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共 140 条记录，以下是131-140 订阅

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A System-Wide Communication to Couple Multiple MPI Programs for Heterogeneous computing 23rd

A System-Wide Communication to Couple Multiple MPI Programs...

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23rd international conference on Parallel and distributed computing, Applications, and Technologies, PDCAT 2022

作者： Sumimoto, Shinji Arakawa, Takashi Sakaguchi, Yoshio Matsuba, Hiroya Yashiro, Hisashi Hanawa, Toshihiro Nakajima, Kengo The University of Tokyo Kashiwa Japan CliMTech Inc. Funabashi Japan Fujitsu Ltd. Tokyo Japan Hitachi Ltd. Tokyo Japan National Institute for Environmental Studies Tsukuba Japan RIKEN CCS Kobe Japan

ISBN: (纸本)9783031299261

This paper proposes a system-wide communication library to couple multiple MPI programs for heterogeneous coupling computing called h3-Open-SYS/WaitIO-Socket (WaitIO-Socket for short). WaitIO-Socket provides an inter-program communication environment among MPI programs and supports different MPI libraries with various interconnects and processor types. We have developed the WaitIO-Socket communication library and tested it on the Wisteria/BDEC-01 supercomputing system, including Odyssey (Fujitsu A64FX-aarch64/Fujitsu-MPI/Tofu) and Aquarius (Intel Xeon-x86_64+NVIDIA-A100/Intel MPI/InfiniBand). As a result of the evaluation, WaitIO-Socket can execute large-scale programs on the Wisteria system, our first target system. The Odyssey and Aquarius MPI programs are able to communicate using WaitIO-Socket and achieve 53.2 GB/s using multiple streams throughout the system. We also show that the application NICAM/ADA is able to run with the h3-Open-UTIL/MP coupler 35% faster on the combination of Odyssey with Arm CPU and Aquarius with NVIDIA GPU than Odyssey with Arm CPU. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Secure Federated Learning with Blockchain and Homomorphic Encryption for Healthcare Data Sharing 23

Secure Federated Learning with Blockchain and Homomorphic En...

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23rd international conference on Cyberworlds, CW 2024

作者： Firdaus, Muhammad Rhee, Kyung-Hyune Pukyong National University Industrial Science Technology Research Center Korea Republic of Telkom University School of Computing Indonesia Pukyong National University Division of Computer Engineering and AI Korea Republic of

ISBN: (纸本)9798331527174

Healthcare data is often fragmented across various institutions due to its highly sensitive and private nature. In this sense, hospitals and clinics maintain electronic health records (EHRs) independently;hence, valuable data is siloed within individual organizations, preventing comprehensive analysis that could benefit from diverse data sources. Federated learning (FL) addresses these challenges by enabling the training of a shared global model using data distributed across multiple institutions without moving the data from its source. By leveraging FL, healthcare institutions can combine their data assets to improve predictive analytics, personalized medicine, and overall healthcare outcomes, ultimately benefiting patients and the healthcare system. However, the current FL model with a central server presents several challenges within healthcare, including the risk of malicious attacks, regulatory compliance, and privacy vulnerabilities. To overcome these issues, this paper introduces the FL framework with blockchain and homomorphic encryption (HE). Our framework aims to minimize the role of the central server, enable collaborative model training across healthcare organizations, and enhance data security and privacy. In this sense, blockchain ensures the integrity and transparency of the process, while homomorphic encryption ensures that the data remains private. This framework can potentially enable institutions to enrich medical knowledge while securely keeping patient data collaboratively and facilitating healthcare analytics in practical settings. © 2024 IEEE.

关键词： Federated learning

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distributed Measurement System for Performance Evaluation of Embedded Clock Synchronization Solutions

Distributed Measurement System for Performance Evaluation of...

