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检索条件"任意字段=Proceedings of the 22nd International Conference on Distributed Computing and Networking"

共 1640 条记录，以下是131-140 订阅

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OneOS: A distributed operating system for the internet of things 21

OneOS: A distributed operating system for the internet of th...

引用

22nd international Middleware conference, Middleware 2021

作者： Jung, Kumseok University of British Columbia VancouverBC Canada

ISBN: (纸本)9781450391559

Existing Internet of Things (IoT) platforms introduce various framework-specific APIs for building user applications, which causes technology fragmentation within the IoT ecosystem and incur engineering costs. I seek to adopt standard POSIX APIs in an IoT platform, to provide an environment much like a distributed operating system (OS). Building a distributed OS for IoT systems poses several challenges due to the high degree of heterogeneity and dynamism, lack of application semantics, and novel security requirements. I propose a system called OneOS, which addresses the challenges of building a distributed OS for the Internet of Things. © 2021 ACM.

关键词： Edge computing

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Photonic computing and Communication for Neural Network Accelerators 22nd

Photonic Computing and Communication for Neural Network Acce...

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22nd international conference on Parallel and distributed computing, Applications and Technologies (PDCAT 2021)

作者： Xia, Chengpeng Chen, Yawen Zhang, Haibo Zhang, Hao Wu, Jigang Univ Otago Dept Comp Sci Dunedin New Zealand Guangdong Univ Technol Sch Comp Guangzhou Peoples R China

ISBN: (纸本)9783030967727;9783030967710

Conventional electronic Artificial Neural Networks (ANNs) accelerators focus on architecture design and numerical computation optimization to improve the training speed. Optical technology with low energy consumption and high transmission speed are expected to play an important role in the next generation of computing architectures. To provide a better understanding of optical technology used in ANN acceleration, we present a comprehensive review for the optical implementations of ANNs accelerator in this paper. We propose a classification of existing solutions which are categorized into optical computing acceleration and optical communication acceleration according to optical effects and optical architectures. Moreover, we discuss the challenges for these photonic neural network acceleration approaches to highlight the most promising future research opportunities in this field.

关键词： Optical neural networks Optical interconnection networks Neural network accelerator

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Collaborative Task Offloading for Vehicular Edge computing 22

Collaborative Task Offloading for Vehicular Edge Computing

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22nd IEEE international conference on Communication Technology, ICCT 2022

作者： Ming, Yujun Leng, Supeng School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665470674

Vehicular edge computing is a new paradigm that overcomes the constraints of physical distances of cloud servers on system performance in the Internet of Vehicles (IoV). In order to increase resource utilization and system scalability, we present in this paper a joint communication and computational resource allocation mechanism in VEC-enhanced IoV,where automobiles and VEC servers simultaneously work as computing service nodes. Given that the offloading and resource allocation strategy depends on the changing environment state, we formulate the problem as a Markov decision process to minimize the overall system overhead. We propose dynamically adapting distributed and centralized Deep Reinforcement Learning (DRL) in response to the various task requirements and the availability of free computing resources in the service nodes. Finally, simulation experiments are conducted to compare the performance differences among the centralized, multi-agent, and proposed algorithms. Numerical results verify that our proposed scheme outperforms the baselines. © 2022 IEEE.

关键词： Reinforcement learning

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Enhancing Intellectual Property Protection in Deep Neural Network with Confidential computing 19

Enhancing Intellectual Property Protection in Deep Neural Ne...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Tian, Wensheng Xia, Ruiyan Yan, Zhichao Tang, Panpan Tu, Yonggang Zhang, Lei Nanhu Lab Jiaxing China ShanghaiTech University Shanghai China Office of Government Services and Data Resource Management Jiaxing China

ISBN: (纸本)9798350346558

Deep neural network(DNN) models consume a lot of computational resources in the training process, and DNN models have become an important intellectual property. The methods for DNN models protection are currently mainly based on digital watermarking. The protection technique based on digital watermarking can only passively verify deep neural networks, and cannot restrict the illegal distribution and use of deep learning models. If the deep neural network model is deployed on a public cloud platform, the watermarking-based approach does not prevent malicious administrators from stealing the model. We propose a protection method based on the confidential computing, which divides the deep neural network model into a data preprocessing model and a inference model, we use a confidential computing environment to protect the data preprocessing model, and the inference model can be distributed to authorized users for deployment, even if malicious users get the inference model or train student models through knowledge distillation, they cannot obtain the highly accurate results from their models. It is also experimentally demonstrated that the method is also able to resist knowledge migration from the model using fine-tuning training. © 2022 IEEE.

