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

共 30762 条记录，以下是631-640 订阅

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Fault-Tolerant Deep Learning Cache with Hash Ring for Load Balancing in HPC systems

Fault-Tolerant Deep Learning Cache with Hash Ring for Load B...

引用

2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Lee, Seoyeong Khan, Awais Kim, Yoochan Park, Junghwan Hwang, Soon Lee, Jae-Kook Hong, Taeyoung Zimmer, Christopher Kim, Youngjae Sogang University Korea Republic of Oak Ridge National Laboratory United States KISTI Korea Republic of

ISBN: (纸本)9798350355543

Large-scale DL on HPC systems like Frontier and Summit uses distributed node-local caching to address scalability and performance challenges. However, as these systems grow more complex, the risk of node failures increases, and current caching approaches lack fault tolerance, jeopardizing large-scale training jobs. We analyzed six months of SLURM job logs from Frontier and found that over 30% of jobs failed after an average of 75 minutes. To address this, we propose fault-tolerance strategies that recache data lost from failed nodes using a hash ring technique for balanced data recaching in the distributed node-local caching, reducing reliance on the PFS. Our extensive evaluations on Frontier showed that the hash ring-based recaching approach reduced training time by approximately 25% compared to the approach that redirects I/O to the PFS after node failures and demonstrated effective load balancing of training data across nodes. © 2024 ieee.

关键词： distributed Deep Learning Fault Tolerance HPC NVMe Cache

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The Influence-Maximizing Node Selection Algorithm Based on Local Isolated Centrality in Large-scale networks Implemented by distributed Graph computing 24

The Influence-Maximizing Node Selection Algorithm Based on L...

引用

24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Chen, Piaoyang Lei, Rong Deng, Mingsen School of Information Guizhou University of Finance and Economics Guiyang China

ISBN: (纸本)9798350319934

Identifying influence-maximizing (IM) nodes is an effective strategy for designing efficient communication networks or contain the spreading of information for rumors and epidemics with limited budgets. With the growing of the sizes of the social networks, finding solutions to the problem of intfuence - maximizing vertices becomes more and more challenging. Tra-ditionally, the number of users finally affected was the focus. However, the information diffusion speed was becoming a bottle-neck of IM problems. In this paper, we proposed a locality-aware vertex selection strategy based on local isolation centrality (LIC), while the information diffusion experiments used the Independent Cascade (IC) model. The computing scheme was implemented by a distributed graph computing framework. The superiority of the LIC algorithm was confirmed by evaluated on three real-world datasets. It was indistinguishable from algorithms such as K-shell, H-index, degree centrality (DC), local centrality (LC), and VoteRank at the diffusion cascade scale. But it showed better superiority in diffusion speed. More importantly, by comparing LIC with the VoteRank algorithm, which was the closest algorithm on diffusion speed to that of LIC, the huge advantage of the LIC algorithm in terms of time cost was found. We believe that the locality-aware LIC algorithm was more suitable for the influence-maximization problem in very large-scale social networks. © 2022 ieee.

关键词： Bottles

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Research on Autonomous Control Strategy for Safe Operation of High Proportion distributed PV Stations 6

Research on Autonomous Control Strategy for Safe Operation o...

引用

6th ieee international conference on Power, Intelligent computing and systems, ICPICS 2024

作者： Li, Guowu Li, Weiyu Yi, Shuxian Liu, Shen Ma, Xinsheng Liu, Zhenyu Pei, Xiaoliang State Grid Jibei Electric Power Company Limited Distribution Network Management Department Beijing China Electric Power Research Institute State Grid Jibei Electric Power Company Limited Beijing China Qingdao Dingxin Communication Co. Ltd Beijing-Tianjin-Hebei Marketing Service Center Shandong Qingdao China

ISBN: (纸本)9798350374315

In recent years, with the progress of distributed photovoltaic technology and the reduction of costs, the installed scale of distributed photovoltaic has grown rapidly. Due to the high average photovoltaic permeability, there is active power backfeed phenomenon in the distribution network at the peak of photovoltaic power generation, resulting in high voltage and distribution overload problems in the station area, especially the serious overvoltage of photovoltaic access points, which has seriously affected the reliable operation of the distribution network and the safe consumption of electricity by users. At present, there is little attention to the regulation of distributed PV access point overvoltage at home and abroad, and the related work has just started, and the relevant achievements are also few, so it is urgent to carry out the relevant research on the autonomous control strategy for the safe operation of distributed power station area. This paper proposes an autonomous control strategy for safe operation of distributed power stations. Firstly, the hardware architecture of 'distribution automation master station-intelligent fusion terminal-intelligent grid-connected switch/plug and play communication unit-photovoltaic inverter' is constructed. Then, based on the above hardware architecture, a refined control strategy is proposed. Finally, based on RTDS, a semi-physical simulation platform of intelligent fusion terminal is built, and the effectiveness and practicability of the method described in this paper are verified by a practical example. © 2024 ieee.