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international Carpathian Control conference (ICCC)

作者： Andrá s Wiesner Tamá s Ková cshá zy Department of Measurement and Information Systems Budapest University of Technology and Economics Budapest Hungary

distributed, real-time, safety-critical systems gain wide-scale acceptance in the automotive, aerospace, industrial, science, telecommunication, energy distribution, and audio-video distribution applications. In these systems, Time-Sensitive networking (TSN) can provide the required real-time and reliable communication services. TSN requires precision, hardware-assisted time synchronization provided by the IEEE 802.1AS-Rev or its progenitor, IEEE 1588. IEEE 1588 has a long history in distributed measurement systems, such as vehicular test systems, energy distribution, astrophysics, communication systems such as 4G and 5G, etc. Most modern hardware supports the hardware requirements for IEEE 1588 or IEEE 802.1AS-Rev in some respect; however, software support is very limited on network embedded microcontrollers (MCU), i.e., most modern MCUs do not have open-source, free SW support for time synchronization. We have developed flexPTP (https://***/epagris/flexPTP), an open-source, open-licensed PTP implementation for MCUs based on FreeRTOS (embedded real-time OS for MCUs) and lwIP (a TCP/IP stack for MCUs). During the development of the project, we have also created a distributed measurement system for the performance evaluation of flexPTP, which is built into flexPTP. The system can be used during development, but it is also applicable for online monitoring. The data collection component of the distributed measurement system collects data from the MCUs running flexPTP in a lightweight, low resource utilization, platform-independent, MCU-conform way, and forwards the information to a central node, on which data storage, analyses, and presentation can be done. We have also developed a WEB application for online supervision of synchronization in a browser. The system also has a self-discovery feature, allowing developers to identify all nodes capable of running the data collection component of the measurement system. In the paper, we will also present the initial pe

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Client Scheduling for Unreliable Semi-Decentralized Federated Learning 23

Client Scheduling for Unreliable Semi-Decentralized Federate...

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23rd IEEE international conference on Communication Technology, ICCT 2023

作者： Tan, Yuhao Zhao, Haitao Xia, Wenchao Wang, Qin Guo, Kun Xu, Bo Quek, Tony Q. S. Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing210003 China Chongqing Institute of East China Normal University Chongqing Key Laboratory of Precision Optics Chongqing401120 China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore

ISBN: (纸本)9798350325959

The Industrial Internet of Things (IIoT) is emerging as a promising technology that can accelerate the application of industrial intelligence. Because of the sensitive nature of user data, federated learning (FL) which performs distributed machine learning while preserving data privacy, is developed to meet the accuracy and privacy requirements of IIoT end devices/clients. However, the unreliable communications in IIoT may negatively affect the training efficiency. In this paper, we study on the client scheduling problem in a multi-server FL framework for the communication reliability and training efficiency improvement. A client-server association optimization problem is formulated, with the objective of minimizing the global training loss. Resorting to the convergence analysis of SD-FL, the original problem is simplified and transformed to guide us to design a high-efficiency client scheduling scheme. Finally, simulation results show that the proposed scheme significantly outperforms the baselines in terms of the test accuracy and training loss. © 2023 IEEE.

关键词： client scheduling edge association edge computing federated learning industrial internet of things

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Many-Constraint and Many-Objective optimization with Bias Index for Intercloud Multi-Workflow Resource Provisioning

Many-Constraint and Many-Objective optimization with Bias In...

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international Symposium on Soft computing and Intelligent Systems (SCIS)

作者： Courtney Powell Katsunori Miura Masaharu Munetomo Information Initiative Center Hokkaido University Sapporo Japan Department of Information and Management Science Otaru University of Commerce Otaru Japan

Optimal deployment of multi-component big data applications is difficult in geo-distributed intercloud environments. This is because of the numerous infrastructure components and options available and the variety of constraints that must be satisfied, such as application, cloud infrastructure, data processing, and privacy-related constraints. The task becomes even more complicated when multiple scientific workflows must be executed with a severely limited resource-acquisition budget. This paper proposes a many-objective constrained optimization framework that addresses these issues. The proposed framework first conducts constraint satisfaction via equivalent transformation, then many-objective optimization using nondominated sorting, reference points, and elitism. In the case of multiple workflows, both optimization for each workflow and optimization for the ensemble of workflows are considered. Furthermore, a proposed bias index is presented that indicates on an objective-by-objective basis the effect of the configuration generated for each ensemble of workflows on the optimal configuration of each constituent workflow. It also provides a means of ascertaining on a granular level the relative fairness of each objective in each composite resource configuration, and can be used as a tool for prioritizing certain aspects of a workflow when deciding on the optimal configuration to utilize. We demonstrate the efficacy of the proposed framework through two genome analysis workflows, for which site availability and resource reliability need to be maximized, deployment cost and makespan need to be minimized, and several constraints must be satisfied.

