Optimizing network throughput in real-world dynamic systems is critical, especially for diverse and delay-sensitive multimedia data types such as VoIP and video streaming. Traditional routing protocols, which rely on static metrics and single shortest-path algorithms, were unable in managing this complex information. To address these challenges, we propose a novel approach that enhances resource utilization while maintaining Quality of Service (QoS). Our dynamic traffic allocation model prioritizes different data types based on their delay sensitivity and allocates traffic by considering factors such as bandwidth, latency, and network failures. This approach is shown to significantly improve network throughput compared to static load balancing, especially for multimedia applications. Simulation results confirm the effectiveness of this dynamic method in maximizing network throughput and maintaining QoS across various data types.
{"title":"Maximization of Communication Network Throughput using Dynamic Traffic Allocation Scheme","authors":"Md. Arquam, Suchi Kumari","doi":"arxiv-2409.04724","DOIUrl":"https://doi.org/arxiv-2409.04724","url":null,"abstract":"Optimizing network throughput in real-world dynamic systems is critical,\u0000especially for diverse and delay-sensitive multimedia data types such as VoIP\u0000and video streaming. Traditional routing protocols, which rely on static\u0000metrics and single shortest-path algorithms, were unable in managing this\u0000complex information. To address these challenges, we propose a novel approach\u0000that enhances resource utilization while maintaining Quality of Service (QoS).\u0000Our dynamic traffic allocation model prioritizes different data types based on\u0000their delay sensitivity and allocates traffic by considering factors such as\u0000bandwidth, latency, and network failures. This approach is shown to\u0000significantly improve network throughput compared to static load balancing,\u0000especially for multimedia applications. Simulation results confirm the\u0000effectiveness of this dynamic method in maximizing network throughput and\u0000maintaining QoS across various data types.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183693","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}
Alice Le Bihan, Juan A. Fraire, Pierre Francois, Felix Flentge
The Interplanetary Network (IPN) emerges as the backbone for communication between various spacecraft and satellites orbiting distant celestial bodies. This paper introduces the Interplanetary Network Visualizer (IPN-V), a software platform that integrates interplanetary communications planning support, education, and outreach. IPN-V bridges the gap between the complexities of astrodynamics and network engineering by enabling the generation and assessment of dynamic, realistic network topologies that encapsulate the inherent challenges of space communication, such as time-evolving latencies and planetary occlusions. Leveraging the power of Unity 3D and C#, IPN-V provides a user-friendly 3D interface for the interactive visualization of interplanetary networks, incorporating contact tracing models to represent line-of-sight communication constraints accurately. IPN-V supports importing and exporting contact plans compatible with established space communication standards, including NASA's ION and HDTN formats. This paper delineates the conception, architecture, and operational framework of IPN-V while evaluating its performance metrics.
行星际网络（IPN）是围绕遥远天体运行的各种航天器和卫星之间进行通信的主干网。本文介绍行星际网络展示台（IPN-V），这是一个集行星际通信规划支持、教育和推广于一体的软件平台。IPN-V 在复杂的空间动力学和网络工程之间架起了一座桥梁，它能够生成和评估动态、逼真的网络拓扑结构，这些拓扑结构囊括了空间通信固有的挑战，如时变延迟和行星遮挡。IPN-V 利用 Unity 3D 和 C# 的强大功能，为行星际网络的交互式可视化提供了用户友好的 3D 界面，并结合了接触跟踪模型，以准确地表示视距通信限制。IPN-V 支持导入和导出与既定空间通信标准兼容的接触计划，包括 NASA 的 ION 和 HDTN 格式。本文介绍了 IPN-V 的构思、架构和运行框架，同时对其性能指标进行了评估。
{"title":"IPN-V: The Interplanetary Network Visualizer","authors":"Alice Le Bihan, Juan A. Fraire, Pierre Francois, Felix Flentge","doi":"arxiv-2409.04857","DOIUrl":"https://doi.org/arxiv-2409.04857","url":null,"abstract":"The Interplanetary Network (IPN) emerges as the backbone for communication\u0000between various spacecraft and satellites orbiting distant celestial bodies.\u0000This paper introduces the Interplanetary Network Visualizer (IPN-V), a software\u0000platform that integrates interplanetary communications planning support,\u0000education, and outreach. IPN-V bridges the gap between the complexities of\u0000astrodynamics and network engineering by enabling the generation and assessment\u0000of dynamic, realistic network topologies that encapsulate the inherent\u0000challenges of space communication, such as time-evolving latencies and\u0000planetary occlusions. Leveraging the power of Unity 3D and C#, IPN-V provides a\u0000user-friendly 3D interface for the interactive visualization of interplanetary\u0000networks, incorporating contact tracing models to represent line-of-sight\u0000communication constraints accurately. IPN-V supports importing and exporting\u0000contact plans compatible with established space communication standards,\u0000including NASA's ION and HDTN formats. This paper delineates the conception,\u0000architecture, and operational framework of IPN-V while evaluating its\u0000performance metrics.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183732","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}
