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Improving Raft Performance with Bulk Transfers 11

Improving Raft Performance with Bulk Transfers

引用

11th international Symposium on computing and networking Workshops, CANDARW 2023

作者： Yamashita, Aoi Tanaka, Masahiro Bessho, Yutaro Fujiwara, Yasuhiro Kawashima, Hideyuki Graduate School of Keio University Japan Ntt Computer and Data Science Laboratories Japan Ntt Communication Science Laboratories Japan Keio University Japan

ISBN: (纸本)9798350306941

Raft is known as a replicating state machine that tolerates crash faults, and its use enables various distributed systems, including distributed transaction processing systems. the original Raft protocol creates an intra-cluster consensus by having the leader generate a log of each incoming client request and replicate it to its followers. When requests arrive frequently, the number of replications increases, increasing the number of communications and possibly leading to performance degradation. To solve this problem, we propose a bulk-transfer protocol that replicates multiple requests at once. We implemented the Raft system to verify the effect of the proposed method. We measured the performance of the implemented system when multiple requests were transferred in bulk and observed an increase in throughput with an increase in the amount of data replicated at a time. the highest throughput of 214 MB/sec was achieved when replicating 12.5 MB at a time. this is 12,066 times higher than the performance when replicating one request (18 B) at a time. Besides, the size of the log is reduced to 5/9 of the conventional method. © 2023 IEEE.

关键词： distributed computer systems

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Cloud Orchestration for Optimized computing Efficiency: the Case of Wind Resource Modelling 11

Cloud Orchestration for Optimized Computing Efficiency: The ...

引用

11th IEEE international conference on Cloud networking (CloudNet)

作者： Touloupou, Marios Kapassa, Evgenia Rizou, Stamatia Singular Logic SA Europen Projects Dept Athens Greece Univ Nicosia Dept Digital Innovat Nicosia Cyprus

ISBN: (数字)9781665486279

ISBN: (纸本)9781665486279

the Weather Research and Forecasting (WRF) model is a multi-purpose open source weather model, which is widely used by the wind energy community. However, the state of the art applications that use the WRF model for wind resources do not always meet the sector's needs. the fundamental challenge is the massive quantity of data required for accurate modelling, as well as the expenses associated with the computing of such massive volumes of data, implying that state-of-the-art approaches are not appropriate. Our work tackles this issue with the use of a novel orchestration framework relying on cloud computing infrastructure. Cloud computing platforms provide robust infrastructure that allows for the deployment of production-level services as well as extended processing and storage capacity. this paper presents the overall software architecture of a Cloud Orchestration System, enabling the dynamic and flexible cloud deployment of containerized wind resource model chains. Moreover, a set of performance, functional and operational criteria are presented and evaluated, using a real use case of a wind resource model.

关键词： cloud orchestration optimal deployment computing efficiency wind resources

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distributed federated and incremental learning for electric vehicles model development in Kafka-ML 11

Distributed federated and incremental learning for electric ...

引用

11th international conference on Wireless Networks and Mobile Communications (WINCOM) - Inclusive and Intelligent Connectivity

作者： Carnero, Alejandro Waqar, Omer Martin, Cristian Diaz, Manuel Univ Malaga ITIS Software Malaga Spain Univ Fraser Valley Abbotsford BC Canada

ISBN: (纸本)9798350377873;9798350377866

With the increasing development and deployment of new systems for efficient and clean mobility, Electric Vehicles (EVs) are becoming more and more common among people. those produce large amounts of data streams that need to be collected and analyzed to understand user needs and improve their performance. For this purpose, Artificial Intelligence (AI) techniques are playing a very important role. Within this context, Kafka-ML is a Machine Learning (ML) framework that enables the consumption and processing of data streams and allows the flexible management and deployment of neural networks throughout their entire life cycle. Kafka-ML can work with distributed Neural Networks (DNN) which reduce latency and response times, perform incremental training over time allowing models to adapt to data on the fly, and carry out Federated Learning (FL) processes for this type of algorithms so a more robust global model can be created while maintaining data privacy and security, but all this separately. this work has considered the joint implementation of FL, for anonymous data sharing, incremental learning for continuous training of the models, and DNN for distribution of the models across different points on the map. All this applied within a Vehicleto-everything (V2X) domain where EV usage and charge data can be shared to improve the user experience, as well as to better understand the behavior of this type of vehicles and their charging points to achieve savings, and how it affects people daily lives. An evaluation of the system related to this EV use case is presented to demonstrate the viability of the tool.

关键词： electric vehicles federated incremental machine learning distributed neural networks data streams Kafka-ML

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Decision Algorithm for Computational Offloading in Vehicular Fog computing with Pedestrians 11

Decision Algorithm for Computational Offloading in Vehicular...