关键词： Cloud computing Costs Processor scheduling Big Data applications Reliability engineering Indexes Resource management

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Construction of Smart Pension System for Urban and Rural Residents Based on Big Data Mining Algorithm

Construction of Smart Pension System for Urban and Rural Res...

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distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Jing Zhao International Business School Xi’an Fanyi University Shaanxi China

Population aging is a social problem that every country cannot avoid in the process of modernization. China has rapidly stepped into the threshold of an aging society. With the deepening of the aging of the population and with the increasing demand for diversified services for the elderly, there is an urgent need to provide multi-level, high-quality elderly care(ec) services for the elderly. There is a large gap in human resources and other resources in the traditional pension industry. The pension industry needs to develop. Zhiyi pension has opened a new realm of the pension model. It cannot only provide a variety of life assistance, Rehabilitation care, psychological comfort and social support services for the elderly through intelligent decision-making and control, but also always protect the safety of the elderly and meet the multi-level and diversified service The needs of urban and rural elderly. We should play the leading comprehensive role of innovation driving role of information technology, accelerate the development of smart health care industry, improve people's livelihood and cultivate new economic drivers, On the 23rd of 2017, the Ministry of Industry, Information Technology and Information Technology, the Ministry of Civil Affairs and the Health and Family Planning Commission jointly issued the Action Plan for the Development of Smart ec Industry (2017-2022), which explicitly mentioned the year 2022, It will "basically form a smart health care industry system covering the whole life cycle. So far, the Smart Pension (SM) industry has completed its overall layout and entered the practical level.

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QR Factorization of Block Low-Rank Matrices on Multi-instance GPU 23rd

QR Factorization of Block Low-Rank Matrices on Multi-insta...

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23rd international conference on Parallel and distributed computing, Applications, and Technologies, PDCAT 2022

作者： Ohshima, Satoshi Ida, Akihiro Yokota, Rio Yamazaki, Ichitaro Information Technology Center Nagoya University Aichi Japan Research Institute for Value-Added-Information Generation Japan Agency for Marine-Earth Science and Technology Kanagawa Japan Global Scientific Information and Computing Center Tokyo Institute of Technology Tokyo Japan Scalable Algorithms Department Sandia National Laboratories New Mexico United States

ISBN: (纸本)9783031299261

The QR factorization, which is a fundamental operation in linear algebra, is used extensively in scientific simulations. The acceleration and memory reduction of it are important research targets. QR factorization using block low-rank matrices (BLR-QR) has previously been proposed to address this issue. In this study, we consider its implementation on a GPU. Current CPUs and GPUs have numerous computational cores and the performance consists of the total performance of them. Therefore, the degree of parallelism of the target calculation is important for obtaining high performance. By contrast, many applications, including BLR-QR, do not have sufficient parallelism. Batched computation has attracted attention for achieving high performance in such calculations. However, the use of it requires major code rewriting and is extremely laborious. Thus, we propose the use of the multi-instance GPU (MIG) feature of current GPUs. Using MIG, we succeeded in obtaining a 53.3% time reduction over the CPU and 77.6% over the GPU without MIG. From the above result, we succeeded in demonstrating rapid implementation of BLR-QR on MIG and usefulness of MIG. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Exposing IoT Platforms Securely and Anonymously Behind CGNAT

Exposing IoT Platforms Securely and Anonymously Behind CGNAT

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Roedunet international conference (RoEduNet)

作者： Daniel-Florin Hriţcan Doru Balan Dept. of Computers Automation and Electronics University “Stefan cel Mare” Suceava Suceava Romania