Mohammad Sajid Shahriar, Suresh Subramaniam, Motoharu Matsuura, Hiroshi Hasegawa, Shih-Chun Lin
In the realms of the internet of vehicles (IoV) and intelligent transportation systems (ITS), software defined vehicular networks (SDVN) and edge computing (EC) have emerged as promising technologies for enhancing road traffic efficiency. However, the increasing number of connected autonomous vehicles (CAVs) and EC-based applications presents multi-domain challenges such as inefficient traffic flow due to poor CAV coordination and flow-table overflow in SDVN from increased connectivity and limited ternary content addressable memory (TCAM) capacity. To address these, we focus on a data-driven approach using virtualization technologies like digital twin (DT) to leverage real-time data and simulations. We introduce a DT design and propose two data-driven solutions: a centralized decision support framework to improve traffic efficiency by reducing waiting times at roundabouts and an approach to minimize flow-table overflow and flow re-installation by optimizing flow-entry lifespan in SDVN. Simulation results show the decision support framework reduces average waiting times by 22% compared to human-driven vehicles, even with a CAV penetration rate of 40%. Additionally, the proposed optimization of flow-table space usage demonstrates a 50% reduction in flow-table space requirements, even with 100% penetration of connected vehicles.
{"title":"Digital Twin Enabled Data-Driven Approach for Traffic Efficiency and Software-Defined Vehicular Network Optimization","authors":"Mohammad Sajid Shahriar, Suresh Subramaniam, Motoharu Matsuura, Hiroshi Hasegawa, Shih-Chun Lin","doi":"arxiv-2409.04622","DOIUrl":"https://doi.org/arxiv-2409.04622","url":null,"abstract":"In the realms of the internet of vehicles (IoV) and intelligent\u0000transportation systems (ITS), software defined vehicular networks (SDVN) and\u0000edge computing (EC) have emerged as promising technologies for enhancing road\u0000traffic efficiency. However, the increasing number of connected autonomous\u0000vehicles (CAVs) and EC-based applications presents multi-domain challenges such\u0000as inefficient traffic flow due to poor CAV coordination and flow-table\u0000overflow in SDVN from increased connectivity and limited ternary content\u0000addressable memory (TCAM) capacity. To address these, we focus on a data-driven\u0000approach using virtualization technologies like digital twin (DT) to leverage\u0000real-time data and simulations. We introduce a DT design and propose two\u0000data-driven solutions: a centralized decision support framework to improve\u0000traffic efficiency by reducing waiting times at roundabouts and an approach to\u0000minimize flow-table overflow and flow re-installation by optimizing flow-entry\u0000lifespan in SDVN. Simulation results show the decision support framework\u0000reduces average waiting times by 22% compared to human-driven vehicles, even\u0000with a CAV penetration rate of 40%. Additionally, the proposed optimization of\u0000flow-table space usage demonstrates a 50% reduction in flow-table space\u0000requirements, even with 100% penetration of connected vehicles.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183692","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}
Vaishnavi Kasuluru, Luis Blanco, Miguel Angel Vazquez, Cristian J. Vaca-Rubio, Engin Zeydan
This paper deals with the problem of energy consumption minimization in Open RAN cell-free (CF) massive Multiple-Input Multiple-Output (mMIMO) systems under minimum per-user signal-to-noise-plus-interference ratio (SINR) constraints. Considering that several access points (APs) are deployed with multiple antennas, and they jointly serve multiple users on the same time-frequency resources, we design the precoding vectors that minimize the system power consumption, while preserving a minimum SINR for each user. We use a simple, yet representative, power consumption model, which consists of a fixed term that models the power consumption due to activation of the AP and a variable one that depends on the transmitted power. The mentioned problem boils down to a binary-constrained quadratic optimization problem, which is strongly non-convex. In order to solve this problem, we resort to a novel approach, which is based on the penalized convex-concave procedure. The proposed approach can be implemented in an O-RAN cell-free mMIMO system as an xApp in the near-real time RIC (RAN intelligent Controller). Numerical results show the potential of this approach for dealing with joint precoding optimization and AP selection.
