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

共 1649 条记录，以下是1-10 订阅

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proceedings - 22nd IEEE/ACIS international conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing, SNPD 2021-Fall

Proceedings - 22nd IEEE/ACIS International Conference on Sof...

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22nd IEEE/ACIS international conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing, SNPD 2021-Fall

ISBN: (纸本)9781665404037

The proceedings contain 55 papers. The topics discussed include: human resource analysis based on used libraries in eclipse projects on GitHub;modified convolutional network for the identification of COVID-19 with a mobile system;which dependency was updated? exploring who changes dependencies in npm packages;developing event routing service to support context-aware service integration;interactive chatbots for software engineering: a case study of code reviewer recommendation;effectiveness of explaining a program to others in finding its bugs;study of microservice execution framework using spoken dialogue agents;association metrics between two continuous variables for software project data;structure-preserving deep autoencoder-based dimensionality reduction for data visualization;Arabic sign language recognition: towards a dual way communication system between deaf and non-deaf people;and an investigation on multiscale normalized deep scattering spectrum with deep residual network for acoustic scene classification.

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proceedings - 2nd international conference on networking and distributed computing, ICndC 2011

Proceedings - 2nd International Conference on Networking and...

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2nd international conference on networking and distributed computing, ICndC 2011

ISBN: (纸本)9780769544274

The proceedings contain 71 papers. The topics discussed include: smart-lab, LAN based application for effective lab supervision;an improved lexicographical sort algorithm of copy-move forgery detection;a steganography scheme based on fractal images;an improved genetic algorithm for 0-1 knapsack problems;a survey of botnet size measurement;efficiency evaluation method of simulation system based on BP neural network;research on errors of utilized bandwidth measured by netflow;design of an application model based on vertical search engine;a document-based information retrieval model vector space;license plate locating based on CRI reasoning scheme;large-scale storage and high-performance processing environment for remote sensor images;an integration of heterogeneous resources based on semantic grid;an effective data placement strategy in main-memory database cluster;and design of a fault-tolerant scheduling system for grid computing.

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Architectural Elements of Decentralized Process Management Systems 22nd

Architectural Elements of Decentralized Process Management S...

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22nd international conference on Service Oriented computing

作者： Grunert, Kai Shoferi, Janis Joderi Gold, Lucas Rieder, Wolf Kuepper, Axel Tech Univ Berlin Serv Ctr Networking Berlin Germany

ISBN: (纸本)9789819608041;9789819608058

In a decentralized Process Management System, several process engines cooperate to execute a single process instance by using direct Machine-to-Machine communication and local coordination of the process flow. In this paper, we analyze the software architecture elements of a decentralized Process Management System. We explain the involved components, connectors, data, and the relationships between them. We also describe the state transitions of decentralized processes during execution.

关键词： decentralized Process Management System distributed Process Engines distributed and Heterogeneous Workflow Enactment Service Business Process Management System Process Automation Process Execution System Process Engine

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TrustMesh: A Blockchain-Enabled Trusted distributed computing Framework for Open Heterogeneous IoT Environments 22

TrustMesh: A Blockchain-Enabled Trusted Distributed Computin...

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22nd IEEE international conference on Software Architecture, ICSA 2025

作者： Rangwala, Murtaza Buyya, Rajkumar Laboratory School of Computing and Information Systems Australia

ISBN: (纸本)9798331520908

The rapid evolution of Internet of Things (IoT) environments has created an urgent need for secure and trust-worthy distributed computing systems, particularly when dealing with heterogeneous devices and applications where centralized trust cannot be assumed. This paper proposes TrustMesh, a novel blockchain-enabled framework that addresses these challenges through a unique three-layer architecture combining permissioned blockchain technology with a novel multi-phase Practical Byzantine Fault Tolerance (PBFT) consensus protocol. The key innovation lies in TrustMesh's ability to support non-deterministic scheduling algorithms while maintaining Byzantine fault tolerance, features traditionally considered mutually exclusive in blockchain systems. The framework supports a sophisticated resource management approach that enables flexible scheduling decisions while preserving the security guarantees of blockchain-based verification. Our experimental evaluation using a real-world cold chain monitoring scenario demonstrates that TrustMesh successfully maintains Byzantine fault tolerance with fault detection latencies under 150 milliseconds, while maintaining consistent framework overhead across varying computational workloads even with network scaling. These results establish TrustMesh's effectiveness in balancing security, performance, and flexibility requirements in trustless IoT environments, advancing the state-of-the-art in secure distributed computing frameworks. © 2025 IEEE.