关键词： autonomous region distributed power supply Photovoltaic control Safe operation Semi-physical simulation

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An Automated Framework for distributed Deep Learning-A Tool Demo 42

An Automated Framework for Distributed Deep Learning-A Tool ...

引用

42nd ieee international conference on distributed computing systems (ICDCS)

作者： Gharibi, Gharib Patel, Ravi Khan, Anissa Gilkalaye, Babak Poorebrahim Vepakomma, Praneeth Raskar, Ramesh Penrod, Steve Storm, Greg Das, Riddhiman TripleBlind Kansas City MO 64112 USA MIT Media Lab 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Split learning (SL) is a distributed deep-learning approach that enables individual data owners to train a shared model over their joint data without exchanging it with one another. SL has been the subject of much research in recent years, leading to the development of several versions for facilitating distributed learning. However, the majority of this work mainly focuses on optimizing the training process while largely ignoring the design and implementation of practical tool support. To fill this gap, we present our automated software framework for training deep neural networks from decentralized data based on our extended version of SL, termed Blind Learning. Specifically, we shed light on the underlying optimization algorithm, explain the design and implementation details of our framework, and present our preliminary evaluation results. We demonstrate that Blind Learning is 65% more computationally efficient than SL and can produce better performing models. Moreover, we show that running the same job in our framework is at least 4.5 x faster than PySyft. Our goal is to spur the development of proper tool support for distributed deep learning.

关键词： Privacy-preserving machine learning distributed learning deep learning split learning

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Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in distributed Learning Environments 29

Weighted Sampled Split Learning (WSSL): Balancing Privacy, R...

引用

29th ieee international conference on Parallel and distributed systems, ICPADS 2023

作者： Osti, Manish Thakuri, Aashray Qolomany, Basheer Mulahuwaish, Aos Saginaw Valley State University University Center Department of Computer Science and Information Systems MI United States University of Michigan - Dearborn College of Engineering and Computer Science Dearborn United States University of Nebraska at Kearney Cyber Systems Department Kearney United States

ISBN: (纸本)9798350330717

This study presents Weighted Sampled Split Learning (WSSL), an innovative framework tailored to bolster privacy, robustness, and fairness in distributed machine learning systems. Unlike traditional approaches, WSSL disperses the learning process among multiple clients, thereby safeguarding data confidentiality. Central to WSSL's efficacy is its utilization of weighted sampling. This approach ensures equitable learning by tactically selecting influential clients based on their contributions. Our evaluation of WSSL spanned various client configurations and employed two distinct datasets: Human Gait sensor and CIFAR-10. We observed three primary benefits: heightened model accuracy, enhanced robustness, and maintained fairness across diverse client compositions. Notably, our distributed frameworks consistently surpassed centralized counterparts, registering accuracy peaks of 82.63% and 75.51% for the Human Gait sensor and CIFAR-10 datasets, respectively. These figures contrast with the top accuracies of 81.12% and 58.60% achieved by centralized systems. Collectively, our findings champion WSSL as a potent and scalable successor to conventional centralized learning, marking it as a pivotal stride forward in privacy-focused, resilient, and impartial distributed machine learning. © 2023 ieee.

关键词： and distributed Learning Privacy-Preserving Machine Learning Robust Learning Unbiased Learning Weighted Sampled Split Learning (WSSL)

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DKWS: A distributed System for Keyword Search on Massive Graphs (Extended Abstract) 40

DKWS: A Distributed System for Keyword Search on Massive Gra...

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40th ieee international conference on Data Engineering, ICDE 2024

作者： Jiang, Jiaxin Choi, Byron Huang, Xin Xu, Jianliang Bhowmick, Sourav S School of Computing National University of Singapore Singapore Hong Kong Baptist University Department of Computer Science Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350317152

Addressing the complexities of querying unstructured graphs such as knowledge graphs and social networks, this paper introduces D KWS, a novel distributed keyword search system. Leveraging a monotonic property, we ensure correct parallelization of our advanced keyword search algorithm, which incorporates tight pruning bounds and is divided into monotonic backward and forward search phases. The system is further augmented by the notify-push paradigm and the PINE programming model, facilitating asynchronous communication and preemptive searches to mitigate staleness in distributed environments. Extensive experiments on real-world datasets demonstrate DKWS's performance advantage, being up to two orders of magnitude faster and incurring 7.6 times lower communication costs than the existing systems. © 2024 ieee.