{"title":"Minimizing Power Consumption under SINR Constraints for Cell-Free Massive MIMO in O-RAN","authors":"Vaishnavi Kasuluru, Luis Blanco, Miguel Angel Vazquez, Cristian J. Vaca-Rubio, Engin Zeydan","doi":"arxiv-2409.04135","DOIUrl":"https://doi.org/arxiv-2409.04135","url":null,"abstract":"This paper deals with the problem of energy consumption minimization in Open\u0000RAN cell-free (CF) massive Multiple-Input Multiple-Output (mMIMO) systems under\u0000minimum per-user signal-to-noise-plus-interference ratio (SINR) constraints.\u0000Considering that several access points (APs) are deployed with multiple\u0000antennas, and they jointly serve multiple users on the same time-frequency\u0000resources, we design the precoding vectors that minimize the system power\u0000consumption, while preserving a minimum SINR for each user. We use a simple,\u0000yet representative, power consumption model, which consists of a fixed term\u0000that models the power consumption due to activation of the AP and a variable\u0000one that depends on the transmitted power. The mentioned problem boils down to\u0000a binary-constrained quadratic optimization problem, which is strongly\u0000non-convex. In order to solve this problem, we resort to a novel approach,\u0000which is based on the penalized convex-concave procedure. The proposed approach\u0000can be implemented in an O-RAN cell-free mMIMO system as an xApp in the\u0000near-real time RIC (RAN intelligent Controller). Numerical results show the\u0000potential of this approach for dealing with joint precoding optimization and AP\u0000selection.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183698","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}
Yongyi Miao, Zhongdang Li, Yang Wang, Die Hu, Jun Yan, Youfang Wang
In response to the rapid growth of global videomtraffic and the limitations of traditional wireless transmission systems, we propose a novel dual-stage vector quantization framework, VQ-DeepVSC, tailored to enhance video transmission over wireless channels. In the first stage, we design the adaptive keyframe extractor and interpolator, deployed respectively at the transmitter and receiver, which intelligently select key frames to minimize inter-frame redundancy and mitigate the cliff-effect under challenging channel conditions. In the second stage, we propose the semantic vector quantization encoder and decoder, placed respectively at the transmitter and receiver, which efficiently compress key frames using advanced indexing and spatial normalization modules to reduce redundancy. Additionally, we propose adjustable index selection and recovery modules, enhancing compression efficiency and enabling flexible compression ratio adjustment. Compared to the joint source-channel coding (JSCC) framework, the proposed framework exhibits superior compatibility with current digital communication systems. Experimental results demonstrate that VQ-DeepVSC achieves substantial improvements in both Multi-Scale Structural Similarity (MS-SSIM) and Learned Perceptual Image Patch Similarity (LPIPS) metrics than the H.265 standard, particularly under low channel signal-to-noise ratio (SNR) or multi-path channels, highlighting the significantly enhanced transmission capabilities of our approach.
