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检索条件"任意字段=Proceedings of the 22nd International Conference on Distributed Computing and Networking"

共 1640 条记录，以下是51-60 订阅

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Joint Communication and Computation Scheduling for MEC-enabled AIGC Services Based on Generative Diffusion Model 22

Joint Communication and Computation Scheduling for MEC-enabl...

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22nd international Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks

作者： Liu, Huaizhe Wu, Jiaqi Zhuang, Xinyi Wu, Hongjia Gao, Lin Harbin Inst Technol Sch Elect & Informat Engn Shenzhen Peoples R China Harbin Inst Technol Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen Peoples R China

ISBN: (纸本)9798331508722;9783903176652

Artificial Intelligence-Generated Content (AIGC) based on Generative Diffusion Model (GDM) has emerged as a promising paradigm of content generation, revolutionizing the creation of diverse contents and driving significant technological advancements. Due to the low latency requirements of AIGC services, mobile edge computing (MEC) has become a crucial enabling technology for these services. In this work, we consider an MEC-enabled GDM-based AIGC network, which consists of multiple GDMs with varying sizes and capabilities deployed on edge computing servers (ES), and multiple mobile users (UEs) with diverse latency and accuracy requirements requesting AIGC services from ES through wireless access points (APs). In such a scenario, we are interested in the joint communication and computation scheduling problem for UEs, which involves selecting the appropriate APs (along with the communication bandwidth allocation) and the appropriate ES (together with the computation resource allocation and model inference optimization) for UEs, considering both the UEs' heterogeneous requirements and the GDMs' heterogeneous capabilities. To address the problem in a practical scenario with decentralized, autonomous, and self-interested UEs, we formulate a non-cooperative game, called the Joint User Association and Computation Offloading (JUACO) game, where each UE acts as a game player, selecting the best AP (for communication) as well as the best ES and the best GDM model inference step (for computation), aiming to minimize the inference time while meeting the specified inference accuracy requirement. We prove that the proposed JUACO game is a potential game, thus guaranteeing the existence of Nash equilibrium (NE) and the convergence of simple best response-based distributed algorithms to NE. Simulation results demonstrate that the proposed JUACO game approach significantly reduces the inference time and meets the required accuracy compared to traditional methods, validating the effec

关键词： Artificial Intelligence-Generated Content Generative Diffusion Model Mobile Edge computing

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HM2: Efficient Host Memory Management for RDMA-Enabled distributed Systems 23

HM2: Efficient Host Memory Management for RDMA-Enabled Distr...

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32nd international Symposium on High-Performance Parallel and distributed computing (HPDC) part of the ACM Federated computing Research conference (FCRC)

作者： Tang, Chen Chu, Zhaole Jin, Peiquan Luo, Yongping Guo, Kuankuan Univ Sci Technol China Hefei Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798400701559

Remote direct memory access (RDMA) supports zero-copy networking by transferring data from clients directly to host memory, eliminating the need to copy data between clients' memory and the data buffers in the hosting server. However, the hosting server must design efficient memory management schemes to handle incoming clients' data. In this paper, we propose a high-performance host memory management scheme called HM2 for RDMA-enabled distributed systems. We present a new buffer structure for incoming data from clients. In addition, we propose efficient data processing methods to reduce network transfers between clients and servers. We conducted a preliminary experiment to evaluate HM2, and the results showed HM2 achieved higher throughput than existing schemes, including L5 and FaRM.

关键词： RDMA memory management data buffer

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Revisiting the Underlying Causes of RDMA Scalability Issues 22

Revisiting the Underlying Causes of RDMA Scalability Issues

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22nd IEEE international Symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Zhang, Junye Yuan, Hui Wang, Zhe Yu, Peng Song, Hexiang Qu, Di Yan, Siyu Zheng, Xiaolong Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Huawei Technologies Co. Ltd China Shanghai Jiao Tong University China

ISBN: (纸本)9798331509712

Remote direct memory access (RDMA) networks are widely deployed in clouds and data centers for low latency and high throughput. Emerging applications like artificial intelligence training and serverless computing demand RDMA networks with high connection scalability. However, increasing connections can reduce throughput, as many academic research states. Conversely, some industry reports indicate the scalability issues are situation-specific. Despite this, existing work lacks a comprehensive analysis of RDMA scalability issue triggers and causes. In this paper, we revisit triggering conditions and underlying causes of RDMA scalability issues. First, we comprehensively analyze RDMA data flows and potential resource contentions, particularly with direct cache access. Second, we conduct extensive tests across varied measurement settings and RDMA NICs (RNICs). We identify triggering conditions including frequent switching of queue pair connections, rapid request posting, intensive memory access demands and inappropriate program configurations. We also systematically uncover causes of scalability issues, mainly due to RNIC cache misses, RNIC processing unit backpressure, host last-level cache misses, and software inefficiency. Finally, we provide guidelines to mitigate the RDMA scalability issues. © 2024 IEEE.