引用

11th IEEE international conference on Cloud networking (CloudNet)

作者： Rocha, Paulo Goncalves Souza, Alisson Silva, Francisco Airton Rego, Paulo A. L. Univ Fed Ceara Dept Comp Sci Fortaleza Brazil Univ Fed Ceara Quixada Brazil Univ Fed Piaui Picos Brazil

ISBN: (数字)9781665486279

ISBN: (纸本)9781665486279

With a more significant number of devices integrated and connected in a VANET (Vehicular Ad Hoc Networks), there is greater availability and variety of computing resources to run applications. In VANETs, VFC (Vehicular Fog computing) technology establishes vehicles, edge, and cloud as resource-providing infrastructures. However, the use of VFC as infrastructure for pedestrians is still limited, with few works addressing computational offloading in such a scenario. In this context, we implemented a decision algorithm for the offloading process based on resources provided by VFC to guarantee better offloading and latency rates. the results showed that the implemented algorithm obtained efficiency rates above 90% in the tested scenarios and a reduction of up to 40% in the offloading execution time compared to a random approach tested.

关键词： VANET VFC offloading pedestrian

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Explainable Deep Learning for Cyber Attack Detection in Electric Vehicle Charging Stations 24

Explainable Deep Learning for Cyber Attack Detection in Elec...

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11th international conference on networking, Systems and Security, NSysS 2024

作者： Rahman, Md Mizanur Hossain Chayan, Md Meheraz Mehrin, Khaleda Sultana, Afrin Hamed, Mosaab M. School of Systems and Computing University of New South Wales CanberraACT Australia Brac University Dhaka Bangladesh Bangladesh Bureau of Educational Information and Statistics Dhaka Bangladesh Bangladesh University of Professionals Dhaka Bangladesh

ISBN: (纸本)9798400711589

the growing integration of Electric Vehicles (EVs) into smart grids has led to significant cybersecurity challenges, particularly concerning vulnerabilities in Electric Vehicle Charging Systems (EVCS). these systems, which facilitate secure interactions among EVs, EVCS, and management systems through protocols such as ISO 15118 and OCPP, are increasingly exposed to cyber threats due to the rise of Internet of things (IoT) connectivity. threats such as denial of service, information gathering, man-in-the-middle, and injection attacks pose serious risks to the stability of charging stations and the overall infrastructure. this paper introduces a novel Deep Learning model to cyber attack detection utilizing the CIC EV Charger Attack Dataset 2024 (CICEVSE2024). Although the original study in this dataset reported an accuracy of 78.87%, our proposed deep learning model achieves a remarkable accuracy of 97.15%, with a precision of 97.19%, recall of 97.15%, F1 score of 97. 15% and an area under the curve (AUC) of 97. 14%. these results demonstrate the effectiveness of advanced deep learning techniques in enhancing the security of the EV charging infrastructure. the Shap explainable AI results revealed the significant impact of 12 selected features on detecting benign and attack data using our deep learning model. © 2024 Copyright held by the owner/author(s).

关键词： Charging stations

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Sentiment Analysis for Detection of Depressive Users on Social Networks 11

Sentiment Analysis for Detection of Depressive Users on Soci...

引用

11th IEEE international conference on Behavioural and Social computing, BESC 2024

作者： Wigell, Christopher Tao, Xiaohui Li, Lin Wang, Grace Y Zhang, Ji Sun, Yuan School of Mathematics Physics and Computing University of Southern Queensland Australia School of Computer Science and Artificial Intelligence Wuhan University of Technology China School of Psychology and Wellbeing University of Southern Queensland Australia NII Library Information and Society Research Division Japan

ISBN: (纸本)9798331531904

Depression affects approximately 121 million people worldwide and has as significant an influence on patients as it does their carers. Until recently, psychologists used paper based psychological batteries for screening. New developments in Natural Language Processing however have enabled real-time analysis, offering increases in scalability and accessibility. We built on current knowledge by developing and training several Centralised Neural Network models around a pre-labelled Reddit dataset, comparing post-length and the number of words used per post, selecting the model with the highest F1 score. Our results indicated that although some models performed better than others, neither of our metrics significantly influenced model performance, with changes explained by another factor. Our research was able to yield support for it's use within online forums at a very low cost;justifying further exploration into the use of AI tools for the screening of depression and other mood disorders. Due to a lack of publicly available natural language processing models, our study also resulted in the release of a model for further research by others. © 2024 IEEE.

关键词： Social networking (online)

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Design of a Scaled-Vehicle Research Platform for Software-Defined Vehicles

Design of a Scaled-Vehicle Research Platform for Software-De...