{"title":"VQ-DeepVSC: A Dual-Stage Vector Quantization Framework for Video Semantic Communication","authors":"Yongyi Miao, Zhongdang Li, Yang Wang, Die Hu, Jun Yan, Youfang Wang","doi":"arxiv-2409.03393","DOIUrl":"https://doi.org/arxiv-2409.03393","url":null,"abstract":"In response to the rapid growth of global videomtraffic and the limitations\u0000of traditional wireless transmission systems, we propose a novel dual-stage\u0000vector quantization framework, VQ-DeepVSC, tailored to enhance video\u0000transmission over wireless channels. In the first stage, we design the adaptive\u0000keyframe extractor and interpolator, deployed respectively at the transmitter\u0000and receiver, which intelligently select key frames to minimize inter-frame\u0000redundancy and mitigate the cliff-effect under challenging channel conditions.\u0000In the second stage, we propose the semantic vector quantization encoder and\u0000decoder, placed respectively at the transmitter and receiver, which efficiently\u0000compress key frames using advanced indexing and spatial normalization modules\u0000to reduce redundancy. Additionally, we propose adjustable index selection and\u0000recovery modules, enhancing compression efficiency and enabling flexible\u0000compression ratio adjustment. Compared to the joint source-channel coding\u0000(JSCC) framework, the proposed framework exhibits superior compatibility with\u0000current digital communication systems. Experimental results demonstrate that\u0000VQ-DeepVSC achieves substantial improvements in both Multi-Scale Structural\u0000Similarity (MS-SSIM) and Learned Perceptual Image Patch Similarity (LPIPS)\u0000metrics than the H.265 standard, particularly under low channel signal-to-noise\u0000ratio (SNR) or multi-path channels, highlighting the significantly enhanced\u0000transmission capabilities of our approach.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183701","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}
The rapid evolution of mobile networks from 5G to 6G has necessitated the development of autonomous network management systems, such as Zero-Touch Networks (ZTNs). However, the increased complexity and automation of these networks have also escalated cybersecurity risks. Existing Intrusion Detection Systems (IDSs) leveraging traditional Machine Learning (ML) techniques have shown effectiveness in mitigating these risks, but they often require extensive manual effort and expert knowledge. To address these challenges, this paper proposes an Automated Machine Learning (AutoML)-based autonomous IDS framework towards achieving autonomous cybersecurity for next-generation networks. To achieve autonomous intrusion detection, the proposed AutoML framework automates all critical procedures of the data analytics pipeline, including data pre-processing, feature engineering, model selection, hyperparameter tuning, and model ensemble. Specifically, it utilizes a Tabular Variational Auto-Encoder (TVAE) method for automated data balancing, tree-based ML models for automated feature selection and base model learning, Bayesian Optimization (BO) for hyperparameter optimization, and a novel Optimized Confidence-based Stacking Ensemble (OCSE) method for automated model ensemble. The proposed AutoML-based IDS was evaluated on two public benchmark network security datasets, CICIDS2017 and 5G-NIDD, and demonstrated improved performance compared to state-of-the-art cybersecurity methods. This research marks a significant step towards fully autonomous cybersecurity in next-generation networks, potentially revolutionizing network security applications.
{"title":"Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection","authors":"Li Yang, Abdallah Shami","doi":"arxiv-2409.03141","DOIUrl":"https://doi.org/arxiv-2409.03141","url":null,"abstract":"The rapid evolution of mobile networks from 5G to 6G has necessitated the\u0000development of autonomous network management systems, such as Zero-Touch\u0000Networks (ZTNs). However, the increased complexity and automation of these\u0000networks have also escalated cybersecurity risks. Existing Intrusion Detection\u0000Systems (IDSs) leveraging traditional Machine Learning (ML) techniques have\u0000shown effectiveness in mitigating these risks, but they often require extensive\u0000manual effort and expert knowledge. To address these challenges, this paper\u0000proposes an Automated Machine Learning (AutoML)-based autonomous IDS framework\u0000towards achieving autonomous cybersecurity for next-generation networks. To\u0000achieve autonomous intrusion detection, the proposed AutoML framework automates\u0000all critical procedures of the data analytics pipeline, including data\u0000pre-processing, feature engineering, model selection, hyperparameter tuning,\u0000and model ensemble. Specifically, it utilizes a Tabular Variational\u0000Auto-Encoder (TVAE) method for automated data balancing, tree-based ML models\u0000for automated feature selection and base model learning, Bayesian Optimization\u0000(BO) for hyperparameter optimization, and a novel Optimized Confidence-based\u0000Stacking Ensemble (OCSE) method for automated model ensemble. The proposed\u0000AutoML-based IDS was evaluated on two public benchmark network security\u0000datasets, CICIDS2017 and 5G-NIDD, and demonstrated improved performance\u0000compared to state-of-the-art cybersecurity methods. This research marks a\u0000significant step towards fully autonomous cybersecurity in next-generation\u0000networks, potentially revolutionizing network security applications.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183731","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}
We introduce a novel weather-adaptive approach for multi-step forecasting of multi-scale SOP changes in aerial fiber links. By harnessing the discrete wavelet transform and incorporating weather data, our approach improves forecasting accuracy by over 65% in RMSE and 63% in MAPE compared to baselines.