关键词： Scalability

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distributed Predictive Maintenance through Edge computing 22

Distributed Predictive Maintenance through Edge Computing

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Rayhana, Rakiba Yun, Hongguang Wang, Teng Chen, Johnson Fan, Yanshuo Liu, Zheng Gao, Wendy The University of British Columbia KelownaBC Canada Machine Learning Developer CRWN.ai KelownaBC Canada

ISBN: (纸本)9798331527471

This study presents a framework for distributed predictive maintenance using the BaSyx platform and edge computing devices. By integrating predictive maintenance algorithms, the system can estimate remaining useful life, detect early faults, and identify anomalies without prior failure data. The real-time processing capabilities of edge devices, combined with a local server, aid in immediate data integration and decision-making to optimize maintenance scheduling and improve operational reliability. While vehicles are used as a specific example in this research, the proposed framework is adaptable and applicable to predictive maintenance across various industrial assets. This study provides a baseline for advanced predictive maintenance strategies. © 2024 IEEE.

关键词： Predictive maintenance

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Scaling State Vector Sync 9

Scaling State Vector Sync

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22nd international conference on Information Centric networking

作者： Patil, Varun Song, Sichen Xiao, Guorui Zhang, Lixia Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781450392570

State Vector Sync (SVS) is a distributed Dataset Synchronization (Sync) protocol designed to support distributed applications running over ndN. SVS encodes raw dataset state in its messages to achieve resilient synchronization with low latency. As a result, the SVS message size grows linearly with the number of data producers in the same communication group, raising concerns about its scalability. This poster proposes a solution to improve SVS's scalability through the use of partial state vectors (p-SVS), and presents the results from our preliminary evaluation. Our results show that p-SVS has similar performance to vanilla SVS with improved scalability.

关键词： Named Data networking distributed Dataset Synchronization ndN Transport State Vector Sync Scaling

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22nd international conference on Information Centric networking

作者： Yoshida, Mikiya Ito, Yusuke Sato, Yurino Koga, Hiroyuki Univ Kitakyushu Kitakyushu Fukuoka Japan Sasebo Coll Natl Inst Technol Sasebo Japan

ISBN: (纸本)9781450392570

To realize low-latency content delivery in Information-Centric networking, a scheme to cluster domains and retain popular content in each cluster has been proposed, which enables consumers to retrieve content from nearby clusters. However, when the distribution of content popularity changes, all caches of content may not be retained adequately in a cluster, so consumers could not retrieve them from nearby clusters. We therefore propose a dynamic clustering scheme to adjust the cluster size, according to the change of content popularity, considering cache utilization and delivery latency, and evaluated its effectiveness through simulation.

关键词： ICN distributed Caching Dynamic Clustering

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Evaluation of distributed Collaborative Learning Approach for 5G Network Data Analytics Function 22

Evaluation of Distributed Collaborative Learning Approach fo...

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22nd IEEE international conference on Trust, Security and Privacy in computing and Communications, TrustCom 2023

作者： Wang, Shoufeng Chen, Hua-Min Ouyang, Ye Li, Fan Chen, Xuan Ma, Limeng Li, Zhanwu Bian, Sen Ren, Zhidong INC. China Beijing University of Technology Faculty of Information Technology Beijing China China Unicom Beijing Branch Network Optimization Center Beijing China China Unicom Beijing China

ISBN: (纸本)9798350381993

As a key role in the future network functionalities, artificial intelligence (AI) has been studied widely. To match the envisioned traffic evolution trend and communication requirement, Network Data Analytic Function(NWDAF) is a new proposed 5G component to provide analytics for any Network Functions (NFs). Considering sending all data to a central NWDAF instance is extremely time-consuming and it raises concerns about security vulnerabilities and data overload, it's expected to design a distributed NWDAF architecture which is able to enhance NF data localization, improve security, reduce control overhead during model training, shorten the training time, and finally, enhance the accuracy of the trained models by virtue of local testing on a real-time network. Federated learning is considered in future NWDAF design, while its detailed structure and processes are not standardized yet. This paper studies the effectiveness of distributed federated learning based traffic characteristics analysis for NWDAF. Simulation shows that the learning accuracy is nolinear with data loss probability. Under the simulation assumption, the solution could endure 30% data loss, while the learning accuracy and the retransmission dramatically rise when data loss probability is greater than 45%. © 2023 IEEE.

