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检索条件"任意字段=15th International Conference on Distributed Computing Systems"

共 5435 条记录，以下是51-60 订阅

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Split computing based Quantized PoseNet Model for distributed AI Architecture in IoT-Edge 15

Split Computing based Quantized PoseNet Model for Distribute...

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15th international conference on Communication systems and Networks (COMSNETS)

作者： Karjee, Jyotirmoy Naik, Praveen S. Anand, Kartik Byadgi, Chandrashekhar S. Dabbiru, Ramesh Babu Venkat Srinidhi, N. Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9781665477062

In recent times, Internet of things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & processing capabilities which makes it challenging to execute Deep Neural Network (DNN) models on them. To address this, we introduce Split computing technology, to partition DNN inference layers based on the computational capabilities (such as bandwidth, battery level and processing power, etc.) of IoT and edge (computationally powerful) devices, respectively. To validate split computing, we propose a framework called distributed Artificial Intelligence (DAI) architecture. We use the architecture for a fitness application (use-case) where we detect the pose of a person for our proposed Quantized Split PoseNet DNN (QSP-DNN) algorithm which partitions the DNN layers among IoT device and edge based on Wi-Fi bandwidth. We perform experiments to validate the QSP-DNN algorithm using DAI architecture. the QSP-DNN with DAI compares split execution (computed among IoT device & edge) for partial offload and full-offload executed on edge device. the result shows that using QSP-DNN in DAI architecture provides split execution performing 20.76 % improvement compared to the full offload case.

关键词： Performance evaluation Computational modeling Image edge detection Computer architecture Bandwidth Inference algorithms Partitioning algorithms

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A Framework for Evaluating Integration Testing Criteria in Serverless Applications 15

A Framework for Evaluating Integration Testing Criteria in S...

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15th international conference on Cloud computing and Services Science, CLOSER 2025

作者： Winzinger, Stefan Wirtz, Guido Distributed Systems Group University of Bamberg An der Weberei 5 Bamberg96047 Germany

ISBN: (纸本)9789897587474

Serverless applications are based on Function-as-a-Service (FaaS) platforms where serverless functions interact with other cloud-specific services. the integration of these components is crucial for the application’s functionality and must be adequately tested. Testing criteria can help here by supporting developers in evaluating test suites and identifying missing test cases. this paper presents a framework for evaluating integration testing criteria in serverless applications by showing the processes needed for evaluating the criteria. the process defined is shown for some control and data flow criteria and applied to some serverless applications showing the feasibility of the approach. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Coverage Criteria Data Flow FaaS Integration Testing Serverless computing

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distributed computing and Artificial Intelligence, Special Sessions, 15th international conference 1st ed. 2019

引用

丛书名： Advances in Intelligent systems and computing

2019年

作者： Sara Rodríguez Javier Prieto Pedro Faria Sławomir Kłos Alberto Fernández Santiago Mazuelas M. Dolores Jiménez-López María N. Moreno Elena M. Navarro

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Daedalus: Self-Adaptive Horizontal Autoscaling for Resource Efficiency of distributed Stream Processing systems 24

Daedalus: Self-Adaptive Horizontal Autoscaling for Resource ...

引用

15th ACM/SPEC international conference on Performance Engineering (ICPE)

作者： Pfister, Benjamin J. J. Scheinert, Dominik Geldenhuys, Morgan K. Kao, Odej Tech Univ Berlin Berlin Germany

ISBN: (纸本)9798400704444

distributed Stream Processing (DSP) systems are capable of processing large streams of unbounded data, offering high throughput and low latencies. To maintain a stable Quality of Service (QoS), these systems require a sufficient allocation of resources. At the same time, over-provisioning can result in wasted energy and high operating costs. therefore, to maximize resource utilization, autoscaling methods have been proposed that aim to efficiently match the resource allocation with the incoming workload. However, determining when and by how much to scale remains a significant challenge. Given the long-running nature of DSP jobs, scaling actions need to be executed at runtime, and to maintain a good QoS, they should be both accurate and infrequent. To address the challenges of autoscaling, the concept of self-adaptive systems is particularly fitting. these systems monitor themselves and their environment, adapting to changes with minimal need for expert involvement. this paper introduces Daedalus, a self-adaptive manager for autoscaling in DSP systems, which draws on the principles of self-adaption to address the challenge of efficient autoscaling. Daedalus monitors a running DSP job and builds performance models, aiming to predict the maximum processing capacity at different scaleouts. When combined with time series forecasting to predict future workloads, Daedalus proactively scales DSP jobs, optimizing for maximum throughput and minimizing both latencies and resource usage. We conducted experiments using Apache Flink and Kafka Streams to evaluate the performance of Daedalus against two state-of-the-art approaches. Daedalus was able to achieve comparable latencies while reducing resource usage by up to 71%.

