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

共 149804 条记录，以下是4501-4510 订阅

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Tutorial: Consensus Algorithms from Classical to Blockchain: Quickly Program, Configure, Run, and Check

Tutorial: Consensus Algorithms from Classical to Blockchain:...

引用

international conference on distributed computing systems Workshop

作者： Yanhong A. Liu Scott D. Stoller Stony Brook University

ISBN: (数字)9798350354713

ISBN: (纸本)9798350354720

This tutorial examines well-known algorithms for distributed consensus problems, from classical consensus to blockchain consensus. We discuss exact algorithms that are high-level as in pseudocode and directly executable as programs at the same time, focusing on how to quickly program, configure, run, and check these algorithms as well as distributed algorithms and systems in general.

关键词： conferences Focusing Tutorials Consensus algorithm Blockchains distributed computing distributed algorithms

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Using Virtualization Approaches to Solve Deep Learning Problems in Voluntary distributed computing Projects 9th

Using Virtualization Approaches to Solve Deep Learning Probl...

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9th Russian Supercomputing Days international conference, RuSCDays 2023

作者： Kurochkin, Ilya Papanov, Valeriy Institute for Information Transmission Problems of Russian Academy of Sciences Moscow Russia The National University of Science and Technology MISIS Moscow Russia

ISBN: (纸本)9783031494345

The task of training deep neural networks on a large amount of data requires a lot of resources. The solution of such a problem is often impossible to carry out on one computing device in an adequate time. distributed computing systems can be used to solve deep learning problems. Such systems may consist of heterogeneous computing nodes with different computing power. To implement deep learning on a distributed heterogeneous system, it is necessary to solve the problem of utilization of all available resources. The solution to this problem is to configure the task delivery system of a distributed system. And to expand the number of computing nodes involved, it is necessary to use virtualization. The article discusses two types of virtualization for grid systems when solving deep learning problems. The features of the implementation of computational applications for training deep neural networks for solving the problem of image classification are discussed. The results of distributed deep learning on a public grid system are discussed. A comparative analysis of two virtualization approaches is given. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Virtualization

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Event-Driven Approach for Monitoring and Orchestration of Cloud and Edge-Enabled IoT systems 15

Event-Driven Approach for Monitoring and Orchestration of Cl...

引用

15th ieee international conference on Cloud computing (ieee CLOUD) / ieee World Congress on Services (ieee SERVICES)

作者： Mouine, Mohamed Saied, Mohamed Aymen Laval Univ Quebec City PQ Canada

ISBN: (数字)9781665481373

ISBN: (纸本)9781665481373

The Internet of Things (IoT) has greatly benefited the technological advances of a variety of fields, such as manufacturing and medicine, to name a few. The context surrounding these use cases is, however, often widely different from conventional Cloud computing and web applications. Cyberphysical environments present us with major concerns and constraints surrounding the resilience of systems, which often rely on critical infrastructure and important workloads to prevent major losses for businesses or even the endangerment of individuals. The supervision of these infrastructures, outside the controlled and relatively safe environment of a datacenter, is therefore one of the major considerations for modern IoT systems. In this paper, we evaluate the core concepts around this thesis and propose an architectural and conceptual approach to improve the monitoring, scalability, and orchestration of IoT systems. We leverage and integrate different solutions inspired by modern IoT practices and the cloud ecosystem to optimize both software and hardware aspects. The solution revolves around an Edge computing approach, Event-driven communication (MQTT) in the Edge, the orchestration of containerized services using Kubernetes and KubeEdge, and Device Twins for the management of physical components. Through development, experiment, and evaluation, we propose an architecture and two complementary fault-tolerance strategies to address synchronization between cloud and edge components and improve the overall resilience of the system.

关键词： edge computing IoT kubernetes scalability event-driven software architecture fault tolerance cloud computing

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Deep Reinforcement Learning for Distribution Power System Cyber-Resilience via distributed Energy Resource Control 14

Deep Reinforcement Learning for Distribution Power System Cy...

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14th ieee international conference on Communications, Control, and computing Technologies for Smart Grids, SmartGridComm 2023

作者： Corrado, Nicholas E. Livesay, Michael Bailey, Tyson Johnson, Jay Levin, Drew University of Wisconsin Department of Computer Sciences Madison United States Sandia National Laboratories United States

ISBN: (纸本)9781665455541

Interoperable, internet-connected distributed energy resource (DER) devices in power systems create a new attack vector for malicious cyber-actors. Modern control systems primarily focus on transmission and sub-transmission operations and rarely incorporate DER or other distribution-connected equipment. While recent advances have expanded grid operator visibility and control functionality to the distribution level, these control systems struggle to scale with thousands of networked devices. Thus, to defend against potential attacks on DER devices, we must develop new real-time control algorithms to ensure grid stability. In this work, we present a new approach to power distribution control based on deep reinforcement learning (RL) algorithms which can learn optimal control policies through experience. We evaluate four RL algorithms in novel voltage stabilization tasks based on the ieee 13-bus and EPRI Ckt5 power distribution models. We demonstrate that RL can successfully perform voltage regulation and outperform a greedy algorithm. Our results coupled with the established capability of RL to scale in high-dimensional settings opens a path towards real-time cyber defense of power systems with RL agents. © 2023 ieee.