关键词： Edge computing

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Decentralized Disaster Management in Smart Cities: Leveraging IPFS, Blockchain, and Edge computing 22

Decentralized Disaster Management in Smart Cities: Leveragin...

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22nd IEEE Consumer Communications and networking conference, CCNC 2025

作者： Madhusudhan, R. Rithesh, S. National Institute of Technology Department of Mathematical and Computational Sciences Karnataka Surathkal India

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

As more and more smart cities come into existence, the necessity for foolproof disaster management solutions intensifies. This paper probes the challenges that smart cities face regarding disaster management and proposes a distributed framework leveraging a six-layer architecture involving Inter-Planetary File System (IPFS), blockchain technology, and edge computing to boost resilience and response capabilities. IPFS provides decentralized storage with interfacing, thus guaranteeing data integrity and availability during natural disasters. Blockchain technology helps in improving data transparency, ensuring tamper-proof updates, and elevating logistics through-put. Smart contracts facilitate efficient coordination and resource allocation. Edge computing enables real-time data processing, optimizing resource allocation, and enhancing the routing of physical resources to key locations for increased system efficiency. Connecting, investigating, aligning, and sharing information in a decentralized manner fosters teamwork among stakeholders. Community engagement and reporting tools are provided for smart city dwellers to participate in disaster management activities. The proposed system ensures a more resilient and faster disaster response. © 2025 IEEE.

关键词： blockchain technology communication community engagement data fusion data integrity decentralization disaster management edge computing governance InterPlanetary File System (IPFS) resilience smart cities smart contracts

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Not Best but Fair: Achieving a Fair Service Deployment Through Sky computing for Latency-Sensitive Applications 22nd

Not Best but Fair: Achieving a Fair Service Deployment Throu...

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22nd international conference on Service Oriented computing

作者： Shi, Weijia Zhao, Baokang Zhou, Huan Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9789819608072;9789819608089

Latency-sensitive applications, such as online gaming, require that the conditions of networks from the server to each client are almost the same to achieve fairness. Traditionally, the servers are always set fixedly in certain locations and only the nearby clients can connect, which is not fair to some far-away clients. To tackle the fairness issue, the emerging cloud computing or sky computing is a potential solution, where the server cannot be fixed anymore. Hence, we design LibraCloud, a novel server selection and allocation mechanism for latency-sensitive applications, using globally distributed cloud infrastructure. LibraCloud aims to select the fairest server rather than the best one. By defining a fairness indicator and dynamically utilizing multiple clouds, it overcomes the limitations of traditional fixed servers. To validate LibraCloud, we conduct experiments using 85 probes to simulate clients and 13 data centers for server selection. Assuming 20 distributed clients, 78.8% of the experimental cases show that LibraCloud selects the fairest server, significantly outperforming random selection or fixed three-server mechanisms.

关键词： Fair service deployment Sky computing Latency-sensitive applications Fairness

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One-shot Bottleneck Size Optimization in CNN for Split computing 22

One-shot Bottleneck Size Optimization in CNN for Split Compu...

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22nd IEEE Consumer Communications and networking conference, CCNC 2025

作者： Horikawa, Yutaro Nishio, Takayuki School of Engineering Institute of Science Tokyo Tokyo 152-8550 Japan

ISBN: (纸本)9798331508050

Split computing (SC) is a distributed inference method designed to balance computational load and reduce latency by partitioning a neural network into head and tail networks, deployed on a mobile device and a server, respectively. Due to the limited bandwidth, the data size at the split point must be reduced. For this reason, layers with small channel sizes, called bottlenecks, are often introduced in the model. However, the degree to which the channel size should be reduced depends on the overall structure of the model and the difficulty of the task, so a search is necessary. In this paper, we propose a training method that simultaneously reduces the number of bottleneck channels and optimizes model parameters for SC. This method introduces weights for the bottleneck layer into the loss function of training using cross-entropy. This makes the output value of each channel in the bottleneck layer closer to zero, and reduces the size of the transmitted data by filling in the values close to zero at the server side instead of sending them by the client during inference. © 2025 IEEE.