关键词： distributed Stream Processing Autoscaling System Tuning Performance Modeling Resource Management Cloud computing

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A Study on the Performance of distributed Storage systems in Edge computing Environments 15

A Study on the Performance of Distributed Storage Systems in...

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15th IEEE international conference on Joint Cloud computing (JCC)

作者： Makris, Antonios Kontopoulos, Ioannis Xyalis, Stylianos Nektarios Psomakelis, Evangelos theodoropoulos, theodoros Varvarigos, Andreas Tserpes, Konstantinos Harokopio Univ Athens Dept Informat & Telemat Athens Greece Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece

ISBN: (纸本)9798350387339

Edge computing presents a promising paradigm for the management and processing of the vast volumes of data generated by Internet of things (IoT) devices. By merging cloud services with decentralized processing at the edge of the network, edge computing optimizes resource utilization while mitigating communication overhead and data transfer delays. Despite advancements, there are issues regarding cloud/edge-based application requirements. A distributed edge storage solution is crucial, ensuring data proximity, minimizing network congestion, and adapting to changing demands. Nevertheless, implementing or selecting an efficient edge-enabled storage system presents numerous challenges due to the distributed and heterogeneous nature of the edge, as well as its limited resource capabilities. Hence, it is essential for the research community to actively contribute towards clarifying the objectives and delineating the strengths and weaknesses of different storage solutions. this work presents an overview and performance analysis of three storage solutions in the edge computing context, namely MinIO, IPFS, and BigchainDB. the evaluation considers a set of Quality of Service (QoS) and resource utilization metrics. the systems are deployed on a cluster of four Raspberry Pis, which function as a network of edge devices. the results demonstrate the superiority of IPFS and provide insights into the performance of the evaluated storage systems for edge deployments.

关键词： edge storage solutions performance evaluation minio ipfs bigchaindb distributed databases

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Serverless Workflow Management on the computing Continuum: A Mini-Survey 15

Serverless Workflow Management on the Computing Continuum: A...

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15th ACM/SPEC international conference on Performance Engineering (ICPE)

作者： Farahani, Reza Loh, Frank Roman, Dumitru Prodan, Radu Alpen Adria Univ Klagenfurt Klagenfurt Austria Univ Wurzburg Wurzburg Germany Sintef Oslo Norway

ISBN: (纸本)9798400704451

the growing desire among application providers for a cost model based on pay-per-use, combined with the need for a seamlessly integrated platform to manage the complex workflows of their applications, has spurred the emergence of a promising computing paradigm known as serverless computing. Although serverless computing was initially considered for cloud environments, it has recently been extended to other layers of the computing continuum, i.e., edge and fog. this extension emphasizes that the proximity of computational resources to data sources can further reduce costs and improve performance and energy efficiency. However, orchestrating the computing continuum in complex applicationworkflows, including a set of serverless functions, introduces new challenges. this paper investigates the opportunities and challenges introduced by serverless computing for workflow management systems (WMS) on the computing continuum. In addition, the paper provides a taxonomy of state-of-the-art WMSs and reviews their capabilities.

关键词： Workflow WorkflowManagement systems (WMS) Serverless computing Function-as-a-Service (FaaS) Edge-Cloud Continuum Function Scheduling Service Orchestration Sustainability

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Enhanced Slicing based Deep Learning architecture for 5G networks in distributed Learning 15

Enhanced Slicing based Deep Learning architecture for 5G net...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dadhich, Atul Shankar, Amar Kumari, Aarsi Department of Electrical Engineering Vivekananda Global University Jaipur India Karnataka Bangalore India Department of Computer Science & IT ARKA JAIN University Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

this paper gives a distributed deep getting-to-know (DDL) architecture for 5G community cutting. DDL is an allotted method which has been a success in schooling massive-scale deep mastering fashions. It applies the ideas of disbursed computing to reduce the dimensions of the statistics being trained at the same time as education samples in parallel on different computing assets. the proposed solution in this paper exploits the improvements in 5G networks' virtualization skills, which include community cutting, to provide a green architecture which allows DDL fashions to be taught with dispensed resources to limit the training time. the proposed structure consists of three predominant modules: a useful resource-cutting manager, a dispensed training cluster, and a dispensed inference cluster. the resource-reducing manager allows dynamic aid cutting throughout numerous assets (e.g. GPU, CPU). the distributed schooling cluster includes workers distributing the training duties across the sources. the allotted inference cluster is used to perform inference on the trained fashions throughout more than one allotted computing source. the advantages of the proposed structure are that it allows green disbursed schooling, green disbursed inference, and value-green education of complex deep gaining knowledge of models. As such, it may be used to educate huge and complex models with decreased prices and also boost the schooling throughput. Additionally, 5G community cutting presents flexibility in useful resource provision. ©2024 IEEE.