引用

11th international conference on Mechatronics and Robotics Engineering, ICMRE 2025

作者： Oh, Sung Bhin Do, Young Soo Lim, Se Jeong Choi, Hyeok Jun Cha, Sung Keun Hong, Hyeong Keun Lim, Seong Hyeon Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798331509293

Modern vehicles are integrated systems of complex software that support advanced features such as autonomous driving and over-the-air updates. Software-defined vehicles (SDVs) are emerging as a future automotive paradigm for managing increasing software complexity by centralizing computing resources and enabling flexible software updates and feature deployment. However, most SDV research is conducted in simulation environments because using real vehicles presents many challenges, including high cost, safety concerns, and implementation difficulties. In this paper, we present the design of an SDV research platform using a 1/5-scale vehicle model to overcome these limitations of SDV research. the platform implements a zone architecture and integrates various embedded systems to replicate the distributed computing environment of a modern vehicle. the platform allows researchers to experiment with different SDV concepts such as distributed computing, OTA updates, middleware, and vehicle software management without the constraints of full-scale vehicle testing. Our design demonstrates the feasibility of using extended vehicles for SDV research and provides insights into platform design for SDV research. © 2025 IEEE.

关键词： distributed cloud

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Experiences with Using Diverse Evaluation Methods for Extracting Insights via Experience Prototyping in Ambient and Civic computing 11th

Experiences with Using Diverse Evaluation Methods for Extrac...

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11th international conference on distributed, Ambient and Pervasive Interactions (DAPI) part of the 25th international conference on Human-Computer Interaction (HCI)

作者： Kimura, Risa Nakajima, Tatsuo Waseda Univ Dept Comp Sci & Engn Tokyo Japan

ISBN: (纸本)9783031346088;9783031346095

In this paper, we report our experiences with using diverse evaluation methods in our recent studies to develop some ambient and civic computing services. Extracting insights from doing researches is essential in our daily research activities, and we usually use diverse research methods for finding new opportunities from our research activities. In our current researches, we examined to use diverse research methods so we like to report our insights extracted from using the research methods. the most important contribution of this paper is to show the importance to use multiple research methods to extract various insights from the diverse angles in conducted researches.

关键词： Research Methods Ambient Intelligence Rapid Prototyping Ambient and Civic computing

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Guidelines for Practicing Responsible Innovation in HPC: A Sociotechnical Approach 11th

Guidelines for Practicing Responsible Innovation in HPC: A S...

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11th international conference on distributed, Ambient and Pervasive Interactions (DAPI) part of the 25th international conference on Human-Computer Interaction (HCI)

作者： Raybourn, Elaine M. Muollo, Killian Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9783031346675;9783031346682

While significant investments have been made in the exploration of ethics in computation, recent advances in high performance computing (HPC) and artificial intelligence (AI) have reignited a discussion for more responsible and ethical computing with respect to the design and development of pervasive sociotechnical systems within the context of existing and evolving societal norms and cultures. the ubiquity of HPC in everyday life presents complex sociotechnical challenges for all who seek to practice responsible computing and ethical technological innovation. the present paper provides guidelines which scientists, researchers, educators, and practitioners alike, can employ to become more aware of one's personal values system that may unconsciously shape one's approach to computation and ethics.

关键词： ethics responsible computing sociotechnical systems

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Accelerating distributed Urban Traffic Simulation via Enhanced Stale Synchronous Parallelism 11

Accelerating Distributed Urban Traffic Simulation via Enhanc...

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11th IEEE international conference on Behavioural and Social computing, BESC 2024

作者： Zhu, Haojia Ma, Ran Jin, Jiahui Lu, Hongru School of Computer Science and Engineering Southeast University China School of Software Engineering Southeast University China School of Computer Science Nanjing Audit University Nanjing China

ISBN: (纸本)9798331531904

Modeling urban mobility behaviours with micro-scopic traffic flow simulation is now crucial for studying intel-ligent urban decision-making algorithms, such as traffic light control and road congestion charging. However, in urban-scale traffic environments, simulation is computationally intensive. Most existing studies use distributed traffic simulations based on the Bulk Synchronous Parallel (BSP) model, leading to significant time and data transfer costs due to the need for coordination and data synchronization. To address these issues, we propose an enhanced Stale Synchronous Parallel (ESSP) model. Our model reduces the waiting time of the simulation compute nodes, and we design it to control and measure errors during simulation. We also develop a distributed traffic simulator to validate our model, and the results show a performance improvement of 30% to 50% compared to conventional synchronization methods. © 2024 IEEE.

关键词： Traffic congestion

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