ISBN: (数字)9798331540388

ISBN: (纸本)9798331540395

The swift expansion of Internet of Things (IoT) devices has resulted in considerable progress in connectivity and automation, yet it has simultaneously brought about notable security weaknesses. This paper presents a novel approach to securing IoT platforms through a comprehensive architecture that integrates a double reverse proxy setup, leveraging Oracle Cloud Virtual Private Servers (VPS), NGINX Proxy Manager, Cloudflare, and Tailscale encrypted peer-to-peer (P2P) tunneling. The proposed solution employs Oracle Cloud VPS to host and manage IoT applications, ensuring robust performance and scalability. NGINX Proxy Manager is utilized to facilitate efficient and secure load balancing and reverse proxy functions. Cloudflare provides additional security layers, including distributed Denial of Service (DDoS) protection, SSL/TLS encryption, and traffic management. To further enhance security, the encryption of Tailscale P2P tunneling creates a secure mesh network between devices, ensuring data integrity and confidentiality. This architecture not only fortifies the security posture of IoT platforms but also improves the resilience and reliability of data transmission.

关键词： Cloud computing Scalability Tunneling Encryption Peer-to-peer computing Internet of Things Servers Reliability Computer crime Resilience

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ClusterX: Adaptive Collaborative Scheduling of Layered User-Proxy Mapping to Enhance DDoS Defense in distributed Clusters

ClusterX: Adaptive Collaborative Scheduling of Layered User-...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Jianbo Lin Lin Yan Zhi Lin Zan Zhou Shujie Yang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

In the contemporary digital landscape, distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and integrity of online services. As cloud services and network infrastructures increasingly adopt distributed architectures, the challenge of defending against these attacks has become more complex. This paper introduces ClusterX, a novel two-tiered scheduling framework designed to enhance DDoS defense and service quality in distributed clusters. ClusterX incorporates Moving Target Defense (MTD) principles with a hierarchical, collaborative approach, featuring Program Chairs and Area Chairs that dynamically manage user-proxy mappings to counteract the fluid nature of DDoS attacks. The architecture of ClusterX is underpinned by a proactive transfer mechanism, which leverages user-service activity profiles to intelligently redistribute user traffic across clusters. This mechanism ensures that no single cluster becomes a bottleneck, maintaining low latency and high service quality even under high traffic conditions. Program Chairs oversee the global traffic distribution, while Area Chairs execute localized scheduling decisions, working in tandem to prevent traffic congestion and enhance the resilience of the network. Through extensive simulations and real-world experiments, we demonstrate the effectiveness of ClusterX in detecting and mitigating DDoS attacks, while maintaining high service availability. The results highlight the superiority of our approach in adapting to the dynamic nature of modern cyber threats and the benefits of a coordinated defense strategy in distributed cloud environments. ClusterX represents a significant advancement in the field of network security, offering a robust and adaptive solution to one of the most pressing challenges in cybersecurity today.

关键词： Privacy Processor scheduling Collaboration Denial-of-service attack User experience Real-time systems Computer crime Surges Traffic congestion Resilience

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Scalable distributed microservices for autonomous UAV swarms: poster abstract 22

Scalable distributed microservices for autonomous UAV swarms...

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Proceedings of the 23rd international Middleware conference Demos and Posters

作者： Kevyn Angueira Irizarry Ziehen Zhang Christopher Stewart Jayson Boubin Ohio State University Binghamton University

ISBN: (纸本)9781450399319

Unmanned aerial vehicles (UAV) are revolutionizing critical industries. Their inexpensive and accessible nature makes them useful for a number of broad applications including agriculture, infrastructure inspection, and more. In response to this popularity, UAV manufacturers, hobbyists, and researchers have developed myriad software packages for UAV control to simplify and automate UAV flight. Recent advances have also led to autonomous UAV that complete complex missions without human pilots and swarms of UAV that work together to solve tasks. Recently, researchers have used autonomy and swarms to allow UAV to cover wide areas quickly and intelligently. Few software packages explicitly support either autonomy and swarming for UAV, and none to our knowledge combine these features. We present early work on SoftwarePilot 2.0, a UAV software package that supports swarms of autonomous UAV. SoftwarePilot 2.0 improves on prior work to expand microservice model designs which are easier to manage using cloud-native technologies. SoftwarePilot 2.0's edge-efficient design allows UAV swarms to easily scale across the edge and cloud, and supports cutting edge autonomy techniques.

关键词： swarm agriculture unmanned aerieal vehicles edge computing

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