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检索条件"任意字段=Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 4330 条记录，以下是221-230 订阅

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An Efficient network Flow Rules Compression Method based On In-network Computation On Cloud Computing 29

An Efficient Network Flow Rules Compression Method Based On ...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Zhang, Penghao Zhang, Xinyi Shanxi University China Chinese Academy of Sciences Computer Network Information Center China

ISBN: (纸本)9798350330717

The development of cloud computing has led to the explosion of network traffic. The switches of cloud computing make it hard to process large-scale network traffic. Prior approaches proposed flow rules compression methods with splitting matching fields, expanding switch storage space, or reconstructing network architecture. The methods incurred little performance improvement in the network. The technology of innetwork computation(INC) has been widely used to accelerate the network of cloud computing and data-intensive distributed applications. The computing tasks performed on servers are offloaded to the network through INC. We implement a flow rules compression method(NetFR) based on INC to accelerate the network performance of cloud computing. NetFR adopts heuristic methods which include rule-deduplication and optimal coverage to compress flow rules, and offloads compressed rules into switches to accelerate the performance of the network on cloud computing. Finally, we evaluate NetFR and show that the performance of NetFR is 1.3x better than other methods of network packet matching. Furthermore, NetFR can achieve 98% compression ratio on average and 99.99% on highest. © 2023 IEEE.

关键词： Cloud Computing In-network Computing network Flow Rules Compression Programmable Switch

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Research on Prototype Tool Improvement System based on Power Internet Reference Application Scenario Model

Research on Prototype Tool Improvement System Based on Power...

引用

2024 Asia-Pacific conference on Software Engineering, Social network Analysis and Intelligent Computing, SSAIC 2024

作者： Mu, Jun Fan, Guangming Yang, Wenqing Feng, Jia Sun, Lintan An, Yeteng Yang, Rui State Grid Electric Power Research Institute Co. Ltd Nanjing China State Grid Customer Service Center Tianjin China

ISBN: (纸本)9798350393682

The prototype system of power Internet application business risk management is the basis for the research of power Internet operation and maintenance, risk management and fault early warning. Integrate existing simulation data and various computing resources of power system to provide parallel/distributed high-performance computing. A service based grid application development platform is constructed to meet the needs of large-scale power system computing and operation and maintenance monitoring. A distributed resource storage and retrieval mechanism is designed and implemented, which is coordinated by multiple resource monitoring servers. The results show that the prototype system has the characteristics of processing distributed heterogeneous data, utilizing distributed computing power and storing computing results in a decentralized manner. The system meets the requirements of multi regional and multi-level power Internet application business risk management. © 2024 IEEE.

关键词： Informatization

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A Moving Target Defense Approach for the distributed Dynamic network 21

A Moving Target Defense Approach for the Distributed Dynamic...

引用

21st IEEE International Symposium on parallel and distributed processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Lin Guo, Yunchuan Leng, Siyuan Li, Zifu Li, Fenghua Fang, Liang Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350329223

The distributed dynamic network is vulnerable to scanning attacks due to the openness of wireless channels. Traditional defense systems tend to be passive and exhibit delayed responses. A moving target defense approach, namely distributed network Address Shuffling (DNAS), is proposed to thwart attackers' network scanning through the shuffling of network addresses. To resolve address conflicts resulting from this shuffling, DNAS employs a dynamic diffusion method of allocated addresses before the shuffling process to reduce the probability of conflict generation, and utilizes a passive detection based conflict elimination algorithm after the shuffling process to eliminate any generated conflicts. To select low-risk addresses, DNAS leverages an artificial feature selection based Fully Connected Neural network (FCNN) to recognize the attacker's scanning policy, and identifies low-risk addresses based on the scanning range of the policy. Empirical experiments and theoretical analysis indicate that DNAS significantly reduces the probability of address conflict generation at a minimal cost. It effectively eliminates all generated address conflicts within an average conflict resolution time of less than 500ms. Furthermore, DNAS exhibits an accuracy of 99.45% in recognizing scanning policies, surpassing pseudorandom address hopping in diminishing the success rate of sequential, local random, and mixed scanning. © 2023 IEEE.