关键词： 5G mobile communication systems

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distributed Crowdsensing Based on Mobile Personal Data Stores 15th

Distributed Crowdsensing Based on Mobile Personal Data Store...

引用

15th international conference on Ubiquitous computing and Ambient Intelligence

作者： Perez-Vereda, Alejandro Cabanero, Luis Moreno, Nathalie Hervas, Ramon Canal, Carlos Univ Castilla La Mancha Ciudad Real Spain Univ Malaga ITIS Software Malaga Spain

ISBN: (纸本)9783031485893;9783031485909

Users' personal information is one of the most important actives for nowadays enterprises. Knowing user preferences allows to offer personalized interactions and obtain more high-value information. In this context, crowdsensing shows as a technique that aims to collect information about the users and their Internet of Everything (IoE) environment. Personal smartphones are the devices that act as the interface between people and the IoE. However, most of the related works in the literature consider smartphones as mere sensors that gather user data, which is then transferred to the crowdsensing requester. As an alternative, in this paper we propose a distributed crowdsensing platform based on a extension of the Digital Avatars framework for Mobile Collaborative Social computing. In our proposal, smartphones are responsible for compiling and keeping the digital avatar or virtual profile of each of the users participating in the crowdsensing activity. Based on these avatars, the framework is extended to provide a distributed platform for both the dissemination and the aggregation of the results of the activity, granting users with privacy and ownership of their personal data. Our proposal also takes into account trust and user reputation by means of subjective logic. the proposed system is tested and validated through a proof of concept.

关键词： Crowdsensing distributed Crowdsensing Virtual Profiles Digital Avatars Personal Data Store

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An Investigation of Model Poisoning Dynamics in Federated Learning Scenarios 15

An Investigation of Model Poisoning Dynamics in Federated Le...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Megha, R. thampi, Abhijith A. Varma, Vanapala Bharath Chandra Sarath, Greeshma Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Federated learning was developed as a distributed learning paradigm to train machine learning models using data collected by individual devices without uploading it to a central server. FL safeguards user privacy because the training is conducted on distinct edge devices. Federated learning is susceptible to several attacks, though. Attackers may introduce several types of attacks while transmitting global model parameters and local changes. this study looks into the effects of several model poisoning attack kinds. We apply federated learning using a heterogeneous dataset, concentrating on model updates and adding controlled noise to mimic adversarial situations. the corruption ratios we implement here for analysis are 0.2,0.5, and 0.8. For every poisoning technique, accuracy is rigorously examined, revealing unique effects on model integrity. the results provide deep insights into the susceptibility of federated learning models to adversarial attacks, contributing to the ongoing discourse on the security of collaborative machine learning systems. © 2024 IEEE.

关键词： and Backdoor Attack Federated Learning Gaussian Noise Label Flipping Model Poisoning

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Refining Autonomous Network Agents in distributed and Cooperative Networks 15

Refining Autonomous Network Agents in Distributed and Cooper...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Deeplata Sevukamoorthy, Lakshmi Pandey, Arvind Kumar Vivekananda Global University Department of Chemistry Jaipur India School of Mangement - Pg Department of Management Karnataka Bangalore India Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

this paper proposes a unique approach to refining self-sufficient network retailers inside distributed and cooperative networks. A dispensed reinforcement gaining knowledge of community (DRLN) architecture is proposed to refine a prototype autonomous agent in a distributed cooperative community. the DRLN consists of a set of unbiased getting-to-know sellers that together form a disbursed reinforcement gaining knowledge of the device. Each agent is accountable for refining an unmarried agent in the network, and the dealers cooperate and trade revel with each other. this paper examines the effect of the usage of multiple dealers to refine the independent agents and evaluates its efficacy. Effects display that the proposed DRLN appreciably advanced the selection-making and coordination of the autonomous agents in comparison to the unrefined model. Moreover, the DRLN was located to be sturdy to small modifications in the community structure and substantially better the reliability of the community's autonomous behavior. © 2024 IEEE.

关键词： Accountable Behavior Community Efficacy Evaluates Network Refining Unrefined

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