关键词： distributed inference Machine learning Regularization Split computing

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SLO-Aware Task Offloading Within Collaborative Vehicle Platoons 22nd

SLO-Aware Task Offloading Within Collaborative Vehicle Plato...

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22nd international conference on Service Oriented computing

作者： Sedlak, Boris Morichetta, Andrea Wang, Yuhao Fei, Yang Wang, Liang Dustdar, Schahram Qu, Xiaobo TU Wien Distributed Syst Grp Vienna Austria Tsinghua Univ Emerging Transportat Solut Beijing Peoples R China

ISBN: (纸本)9789819608072;9789819608089

In the context of autonomous vehicles (AVs), offloading is essential for guaranteeing the execution of perception tasks, e.g., mobile mapping or object detection. While existing work on offloading focused extensively on minimizing inter-vehicle networking latency, vehicle platoons (e.g., heavy-duty transport) present numerous other objectives, such as energy efficiency or data quality. To optimize these Service Level Objectives (SLOs) during operation, this work presents a purely Vehicle-to-Vehicle approach (V2V) for collaborative services offloading within a vehicle platoon. By training and using a Bayesian Network (BN), services can proactively decide to offload whenever this promises to improve platoon-wide SLO fulfillment;therefore, vehicles estimate how both sides would be impacted by offloading a service. In particular, this considers resource heterogeneity within the platoon to avoid overloading more restricted devices. We evaluate our approach in a physical setup, where vehicles in a platoon continuously (i.e., every 500 ms) interpret the SLOs of three perception services. Our probabilistic, predictive method shows promising results in handling large AV platoons;within seconds, it detects and resolves SLO violations through offloading.

关键词： Service Level Objectives Edge computing Intelligent Transportation Microservices Offloading Bayesian Networks

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Heuristic Data Placement in distributed Hierarchical Storage Network Using Deep Reinforcement Learning 22

Heuristic Data Placement in Distributed Hierarchical Storage...

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22nd IEEE Consumer Communications and networking conference, CCNC 2025

作者： Kaler, Ravi Toshniwal, Durga Indian Institute of Technology Roorkee Computer Science and Engineering Department Uttarakhand India

ISBN: (纸本)9798331508050

Intelligent data placement in hierarchical distributed storage networks (DSNs) has become crucial due to advancements in storage devices, an increase in big data applications, and strict time constraints. Inefficient data placement can lead to significant delays in data movement and increased service latency, impacting the overall performance of a distributed environment. It is a complex online decision-making problem due to its varying access patterns, dynamic network conditions, and ever-changing distributed environment, which traditional network optimization techniques fail to address efficiently. Reinforcement learning is best suited to address this problem as it learns from the environment dynamically and performs action. This paper presents a novel deep reinforcement learning (DRL) framework, soft actor-critic-2 (SAC-2), for heuristic data placement in the distributed network, leveraging the state-of-the-art SAC technique to improve efficiency, reduce latency, and subsequently minimize storage costs. We formulate the problem as a Markov Decision Process (MDP) model and incorporate a prioritized replay buffer for efficient learning. The heuristic data placement technique improves distributed hierarchical storage system efficiency compared to baseline strategies, as demonstrated by extensive experiments on Microsoft Research Cambridge (MSR) network traces. © 2025 IEEE.

关键词： Cloud computing Data Placement Deep Reinforcement Learning distributed Storage Network Management Resource Optimization

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Improving Semi-Supervised Federated Learning with Limited Labeled Data via Adaptive Batchsize and Pseudo Labeling 22

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

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22nd IEEE Consumer Communications and networking conference, CCNC 2025

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

ISBN: (纸本)9798331508050

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. © 2025 IEEE.

关键词： Federated Learning Mobile Edge computing Semi-Supervised Federated Learning Semi-Supervised Learning

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