mode), in the operating system (kernel mode), on the Wi-Fi chip (Wi-Fi mode), and/or on a sensor hub (hub mode), providing significant improvements in terms of power consumption and throughput. Multimodal CEP automatically breaks up CEP queries and selects the most adequate execution mode for the involved CEP operators. Filter, aggregation, and correlation operators can be expressed in a high-level language without requiring system-level domain-specific knowledge. Multimodal CEP enables developers to efficiently detect user activities, collect environmental conditions, or interpret operating system and network events. Furthermore, it facilitates novel context-aware services, demonstrated by a use case for gathering and analyzing mobility data by Wi-Fi probe request tracking.

关键词： Mobile Device Complex Event processing Edge/Fog Computing

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Industry Paper: LoCoVolt: distributed Detection of Broken Meters in Smart Grids through Stream processing 12

Industry Paper: LoCoVolt: Distributed Detection of Broken Me...

引用

12th ACM International conference on distributed and Event-based Systems (DEBS)

作者： van Rooij, Joris Gulisano, Vincenzo Papatriantafilou, Marina Chalmers Univ Technol Gothenburg Sweden Goteborg Energi Gothenburg Sweden

ISBN: (纸本)9781450357821

Smart Grids and Advanced Metering Infrastructures are rapidly replacing traditional energy grids. the cumulative computational power of their IT devices, which can be leveraged to continuously monitor the state of the grid, is nonetheless vastly underused. this paper provides evidence of the potential of streaming analysis run at smart grid devices. We propose a structural component, which we name LoCoVolt (Local Comparison of Voltages), that is able to detect in a distributed fashion malfunctioning smart meters, which report erroneous information about the power quality. this is achieved by comparing the voltage readings of meters that, because of their proximity in the network, are expected to report readings following similar trends. Having this information can allow utilities to react promptly and thus increase timeliness, quality and safety of their services to society and, implicitly, their business value. As we show, based on our implementation on Apache Flink and the evaluation conducted with resource-constrained hardware (i.e., with capacity similar to that of hardware in smart grids) and data from a real-world network, the streaming paradigm can deliver efficient and effective monitoring tools and thus achieve the desired goals with almost no additional computational cost.

关键词： Data Streaming Advanced Metering Infrastructure Smart Meter

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Efficient ConvNet-based Object Detection for Unmanned Aerial Vehicles by Selective Tile processing 18

Efficient ConvNet-based Object Detection for Unmanned Aerial...

引用

12th International conference on distributed Smart Cameras (ICDSC)

作者： Plastiras, George Kyrkou, Christos theocharides, theocharis Univ Cyprus Dept Elect & Comp Engn KIOS Res & Innovat Ctr Excellence Nicosia Cyprus Univ Cyprus KIOS Res & Innovat Ctr Excellence Nicosia Cyprus

ISBN: (纸本)9781450365116

Many applications utilizing Unmanned Aerial Vehicles (UAVs) require the use of computer vision algorithms to analyze the information captured from their on-board camera. Recent advances in deep learning have made it possible to use single-shot Convolutional Neural network (CNN) detection algorithms that process the input image to detect various objects of interest. To keep the computational demands low these neural networks typically operate on small image sizes which, however, makes it difficult to detect small objects. this is further emphasized when considering UAVs equipped with cameras where due to the viewing range, objects tend to appear relatively small. this paper therefore, explores the trade-offs involved when maintaining the resolution of the objects of interest by extracting smaller patches (tiles) from the larger input image and processing them using a neural network. Specifically, we introduce an attention mechanism to focus on detecting objects only in some of the tiles and a memory mechanism to keep track of information for tiles that are not processed. through the analysis of different methods and experiments we show that by carefully selecting which tiles to process we can considerably improve the detection accuracy while maintaining comparable performance to CNNs that resize and process a single image which makes the proposed approach suitable for UAV applications.

关键词： Object Detection Convolutional Neural networks Aerial Cameras Pedestrian Detection

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GPU-Accelerated Differential Dependency network Analysis

GPU-Accelerated Differential Dependency Network Analysis

引用

euromicro conference on parallel, distributed and network-based processing

作者： Gil Speyer Juan Rodriguez Tomas Bencomo Seungchan Kim The Translational Genomics Research Institute Phoenix AZ U.S.A

EDDY (Evaluation of Differential DependencY) interrogates transcriptomic data to identify differential genetic dependencies within a biological pathway. through its probabilistic framework with resampling and permutation, aided by the incorporation of annotated gene sets, EDDY demonstrated superior sensitivity to other methods. However, this statistical rigor incurs considerable computational cost, limiting its application to larger datasets. the ample and independent computation coupled with manageable memory footprint positioned EDDY as a strong candidate for graphical processing unit (GPU) implementation. Custom kernels decompose the independence test loop, network construction, network enumeration, and Bayesian network scoring to accelerate the computation. GPU-accelerated EDDY consistently exhibits two orders of magnitude in performance enhancement, allowing the statistical rigor of the EDDY algorithm to be applied to larger datasets.

