arXiv - CS - Networking and Internet Architecture最新文献_第3页

Resources on the Move for Smart City: A Disruptive Perspective on the Grand Convergence of Sensing, Communications, Computing, Storage, and Intelligence 智慧城市的移动资源：传感、通信、计算、存储和智能大融合的颠覆性视角

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-09-14 DOI: arxiv-2409.09417

Yuguang Fang, Yiqin Deng, Xianhao Chen

{"title":"Resources on the Move for Smart City: A Disruptive Perspective on the Grand Convergence of Sensing, Communications, Computing, Storage, and Intelligence","authors":"Yuguang Fang, Yiqin Deng, Xianhao Chen","doi":"arxiv-2409.09417","DOIUrl":"https://doi.org/arxiv-2409.09417","url":null,"abstract":"The most commonly seen things on streets in any city are vehicles. However,\u0000most of them are used to transport people or goods. What if they also carry\u0000resources and capabilities for sensing, communications, computing, storage, and\u0000intelligence (SCCSI)? We will have a web of sensors to monitor the city, a\u0000network of powerful communicators to transport data around, a grid of computing\u0000power to conduct data analytics and machine learning (ML), a network of\u0000distributed storage to buffer/cache data/job for optimization, and a set of\u0000movable AI/ML toolboxes made available for specialized smart applications. This\u0000perspective article presents how to leverage SCCSI-empowered vehicles to design\u0000such a service network, simply called SCCSI network, to help build a smart city\u0000with a cost-effective and sustainable solution. It showcases how\u0000multi-dimensional technologies, namely, sensing, communications, computing,\u0000storage, and intelligence, converge to a unifying technology to solve grand\u0000challenges for resource demands from emerging large-scale applications. Thus,\u0000with SCCSI-empowered vehicles on the ground, over the air, and on the sea,\u0000SCCSI network can make resources and capabilities on the move, practically\u0000pushing SCCSI services to the edge! We hope this article serves as a spark to\u0000stimulate more disruptive thinking to address grand challenges of paramount\u0000importance.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reputation-Driven Peer-to-Peer Live Streaming Architecture for Preventing Free-Riding 防止 "搭便车 "的信誉驱动型点对点直播架构

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-09-14 DOI: arxiv-2409.09329

Rashmi Kushwaha, Rahul Bhattacharyya, Yatindra Nath Singh

引用次数: 0

Evaluating the Impact of Inter-cluster Communications in Edge Computing 评估边缘计算中集群间通信的影响

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-09-14 DOI: arxiv-2409.09278

Marc Michalke, Iulisloi Zacarias, Admela Jukan

{"title":"Evaluating the Impact of Inter-cluster Communications in Edge Computing","authors":"Marc Michalke, Iulisloi Zacarias, Admela Jukan","doi":"arxiv-2409.09278","DOIUrl":"https://doi.org/arxiv-2409.09278","url":null,"abstract":"Distributed applications based on micro-services in edge computing are\u0000becoming increasingly popular due to the rapid evolution of mobile networks.\u0000While Kubernetes is the default framework when it comes to orchestrating and\u0000managing micro-service-based applications in mobile networks, the requirement\u0000to run applications between multiple sites at cloud and edge poses new\u0000challenges. Since Kubernetes does not natively provide tools to abstract\u0000inter-cluster communications at the application level, inter-cluster\u0000communication in edge computing is becoming increasingly critical to the\u0000application performance. In this paper, we evaluate for the first time the\u0000impact of inter-cluster communication on edge computing performance by using\u0000three prominent, open source inter-cluster communication projects and tools,\u0000i.e., Submariner, ClusterLink and Skupper. We develop a fully open-source\u0000testbed that integrates these tools in a modular fashion, and experimentally\u0000benchmark sample applications, including the ML class of applications, on their\u0000performance running in the multi-cluster edge computing system under varying\u0000networking conditions. We experimentally analyze two classes of envisioned\u0000mobile applications, i.e., a) industrial automation, b) vehicle decision drive\u0000assist. Our results show that Submariner performs best out of the three tools\u0000in scenarios with small payloads, regardless of the underlying networking\u0000conditions or transmission direction between clusters. When sending larger data\u0000to a service, ClusterLink outperforms Submariner once the inter-node networking\u0000conditions deteriorate, which may be the case in highly mobile scenarios in\u0000edge computing. Finally, Skupper significantly outperforms others in a variety\u0000of scenarios with larger payloads.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"195 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative AI in Data Center Networking: Fundamentals, Perspectives, and Case Study 数据中心网络中的生成式人工智能：基础、视角和案例研究