关键词： 5G mobile communication systems

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Hierarchical Federated Transfer Learning: A Multi-Cluster Approach on the computing Continuum 22

Hierarchical Federated Transfer Learning: A Multi-Cluster Ap...

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22nd IEEE international conference on Machine Learning and Applications, ICMLA 2023

作者： Ahmad, Sabtain Aral, Atakan Vienna University of Technology Vienna Austria University of Vienna Vienna Austria Umea University Umea Sweden

ISBN: (纸本)9798350345346

Federated Learning (FL) involves training models over a set of geographically distributed users. We address the problem where a single global model is not enough to meet the needs of geographically distributed heterogeneous clients. This setup captures settings where different groups of users have their own objectives however, users based on geographical location or task similarity, can be grouped together and by inter-cluster knowledge they can leverage the strength in numbers and better generalization in order to perform more efficient FL. We introduce a Hierarchical Multi-Cluster computing Continuum for Federated Learning Personalization (HC3FL) to cluster similar clients and train one edge model per cluster. HC3FL incorporates federated transfer learning to enhance the performance of edge models by leveraging a global model that captures collective knowledge from all edge models. Furthermore, we introduce dynamic clustering based on task similarity to handle client drift and to dynamically recluster mobile (non-stationary) clients. We evaluate the HC3FL approach through extensive experiments on real-world datasets. The results demonstrate that our approach effectively improves the performance of edge models compared to traditional FL approaches. © 2023 IEEE.

关键词： dynamic clustering federated transfer learning hierarchical collab-orative learning

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Information-Centric Service Mesh for Autonomous In-network computing 9

Information-Centric Service Mesh for Autonomous In-network C...

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22nd international conference on Information Centric networking

作者： Kanai, Kenji Tsuda, Toshitaka Nakazato, Hidenori Katto, Jiro Waseda Univ Waseda Res Inst Sci & Engn Tokyo Japan Waseda Univ Dept Comp Sci & Comm Engn Tokyo Japan

ISBN: (纸本)9781450392570

In this paper, we introduce an information-centric service mesh for autonomous in-network computing. The information-centric service mesh offers service-centric networking, not connectivity-based networking provided by the current container orchestration, like Kubernetes (K8s). We implement the information-centric service mesh by adopting an ambassador container style and using Cefore and Cefpyco which is the CCNx software implementation developed by NICT in Japan. For the proof-of-concept test, we demonstrate the end-to-end delay of more complex service chain models (up to 17 nodes) on our K8s cluster.

关键词： ICN Service Function Chaining Autonomous Network

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distributed Denial of Service Attack Detection by Machine Learning Techniques 22

Distributed Denial of Service Attack Detection by Machine Le...

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22nd international Learning and Technology conference, L and T 2025

作者： Jewani, Varkha K. Ajmire, Prafulla E. Atique, Mohd. Sharma, Bhisham Dhaou, Imed Ben Awadhesh Chaurasia, Suhashini Sant Gadge Baba Amravati University Amravati India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140401 India Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah Saudi Arabia RTM Nagpur University Department of Computer Science Nagpur India

ISBN: (纸本)9798331532215

The exponential expansion of computer networks and the internet makes it clear that there is a chance of being attacked and harmed. In the meantime, one of the most crucial defensive measures against the more complex and frequent network attacks is the intrusion detection and prevention systems (IDSs and IPSs). Due to insufficient datasets, anomaly-based research in intrusion detection systems is plagued by imprecise deployment, analysis, and evaluation. The researchers analyzed a variety of datasets, including DARPA98, KDD99, ISC2012, and ADFA13, to assess how well their suggested intrusion detection and intrusion prevention techniques performed. There are several issues: lack of adequate attack coverage, unrepresentative payloads, and anonymised packet information, insufficient traffic volume and diversity, or a deficiency in feature set and metadata. This study is focused on CICIDS-2017 which is the recently updated IDS dataset that meets real-world requirements and is accessible publicly. In addition to benign network flows and distributed denial of service (DDoS) attacks, it contains seven common attack network flows as well. To provide the optimal combination of features for the identification of the attack category, it also assesses the efficacy of several machine learning (ML) algorithms and network traffic feature sets. Even though various ML techniques like Linear Regression, Random Forests, and Decision Tree can be used, and performed very well, Hybrid Algorithm is designed to get better analysis on dataset. © 2025 IEEE.

关键词： Network intrusion

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