关键词： Graphics processing units Kernel Java Runtime Genomics Bioinformatics

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O-TF and O-FTF, Optical Fault-Tolerant DCNs

O-TF and O-FTF, Optical Fault-Tolerant DCNs

引用

euromicro International conference on parallel, distributed and network-based processing

作者： Saeedeh Akbari Rokn Abadi Somayyeh Koohi Sharif Univ. of Technol Tehran Iran Sharif University of Technology Tehran Iran

Performance of a data center is a function of three features;bandwidth, latency, and reliability. By adopting optical technology in data center network, bandwidth increment, in addition to reduction of transmission latency and power consumption, is achieved. Unfortunately, fault tolerance of the optical networks has raised less attention so far. So in this paper, we propose a fault-tolerant, scalable, and high-performance optical architecture built upon previously-proposed O-TF network, with the goal of redundancy optimization and reducing the minimum number of wavelength channels required for non-blocking functionality of the network. Moreover, reducing network diameter, in O-FTF network compared to O-TF and Wavecube networks, has led to higher network performance at the presence of network failures.

关键词： Optical interconnection network Data Center Fault-tolerant Non-blocking High-performance

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Studying Victim Caches in GPUs

Studying Victim Caches in GPUs

引用

euromicro conference on parallel, distributed and network-based processing

作者： Eric M. Taylor Daniel W. Chang Rose Hulman Institute of Technology Terre Haute IN US

Today Graphics processing Units (GPUs) are being used for more than traditional graphics processing. Large super computers such as Titan are utilizing GPUs to solve problems that have little resemblance to their original purpose. To improve the performance of these applications, GPU architects are increasing cache sizes to lower latencies of non-uniform memory references found in these programs. In this paper, we investigate an alternative approach where a victim buffer is added to the first level cache. Our studies show that a 256-line victim cache can increase L1 hit rate by 15% and improve IPC by 7.5% over the baseline. this victim cache outperforms increasing the cache size by 400% while being a less costly solution in terms of area.

关键词： Graphics processing units Benchmark testing Heterogeneous networks Hardware Memory management Computational modeling

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MinVisited: A Message Routing Protocol for Delay Tolerant network

MinVisited: A Message Routing Protocol for Delay Tolerant Ne...

引用

euromicro International conference on parallel, distributed and network-based processing

作者： Luis Veas-Castillo Gabriel Ovando-Leon Veronica Gil-Costa Mauricio Marin Universidad de Santiago de Chile Santiago Chile CONICET UNSL San Luis Argentina CeBiB Universidad de Santiago de Chile Santiago Chile

We studied routing protocols for Delay Tolerant networks devised to improve the message delivery performance in natural disaster scenarios. In this paper we propose the Min-Visited protocol which during the transitive path to the message destination, selects the next node based on two features: (1) the most distant neighbor, and (2) the largest number of encounters with the destination node of the message. We compare our protocol with well-known protocols of the technical literature. the results show that the proposed protocol presents a low workload overhead with a number of hops lower than 2, and in average 95% of the messages are successfully delivered.

关键词： Delay Tolerant networks the ONE simulator

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Predicting the Price of Bitcoin Using Machine Learning

Predicting the Price of Bitcoin Using Machine Learning

引用

euromicro International conference on parallel, distributed and network-based processing

作者： Sean McNally Jason Roche Simon Caton National College of Ireland School of Computing Ireland Dublin Business School Ireland

the goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD can be predicted. the price data is sourced from the Bitcoin Price Index. the task is achieved with varying degrees of success through the implementation of a Bayesian optimised recurrent neural network (RNN) and a Long Short Term Memory (LSTM) network. the LSTM achieves the highest classification accuracy of 52% and a RMSE of 8%. the popular ARIMA model for time series forecasting is implemented as a comparison to the deep learning models. As expected, the non-linear deep learning methods outperform the ARIMA forecast which performs poorly. Finally, both deep learning models are benchmarked on both a GPU and a CPU with the training time on the GPU outperforming the CPU implementation by 67.7%.

关键词： Bitcoin Deep Learning GPU Recurrent Neural network Long Short Term Memory ARIMA

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