关键词： Reinforcement learning

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Low Threshold Voltage MOSFET - A Potential Candidate for Biomedical IC Design

Low Threshold Voltage MOSFET - A Potential Candidate for Bio...

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2024 international conference on Advances in computing, Communication, Electrical, and Smart systems, iCACCESS 2024

作者： Emon, Md. Zubair Alam Chowdhury, Md. Iqbal Bahar United International University Department of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350350289

As the world is progressing, futuristic innovation demand is testing the limits of design related to analog circuits. Thus design with traditional MOSFET is becoming more complex. In this paper, we propose the use of low-threshold voltage (LVT) MOSFET instead of traditional MOSFET. Operational trans-conductance amplifiers were designed with both the traditional and LVT MOSFETs and a comparison was made. Further, a fifth order low pass filter with LVT MOSFETs was designed to show it's effectiveness in removing noise from ECG signal. The simulation was done in Cadence, an industry -grade software, with 45nm technology. The spectre simulation result shows that the low power filter produces a gain of 44.65 dB, a total harmonic distortion of -53.71 dB, an input-referred-noise of 130 μ Vrms/√Hz within a bandwidth of 246.9 Hz. © 2024 ieee.

关键词： Electrocardiograms

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Equitable Extended Reality via Cooperative NOMA in Decentralized Mobile Edge computing systems 58

Equitable Extended Reality via Cooperative NOMA in Decentral...

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58th Asilomar conference on Signals, systems and Computers, ACSSC 2024

作者： Reifert, Robert-Jeron Dahrouj, Hayssam Sezgin, Aydin Institute of Digital Communication Systems Ruhr University Bochum Germany University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350354058

Extended reality (XR) applications pose strict technical demands on future communication systems in terms of rapid connectivity and resource-intensive task computations. To this end, this paper addresses the benefits of an optimized mobile edge computing system using battery-limited edge computers (ECs) so as to meet the intricate requirements of future XR applications. The paper classifies well-connected and poorly-connected XR devices as strong and weak devices, respectively. Such platform is then provisioned through the use of a cooperative non-orthogonal multiple access (Co-NOMA) optimized scheme. With the aid of device cooperation, the paper illustrates how Co-NOMA improves the weak devices' performance by flexibly selecting either the direct or relay transmission link. More specifically, to achieve equitable XR services, the paper maximizes the minimum rate among all XR devices by jointly managing computation, communication, and link selection resources subject to power consumption, computational capacity, and delay constraints. The proposed solution then utilizes discrete relaxations, successive-convex approximation, and fractional programming techniques, and is shown to be amenable for distributed implementation across the multiple ECs. Simulations illustrate the proposed approach efficacy, especially in terms of minimum rate and delay performance in decentralized XR systems, as compared to several state-of-the-art methods. © 2024 ieee.

关键词： Mobile edge computing

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Comparing and Elucidating Blockchain Based Voting Mechanisms

Comparing and Elucidating Blockchain Based Voting Mechanisms

引用

2022 international conference on Sustainable computing and Data Communication systems, ICSCDS 2022

作者： Donepudi, Swapna Reddy, K Thammi Gitam Institute of Technology Gitam Department of Cse Visakhapatnam India

ISBN: (纸本)9781665478847

Block chain is a distributed ledger technology that stores data on several servers globally, and anyone on the authenticated network can see data in near real-time. Data stored on a Block chain is immutable, time-stamped, unanimous, decentralized, secure, irreversible, fast, and consensus-dependent. Due to these benifits many of the real life software applications are adopting to block chain technology. Online voting process demands all the advantages offered by block chain technology, because block chain uses type of distributed Ledger Technology that uses a cryptographic signature known as a hash to record transactions. There are numerous block chain based voting mechanicams/strategies have been proposed by various researchers. This paper aims at evaluating block chain based voting mechanisms versus a block chain voting strategy named 'Performance Driven Framework for Mass e-Voting' strategy proposed by the authors of this paper [1] using Hyperledger Caliper. This paper also discusses detail the advantages, disadvantages and future possible research directions. © 2022 ieee.

关键词： distributed ledger

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Fine-grained Calculation Method of Accepting distributed Generation Capacity in Distribution Network for Virtual Power Plant 6

Fine-grained Calculation Method of Accepting Distributed Gen...