关键词： network security

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24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

引用

24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615247

The proceedings contain 131 papers. The special focus in this conference is on Algorithms and Architectures for parallel processing. The topics include: MARO: Enabling Full MPI Automatic Refactoring in DSL-based Programming Framework;SSC: An SRAM-based Silence Computing Design for On-chip Memory;TP-BFT: A Faster Asynchronous BFT Consensus with parallel Structure;LTP: A Lightweight On-Chip Temporary Prefetcher for Data-Dependent Memory Accesses;A Neural network-based PUF Protection Method Against Machine Learning Attack;Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs;multidimensional Intrinsic Identity Construction and Dynamic Seamless Authentication Schemes in IoT Environments;invisible Backdoor Attack with Image Contours Triggers;finestra: Multi-aggregator Swarm Learning for Gradient Leakage Defense;DIsFU: Protecting Innocent Clients in Federated Unlearning;multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning;dynamic Privacy Protection with Large Language Model in Social networks;a Dynamic Symmetric Searchable Encryption Scheme for Rapid Conjunctive Queries;a Data Watermark Scheme Base on Data Converted Bitmap for Data Trading;distributed Incentive Algorithm for Fine-Grained Offloading in Vehicular Ad Hoc networks;mitigating Over-Unlearning in Machine Unlearning with Synthetic Data Augmentation;AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV Detection;efficient and Privacy-Preserving Ranking-based Federated Learning;on-Chain Dynamic Policy Evaluation for Decentralized Access Control;DPG-FairFL: A Dual-Phase GAN-based Defense Framework Against Image-based Fairness Data Poisoning Attacks in Federated Learning.

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24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615476

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24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615278

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Federal Hierarchical Mutual Assistance based on Maritime Intelligent Decision Recommendation 24

Federal Hierarchical Mutual Assistance Based on Maritime Int...

引用

3rd International conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Liu, Xinru Kang, Jiabao Yang, Tingting Zhang, Shubo Navigation College Dalian Maritime University China School of Future Technology South China University of Technology China Peng Cheng Laboratory China

ISBN: (纸本)9798400718120

With the rapid development of artificial intelligence, edge computing and distributed collaborative processing, more effective solutions have been provided to solve the problems in the field of maritime communications. The surge in maritime activities and the growing number of vessels have precipitated an escalating demand for the real-time processing of oceanic data. However, the vast ocean environment and limited communication resources make the management and intelligent decision-making of heterogeneous data complex. In handling the collaborative processing of large-scale maritime fleet tasks, such as emergency responses to marine pollution, the network nodes of individual vessels are dispersed, and data timeliness is poor, which significantly impacts decision-making for ship operators. Additionally, the information exchanged between cooperating vessels must maintain privacy, making it difficult to develop personalized intelligent communication strategies. To address these potential issues, we propose a Federated Hierarchical Mutual Assistance (FHMA) algorithm for maritime recommendation systems. By integrating federated learning with layered privacy protection techniques, this approach ensures data privacy while also enabling model personalization. Additionally, it introduces compute power sharing and reward distribution mechanisms, incentivizing client participants to actively contribute their computational resources, thereby improving network efficiency. This algorithm not only efficiently handles large-scale, dynamically changing spatiotemporal data but also maintains high-quality recommendation results in parallel and distributed data processing environments, providing new directions for intelligent decision-making in the maritime communication field. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Transparent Remote OpenMP Offloading based on MPI

Transparent Remote OpenMP Offloading Based on MPI

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29th International conference on parallel and distributed Computing (Euro-Par)

作者： Kasmeridis, Ilias K. Mantelos, Spyros Piperis, Apostolos Dimakopoulos, Vassilios V. Univ Ioannina Dept Comp Sci & Engn Ioannina Greece

ISBN: (纸本)9783031488023;9783031488030

In this work, we present an efficient mechanism which allows unmodified OpenMP applications to leverage the computational resources of any node in a cluster through the OpenMP device interface. Remote CPUs and remote accelerators such as GPUs are all available to offload code portions to, completely transparently, as if they belonged to the host node that executes the application. This is possible by virtualizing remote resources to appear as local devices and introducing a device-agnostic communication mechanism that forwards data requests to/from the remote nodes. The communication layer is based on MPI, eliminating the need for custom request servers, while providing excellent portability and optimized performance for a variety of network fabrics. Moreover, our implementation allows concurrent offloading to any combination of local and remote devices.

关键词： OpenMP Cluster computing GPUs Offloading

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24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615506