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-09-14 DOI: arxiv-2409.09343

Yinqiu Liu, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Yonggang Wen, Dong In Kim

{"title":"Generative AI in Data Center Networking: Fundamentals, Perspectives, and Case Study","authors":"Yinqiu Liu, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Yonggang Wen, Dong In Kim","doi":"arxiv-2409.09343","DOIUrl":"https://doi.org/arxiv-2409.09343","url":null,"abstract":"Generative AI (GenAI), exemplified by Large Language Models (LLMs) such as\u0000OpenAI's ChatGPT, is revolutionizing various fields. Central to this\u0000transformation is Data Center Networking (DCN), which not only provides the\u0000computational power necessary for GenAI training and inference but also\u0000delivers GenAI-driven services to users. This article examines an interplay\u0000between GenAI and DCNs, highlighting their symbiotic relationship and mutual\u0000advancements. We begin by reviewing current challenges within DCNs and discuss\u0000how GenAI contributes to enhancing DCN capabilities through innovations, such\u0000as data augmentation, process automation, and domain transfer. We then focus on\u0000analyzing the distinctive characteristics of GenAI workloads on DCNs, gaining\u0000insights that catalyze the evolution of DCNs to more effectively support GenAI\u0000and LLMs. Moreover, to illustrate the seamless integration of GenAI with DCNs,\u0000we present a case study on full-lifecycle DCN digital twins. In this study, we\u0000employ LLMs equipped with Retrieval Augmented Generation (RAG) to formulate\u0000optimization problems for DCNs and adopt Diffusion-Deep Reinforcement Learning\u0000(DRL) for optimizing the RAG knowledge placement strategy. This approach not\u0000only demonstrates the application of advanced GenAI methods within DCNs but\u0000also positions the digital twin as a pivotal GenAI service operating on DCNs.\u0000We anticipate that this article can promote further research into enhancing the\u0000virtuous interaction between GenAI and DCNs.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"215 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VOMTC: Vision Objects for Millimeter and Terahertz Communications VOMTC：毫米波和太赫兹通信视觉对象

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-09-14 DOI: arxiv-2409.09330

Sunwoo Kim, Yongjun Ahn, Daeyoung Park, Byonghyo Shim

引用次数: 0

Throughput-Optimal Scheduling via Rate Learning 通过速率学习优化吞吐量调度

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-09-13 DOI: arxiv-2409.09198

Panagiotis Promponas, Víctor Valls, Konstantinos Nikolakakis, Dionysis Kalogerias, Leandros Tassiulas

引用次数: 0

WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks WirelessAgent：用于智能无线网络的大型语言模型代理

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-09-12 DOI: arxiv-2409.07964

Jingwen Tong, Jiawei Shao, Qiong Wu, Wei Guo, Zijian Li, Zehong Lin, Jun Zhang

引用次数: 0

External Memories of PDP Switches for In-Network Implementable Functions Placement: Deep Learning Based Reconfiguration of SFCs 用于网内可实现功能安置的 PDP 交换机外部存储器：基于深度学习的 SFC 重配置

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-09-12 DOI: arxiv-2409.08043

Somayeh Kianpisheh, Tarik Taleb

引用次数: 0

Directional WPT Charging for Routing-Asymmetric WRSNs with a Mobile Charger 利用移动充电器为路由不对称 WRSN 进行定向 WPT 充电