关键词： Neural network models

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Matching Program Implementations and Heterogeneous computing Systems 22nd

Matching Program Implementations and Heterogeneous Computing...

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22nd international conference on Parallel and distributed computing, Applications and Technologies (PDCAT 2021)

作者： Sandrieser, Martin Benkner, Siegfried Univ Vienna Fac Comp Sci Res Grp Sci Comp Vienna Austria

ISBN: (纸本)9783030967727;9783030967710

High performance computing (HPC) systems have become highly parallel aggregations of heterogeneous system elements. Different kinds of processors, memory regions, interconnects and software resources constitute the modern HPC computing platform. This makes software development and efficient program execution a challenging task. Previously, we have developed a platform description framework for describing multiple aspects of computing platforms. It enables tools and users to better cope with the complexities of heterogeneous platforms in a programming model and system independent way. In this paper we present how our platform model can be used to describe program implementation variants that utilize different parallel programming models. We show that by matching platform models of program implementations to descriptions of a concrete heterogeneous system we can increase overall resource utilization. In addition, we show that our model featuring control relationships brings significant performance gains for finding platform patterns within a commonly used heterogeneous compute cluster configuration.

关键词： Modeling Platform Heterogeneous computing

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Multi-index Federated Aggregation Algorithm Based on Trusted Verification 22nd

Multi-index Federated Aggregation Algorithm Based on Trusted...

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22nd international conference on Parallel and distributed computing, Applications and Technologies (PDCAT 2021)

作者： Bao, Zhenshan Bai, Wei Zhang, Wenbo Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

ISBN: (纸本)9783030967727;9783030967710

Movited by the modern phenomenon of distributed data collected by edge devices at scale, federated learning can use the large amounts of training data from diverse users for better representation and generalization. To improve flexibility and scalability, we propose a new federated optimization algorithm, named as Multi-index federated aggregation algorithm based on trusted verfication(TVFedmul). TVFedmul is optimized based on Fedavg algorithm, which overcomes a series of problems caused by the original aggregation algorithm, which only takes the single index of data quantity as a reference factor to measure the aggregation weight of each client. The improved aggregation algorithm is based on multi-index measurement, which can reflect the comprehensive ability of clients more comprehensively, so as to make overall judgment. Further, we introduces hyperparameter alpha, which can be changed to determine the importance of the indexes. Finally, via extensive experimentation, the efficiency and effectiveness of the proposed algorithm is verified.

关键词： Federated learning Aggregation algorithm distributed learning

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Improving Semi-Supervised Federated Learning with Limited Labeled Data via Adaptive Batchsize and Pseudo Labeling

Improving Semi-Supervised Federated Learning with Limited La...

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Consumer Communications and networking conference, CCNC IEEE

作者： Byoungjun Park Pedro Porto Buarque de Gusmão Minhoe Kim Dept. of Computer and Information Science Korea University Sejong South Korea Computer Science Research Centre University of Surrey Guildford United Kingdom

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Federated learning (FL) is a distributed learning method that leverages numerous edge devices for training while protecting data privacy. However, most of the data produced by distributed edge devices are unlabeled, leading to the emergence of semi-supervised federated learning (SSFL) to address this issue. Although state-of-the-art approaches perform well when labeled data is sufficient, it shows severe performance degradation and training becomes more challenging as labeled data becomes scarce. In this paper, we improve performance in environments with limited labeled data by dynamically adjusting batch sizes and applying an Adaptive Threshold (AT). Additionally, we propose methods to resolve issues arising when adopting Adaptive Thresholds method of the centralized approach and investigate their limitations.

关键词： Performance evaluation Training Degradation Multi-access edge computing Federated learning Distance learning distributed databases Semisupervised learning Servers Labeling

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The Modified Approach to Internet of Things Data Transmission Based on a Combined Neural Network Autoencoder 22nd

The Modified Approach to Internet of Things Data Transmissio...