引用

6th ieee international Electrical and Energy conference, CIEEC 2023

作者： Zhong, Yongjie Ji, Ling Jiang, Yanjun Li, Jingxia Wang, Zidong Wang, Yong Guodian Nanjing Automation Co. Ltd Nanjing211153 China

ISBN: (纸本)9798350346671

The fine-grained calculation method of accepting distributed generation capacity in distribution network for virtual power plant is proposed in this paper. Firstly, a distributed generation capacity model including photovoltaic power generation and wind power generation are established based on the collection of virtual power plant information. Secondly, distribution network power flow model on power grid side, and distributed power generation output model on power supply side including photovoltaic generation model and wind power generation model are established. Thirdly, the fine-grained safe operation constraints including power supply side and power grid side are set. Then, the optimal coordinated scheduling model of acceptability of new energy distributed generation with adjustable importance factor setting mode is proposed. Finally, output virtual power plant information through optimal coordinated scheduling, and reverse determine the actual photovoltaic and wind power installed capacity selection. The proposed model and method can provide theoretical guidance and reference for the fine modeling of distributed generation capacity accepted by the distribution network of virtual power plant, fine-grained computing application, actual installed capacity selection of distributed generation, multi-mode coordinated scheduling and so on. © 2023 ieee.

关键词： Virtual Power Plants

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Development Anonymous Authentication Maria et al.'s Scheme of VANETs Using Blockchain and Fog computing with QR Code Technique 10

Development Anonymous Authentication Maria et al.'s Scheme o...

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10th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Alzaidi, Zahraa Sh Yassin, Ali A. Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo Univ Basrah Coll Educ Pure Sci Dept Comp Sci Basrah 61004 Iraq Jaram Oginga Odinga Univ Sci & Technol Dept Comp Sci & Software Engn Bando 40601 Kenya SIMATS Dept Appl Elect Saveetha Sch Engn Chennai 600124 Tamil Nadu India

ISBN: (纸本)9798350373981;9798350373974

The evolution of intelligent transport systems (ITS) has significantly enhanced driving experiences in vehicular ad-hoc networks (VANET), crucial for improving road safety and traffic efficiency. Despite these advancements, challenges persist. Trust management solutions often assume user legitimacy without verification. Moreover, the need for reauthentication as vehicles move between RSUs adds computational complexity. VANET is relatively vulnerable to security threats from multiple parties given its highly dynamic topology and its wireless and heterogeneous communication mode. We have expanded upon Maria et al.'s schema by specifically targeting and resolving significant shortcomings in their research. More precisely, we improve the features related to confirming the identity of users, preventing vehicles from being used without authorization, and continuously monitoring their current location in order to strengthen the security of vehicles and increase user involvement. We suggest implementing a schema that utilizes QR code verification to authenticate vehicle users, specifically targeting these difficulties. In VANETs, blockchain has been adopted with the authentication process for fog computing. The result is that there is no need for trusted authority (TA) while achieving privacy within the blockchain network and ensuring of the vehicle user authentication and the integrity of messages. Several key benefits have been achieved, such as blocking malicious entities and the potential damage they cause, enabling the revocation of malicious vehicles by conditional anonymization, as well as simplifying conflict resolution. Likewise, the proposed work was characterized by achieving an effective balance between effectiveness and complexity, protecting privacy, conditional tracking, ensuring integrity, and anonymous authentication.

关键词： Vehicular ad hoc networks

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TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency 23

TriplePlay: Enhancing Federated Learning with CLIP for Non-I...

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23rd ieee international conference on Machine Learning and Applications, ICMLA 2024

作者： Imteaj, Ahmed Hossain, Md Zarif Zaman, Saika Shahid, Abdur R. School of Computing Southern Illinois University CarbondaleIL United States United States

ISBN: (纸本)9798350374889

The recent advancement of pretrained models shows great potential as well as challenges for privacy-preserving distributed machine learning technique called Federated Learning (FL). With the growing demands of foundation models, it is now an urgent need to explore the potential of such foundation models in a distributed setting. In this paper, In this paper, we delve into the complexities of leveraging foundation models, like CLIP into FL frameworks to preserve data privacy, and efficiently training distributed network clients across heterogeneous data landscapes. We specifically aim to address the issues related to non-IID data distributions, skewed class representation of FL clients' local dataset, communication overhead and high resource consumption due to large, complex model training in an FL setting. To address these, we propose TriplePlay, a framework that tailors CLIP foundation model as an adapter to strengthen FL model's performance and adaptability across heterogeneous data distributions among the clients. Besides, we address the long-tail distribution problem in an FL environment to maintain fairness and optimize the computational resource demands of the FL clients through quantization and low-rank adaptation techniques. A comprehensive simulations results with two distinct datasets and different FL settings demonstrate that TriplePlay efficiently reduces GPU usage and accelerates the convergence time that ultimately reduces the communication cost. © 2024 ieee.

关键词： Federated learning

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