{"title":"Weather-Adaptive Multi-Step Forecasting of State of Polarization Changes in Aerial Fibers Using Wavelet Neural Networks","authors":"Khouloud Abdelli, Matteo Lonardi, Jurgen Gripp, Samuel Olsson Fabien Boitier, Patricia Layec","doi":"arxiv-2409.03663","DOIUrl":"https://doi.org/arxiv-2409.03663","url":null,"abstract":"We introduce a novel weather-adaptive approach for multi-step forecasting of\u0000multi-scale SOP changes in aerial fiber links. By harnessing the discrete\u0000wavelet transform and incorporating weather data, our approach improves\u0000forecasting accuracy by over 65% in RMSE and 63% in MAPE compared to baselines.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183700","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}
Volunteer Edge-Cloud (VEC) computing has a significant potential to support scientific workflows in user communities contributing volunteer edge nodes. However, managing heterogeneous and intermittent resources to support machine/deep learning (ML/DL) based workflows poses challenges in resource governance for reliability, and confidentiality for model/data privacy protection. There is a need for approaches to handle the volatility of volunteer edge node availability, and also to scale the confidential data-intensive workflow execution across a large number of VEC nodes. In this paper, we present VECA, a reliable and confidential VEC resource clustering solution featuring three-fold methods tailored for executing ML/DL-based scientific workflows on VEC resources. Firstly, a capacity-based clustering approach enhances system reliability and minimizes VEC node search latency. Secondly, a novel two-phase, globally distributed scheduling scheme optimizes job allocation based on node attributes and using time-series-based Recurrent Neural Networks. Lastly, the integration of confidential computing ensures privacy preservation of the scientific workflows, where model and data information are not shared with VEC resources providers. We evaluate VECA in a Function-as-a-Service (FaaS) cloud testbed that features OpenFaaS and MicroK8S to support two ML/DL-based scientific workflows viz., G2P-Deep (bioinformatics) and PAS-ML (health informatics). Results from tested experiments demonstrate that our proposed VECA approach outperforms state-of-the-art methods; especially VECA exhibits a two-fold reduction in VEC node search latency and over 20% improvement in productivity rates following execution failures compared to the next best method.
{"title":"VECA: Reliable and Confidential Resource Clustering for Volunteer Edge-Cloud Computing","authors":"Hemanth Sai Yeddulapalli, Mauro Lemus Alarcon, Upasana Roy, Roshan Lal Neupane, Durbek Gafurov, Motahare Mounesan, Saptarshi Debroy, Prasad Calyam","doi":"arxiv-2409.03057","DOIUrl":"https://doi.org/arxiv-2409.03057","url":null,"abstract":"Volunteer Edge-Cloud (VEC) computing has a significant potential to support\u0000scientific workflows in user communities contributing volunteer edge nodes.\u0000However, managing heterogeneous and intermittent resources to support\u0000machine/deep learning (ML/DL) based workflows poses challenges in resource\u0000governance for reliability, and confidentiality for model/data privacy\u0000protection. There is a need for approaches to handle the volatility of\u0000volunteer edge node availability, and also to scale the confidential\u0000data-intensive workflow execution across a large number of VEC nodes. In this\u0000paper, we present VECA, a reliable and confidential VEC resource clustering\u0000solution featuring three-fold methods tailored for executing ML/DL-based\u0000scientific workflows on VEC resources. Firstly, a capacity-based clustering\u0000approach enhances system reliability and minimizes VEC node search latency.\u0000Secondly, a novel two-phase, globally distributed scheduling scheme optimizes\u0000job allocation based on node attributes and using time-series-based Recurrent\u0000Neural Networks. Lastly, the integration of confidential computing ensures\u0000privacy preservation of the scientific workflows, where model and data\u0000information are not shared with VEC resources providers. We evaluate VECA in a\u0000Function-as-a-Service (FaaS) cloud testbed that features OpenFaaS and MicroK8S\u0000to support two ML/DL-based scientific workflows viz., G2P-Deep (bioinformatics)\u0000and PAS-ML (health informatics). Results from tested experiments demonstrate\u0000that our proposed VECA approach outperforms state-of-the-art methods;\u0000especially VECA exhibits a two-fold reduction in VEC node search latency and\u0000over 20% improvement in productivity rates following execution failures\u0000compared to the next best method.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183702","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}
Laura Landon, Vipindev Adat Vasudevan, Jaeweon Kim, Junmo Sung, Jeffery Tony Masters, Muriel Médard
This paper presents several methods for minimizing packet service time in networks using 5G and beyond. We propose leveraging network coding alongside Hybrid Automatic Repeat reQuest (HARQ) to reduce service time as well as optimizing Modulation and Coding Scheme (MCS) selection based on the service time. Our network coding approach includes a method to increase the number of packets in flight, adhering to the current standard of the 16 HARQ process limit, demonstrating that these strategies can enhance throughput and reduce latency. Experimental results show that network coding reduces service times by up to 7% in low SNR regimes, with greater reduction across all SNR as the number of packets in flight increases, suggesting that future 6G standards should consider increasing the number of HARQ processes for better performance.