引用

22nd international Scientific and Practical conference Information and Communication Technologies and Sustainable Development, ICT and SD 2022

作者： Globa, Larysa Kurdecha, Vasyl Ushakov, Serhii National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Beresteiskyi Avenue 37 Kyiv03056 Ukraine

ISBN: (纸本)9783031468797

With the appearance of the Internet of Things (IoT) systems, the problem of processing and efficiently transmitting data has attracted increased attention due to the need to work in real-time. There are a lot of theoretical and practical researches in this direction but the critical remains data processing and transmission particular in real time mode. This paper addresses the challenges of data compression and transmission in the networks of the IoT, using DeepZip - a neural network-based model, distributed systems, and edge computing methodologies. Detailed research has been conducted on data compression through DeepZip and its application to IoT networks. The modified method of data compression in the IoT network using algorithms of a trained neural network and edge computing technologies is proposed, which combines the advantages of recurrent neural networks with the capabilities of the classical compression method, which made it possible to reduce the volume of data transmitted by the network. Practical data derived from real-world IoT networks have been employed to demonstrate the efficiency of these approaches. The data compression and transmission methodologies were optimized with DeepZip, distributed systems, and edge computing. This improves the operational efficiency of IoT networks. As such, this approach enables engineers to design and implement IoT systems with improved data handling capabilities. Application of the modified method made it possible to optimize data transfer and reduce network load. Software developed on the basis of a modified method is used in Internet of Things networks to process large volumes of data, including in real time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Edge computing

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Performance analysis of a distributed algorithm for admission control in wireless networks under the 2-hop interference model 21

Performance analysis of a distributed algorithm for admissio...

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22nd international conference on distributed computing and networking (ICDCN)

作者： Ganesan, Ashwin Int Sch Engn INSOFE Mumbai Maharashtra India

ISBN: (纸本)9781450389334

A general open problem in networking is: what are the fundamental limits to the performance that is achievable with some given amount of resources? More specifically, if each node in the network has information about only its 1-hop neighborhood, then what are the limits to performance? This problem is considered for wireless networks where each communication link has a minimum bandwidth quality-of-service (QoS) requirement. Links in the same vicinity contend for the shared wireless medium. The conflict graph captures which pairs of links interfere with each other and depends on the MAC protocol. In IEEE 802.11 MAC protocol-based networks, when communication between nodes i and j takes place, the neighbors of both i and j remain silent. This model of interference is called the 2-hop interference model because the distance in the network graph between any two links that can be simultaneously active is at least 2. In the admission control problem studied in the present paper, the objective is to determine, using only localized information, whether a given set of flow rates is feasible. In the present work, a distributed algorithm is proposed for this problem, where each node has information only about its 1-hop neighborhood. The worst-case performance of the distributed algorithm, i.e. the largest factor by which the performance of this distributed algorithm is away from that of an optimal, centralized algorithm, is analyzed. Lower and upper bounds on the suboptimality of the distributed algorithm are obtained, and both bounds are shown to be tight. The exact worst-case performance is obtained for some ring topologies. While distance-d distributed algorithms have been analyzed for the 1-hop interference model, an open problem in the literature is to extend these results to the K-hop interference model, and the present work initiates the generalization to the K-hop interference model.

关键词： Graph theory wireless ad hoc networks quality-of-service (QoS) media access (MAC) protocols distributed algorithms approximation algorithms admission control 2-hop interference model secondary interference model imperfection ratio

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Machine Learning-Based Scheduling and Resources Allocation in distributed computing 22nd

Machine Learning-Based Scheduling and Resources Allocation i...

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22nd Annual international conference on Computational Science (ICCS)

作者： Toporkov, Victor Yemelyanov, Dmitry Bulkhak, Artem Natl Res Univ MPEI Moscow Russia

ISBN: (纸本)9783031087608;9783031087592

In this work we study a promising approach for efficient online scheduling of job-flows in high performance and distributed parallel computing. The majority of job-flow optimization approaches, including backfilling and micro scheduling, require apriori knowledge of a full job queue to make the optimization decisions. In a more general scenario when user jobs are submitted individually, the resources selection and allocation should be performed immediately in the online mode. In this work we consider a neural network prototype model trained to perform online optimization decisions based on a known optimal solution. For this purpose, we designed MLAK algorithm which implements 0-1 knapsack problem based on the apriori unknown utility function. In a dedicated simulation experiments with different utility functions MLAK provides resources selection efficiency comparable to a classical greedy algorithm.

关键词： Resource Scheduling Online Knapsack Optimization Neural network Machine learning

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