关键词： Search engines

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A Wireless sensor Network Integrated with Edge computing for Real-Time Crop Monitoring and Automated Irrigation 18

A Wireless Sensor Network Integrated with Edge Computing for...

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18th international conference on Telecommunication systems, Services, and Applications, TSSA 2024

作者： Garma, Angeline D. Cruz, Febus Reidj G. School of Eece Mapúa University Manila Philippines

ISBN: (纸本)9798331527686

Cloud-based smart agriculture systems struggle with real-time processing and connectivity in remote areas. This study integrates edge computing with WSNs to create a real-time crop monitoring and auto-irrigation system. The edge node handles RF communication with sensor and actuator nodes and connects to a LoRaWAN gateway provided by Packetworx using an SX1276 LoRa chip at 868 MHz. This design allows efficient data collection, local processing, and automated control without needing constant internet access. Implemented in Rosario, Batangas, Philippines, the system monitors soil moisture, NPK levels, temperature, and humidity, and controls irrigation via five solenoid valves. Each node operates for about 10.3 hours, extended by solar panels. Connectivity tests indicate better signal quality with higher gateways and increased packet loss at greater distances. © 2024 ieee.

关键词： Solenoid valves

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Real-Time Sentiment Analysis Pipeline for Yelp Customer Feedback using ChatGPT’s LLM and distributed systems 6

Real-Time Sentiment Analysis Pipeline for Yelp Customer Feed...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Revathy, S. Shahid, J. Mohammad Salih, S. Computer Science and Engineering St.Joseph’s Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

This work proposes a real-time sentiment analysis pipeline on customer feedback using Yelp and addresses the high-volume dynamic user-generated contents processing problem. The proposal integrates state-of-the-art machine learning models and distributed systems, making use of Large Language Models within a Docker container called ChatGPT for the real-time sentiments analysis. The system ingests live Yelp data streams through TCP/IP, processes them in a scalable Apache Spark cluster, and ensures that data flows seamlessly through Apache Kafka. Enriched feedback is indexed into Elasticsearch, providing powerful analytics and visualizations through tools like Kibana, Tableau, and Power BI. The low-latency design of the pipeline gives enterprises actionable insights for real-time decision-making, enabling enhanced customer sentiment analysis and business intelligence. Scalable and efficient, this architecture fits all large volume Enterprise Feedback Management (EFM) needs when looking to achieve insights fast from customer sentiment. © 2025 ieee.

关键词： Sales

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Spatially-Resolved Hyperlocal Weather Prediction and Anomaly Detection Using IoT sensor Networks and Machine Learning Techniques

Spatially-Resolved Hyperlocal Weather Prediction and Anomaly...

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2023 ieee international conference on Modelling, Simulation and Intelligent computing, MoSICom 2023

作者： Agarwal, Anita B. Rajesh, Rohit Arul, Nitin BITS Pilani Goa India

ISBN: (纸本)9798350393415

Accurate and timely hyperlocal weather predictions are essential for various applications, ranging from agriculture to disaster management. In this paper, we propose a novel approach that combines hyperlocal weather prediction and anomaly detection using IoT sensor networks and advanced machine learning techniques. Our approach leverages data from multiple spatially-distributed yet relatively close locations and IoT sensors to create high-resolution weather models capable of predicting short-Term, localized weather conditions such as temperature, pressure, and humidity. By monitoring changes in weather parameters across these locations, our system is able to enhance the spatial resolution of predictions and effectively detect anomalies in real-Time. Additionally, our system employs unsupervised learning algorithms to identify unusual weather patterns, providing timely alerts. Our findings indicate that this system has the potential to enhance *** weather prediction, Anomaly detection, IoT sensor networks, Machine learning, Spatial resolution, Weather monitoring © 2023 ieee.

关键词： sensor networks

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Passively Measuring IPFS Churn and Network Size 42

Passively Measuring IPFS Churn and Network Size

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42nd ieee international conference on distributed computing systems (ICDCS)

作者： Daniel, Erik Tschorsch, Florian Tech Univ Berlin Distributed Secur Infrastruct Berlin Germany

ISBN: (数字)9781665488792

ISBN: (纸本)9781665488792

The InterPlanetary File System (IPFS) is a popular decentralized peer-to-peer network for exchanging data. While there are many use cases for IPFS, the success of these use cases depends on the network. In this paper, we provide a passive measurement study of the IPFS network, investigating peer dynamics and curiosities of the network. With the help of our measurement, we estimate the network size and confirm the results of previous active measurement studies.

关键词： Network Measurement IPFS P2P Networks

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