{"title":"Enhancing 5G Performance: Reducing Service Time and Research Directions for 6G Standards","authors":"Laura Landon, Vipindev Adat Vasudevan, Jaeweon Kim, Junmo Sung, Jeffery Tony Masters, Muriel Médard","doi":"arxiv-2409.02788","DOIUrl":"https://doi.org/arxiv-2409.02788","url":null,"abstract":"This paper presents several methods for minimizing packet service time in\u0000networks using 5G and beyond. We propose leveraging network coding alongside\u0000Hybrid Automatic Repeat reQuest (HARQ) to reduce service time as well as\u0000optimizing Modulation and Coding Scheme (MCS) selection based on the service\u0000time. Our network coding approach includes a method to increase the number of\u0000packets in flight, adhering to the current standard of the 16 HARQ process\u0000limit, demonstrating that these strategies can enhance throughput and reduce\u0000latency. Experimental results show that network coding reduces service times by\u0000up to 7% in low SNR regimes, with greater reduction across all SNR as the\u0000number of packets in flight increases, suggesting that future 6G standards\u0000should consider increasing the number of HARQ processes for better performance.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183734","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}
David Adei, Varun Madathil, Sathvik Prasad, Bradley Reaves, Alessandra Scafuro
Unsolicited telephone calls that facilitate fraud or unlawful telemarketing continue to overwhelm network users and the regulators who prosecute them. The first step in prosecuting phone abuse is traceback -- identifying the call originator. This fundamental investigative task currently requires hours of manual effort per call. In this paper, we introduce J"ager, a distributed secure call traceback system. J"ager can trace a call in a few seconds, even with partial deployment, while cryptographically preserving the privacy of call parties, carrier trade secrets like peers and call volume, and limiting the threat of bulk analysis. We establish definitions and requirements of secure traceback, then develop a suite of protocols that meet these requirements using witness encryption, oblivious pseudorandom functions, and group signatures. We prove these protocols secure in the universal composibility framework. We then demonstrate that J"ager has low compute and bandwidth costs per call, and these costs scale linearly with call volume. J"ager provides an efficient, secure, privacy-preserving system to revolutionize telephone abuse investigation with minimal costs to operators.
{"title":"Jäger: Automated Telephone Call Traceback","authors":"David Adei, Varun Madathil, Sathvik Prasad, Bradley Reaves, Alessandra Scafuro","doi":"arxiv-2409.02839","DOIUrl":"https://doi.org/arxiv-2409.02839","url":null,"abstract":"Unsolicited telephone calls that facilitate fraud or unlawful telemarketing\u0000continue to overwhelm network users and the regulators who prosecute them. The\u0000first step in prosecuting phone abuse is traceback -- identifying the call\u0000originator. This fundamental investigative task currently requires hours of\u0000manual effort per call. In this paper, we introduce J\"ager, a distributed\u0000secure call traceback system. J\"ager can trace a call in a few seconds, even\u0000with partial deployment, while cryptographically preserving the privacy of call\u0000parties, carrier trade secrets like peers and call volume, and limiting the\u0000threat of bulk analysis. We establish definitions and requirements of secure\u0000traceback, then develop a suite of protocols that meet these requirements using\u0000witness encryption, oblivious pseudorandom functions, and group signatures. We\u0000prove these protocols secure in the universal composibility framework. We then\u0000demonstrate that J\"ager has low compute and bandwidth costs per call, and\u0000these costs scale linearly with call volume. J\"ager provides an efficient,\u0000secure, privacy-preserving system to revolutionize telephone abuse\u0000investigation with minimal costs to operators.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183739","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}
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