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-09-12 DOI: arxiv-2409.07994

Zhenguo Gao, Qi Zhang, Qingyu Gao, Yunlong Zhao, Hsiao-Chun Wu

{"title":"Directional WPT Charging for Routing-Asymmetric WRSNs with a Mobile Charger","authors":"Zhenguo Gao, Qi Zhang, Qingyu Gao, Yunlong Zhao, Hsiao-Chun Wu","doi":"arxiv-2409.07994","DOIUrl":"https://doi.org/arxiv-2409.07994","url":null,"abstract":"Mobile Charge Scheduling for wirelessly charging nodes in Wireless\u0000Rechargeable Sensor Networks (WRSNs) is a promising but still evolving research\u0000area. Existing research mostly assumes a symmetric environment, where the\u0000routing costs in opposite directions between two locations are considered\u0000identical. However, various factors such as terrain restrictions and wind or\u0000water flows may invalidate the routing-symmetric assumption in practical\u0000environments, thereby significantly limiting the performance of these solutions\u0000in routing-asymmetric WRSNs (RA-WRSNs). To address the routing-asymmetric\u0000challenges in mobile charge scheduling for WRSNs, this paper systematically\u0000investigates the underlying Asymmetric Directional Mobile Charger (DMC) Charge\u0000Scheduling (ADMCCS) problem, aiming to minimize energy loss while satisfying\u0000the charging demands of the network nodes. The DMC model is assumed because its\u0000results can be easily applied to the specialized case of an Omnidirectional\u0000Mobile Charger (OMC). To solve the ADMCCS problem, we propose a four-step\u0000framework. First, a minimum-size efficient charging position set is selected\u0000using our designed K-means-based Charging Position Generation (KCPG) algorithm,\u0000addressing the challenge of the unlimited charging position selection space.\u0000Next, minimum-size functional-equivalent direction sets at these positions are\u0000determined using an optimal algorithm, tackling the challenge of infinite\u0000charging directions. Subsequently, the optimal energy transmission time lengths\u0000for all directions at the positions are obtained by formulating and solving a\u0000Nonlinear Program (NLP) problem. Finally, the Lin-Kernighan Heuristic (LKH)\u0000algorithm for the Asymmetric Traveling Salesman Problem is adapted to obtain a\u0000highly probable optimal loop tour, addressing the routing-asymmetric challenge.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards a graph-based foundation model for network traffic analysis 建立基于图形的网络流量分析基础模型

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-09-12 DOI: arxiv-2409.08111

Louis Van Langendonck, Ismael Castell-Uroz, Pere Barlet-Ros

{"title":"Towards a graph-based foundation model for network traffic analysis","authors":"Louis Van Langendonck, Ismael Castell-Uroz, Pere Barlet-Ros","doi":"arxiv-2409.08111","DOIUrl":"https://doi.org/arxiv-2409.08111","url":null,"abstract":"Foundation models have shown great promise in various fields of study. A\u0000potential application of such models is in computer network traffic analysis,\u0000where these models can grasp the complexities of network traffic dynamics and\u0000adapt to any specific task or network environment with minimal fine-tuning.\u0000Previous approaches have used tokenized hex-level packet data and the model\u0000architecture of large language transformer models. We propose a new, efficient\u0000graph-based alternative at the flow-level. Our approach represents network\u0000traffic as a dynamic spatio-temporal graph, employing a self-supervised link\u0000prediction pretraining task to capture the spatial and temporal dynamics in\u0000this network graph framework. To evaluate the effectiveness of our approach, we\u0000conduct a few-shot learning experiment for three distinct downstream network\u0000tasks: intrusion detection, traffic classification, and botnet classification.\u0000Models finetuned from our pretrained base achieve an average performance\u0000increase of 6.87% over training from scratch, demonstrating their ability to\u0000effectively learn general network traffic dynamics during pretraining. This\u0000success suggests the potential for a large-scale version to serve as an\u0000operational foundational model.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0