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检索条件"任意字段=Conference on Scalability and Traffic Control in IP Networks"

共 5923 条记录，以下是21-30 订阅

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Multi-controller Architecture Design and Features in Software - Defined Networking (SDN)

Multi-Controller Architecture Design and Features in Softwar...

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Mobile Computing and Sustainable Informatics (ICMCSI), International conference on

作者： V. Prabhu P. Balamurugan Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamilnadu India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamilnadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Networking paradigm known as “Software-Defined Networking” (SDN) offers more flexibility with network management and is fast gaining popularity. Separating the control plane from the data plane is largely responsible for this success. In SDN-enabled networks, a centralized controller is used to translate high-level network application requirements into low-level directives that are issued to the forwarding devices. Such the controller requires limited ability to react to the request for flow processing generated by forwarding devices. Whenever an SDN-enabled network expands in terms of size or as a network traffic rises, flow processing demands can't be able to fulfill by a single centralized controller. This causes a controller's performance to decline, which eventually results in a malfunctioning SDN-enabled network. Implementing a multi-controller architecture, in which a number of controllers cooperate to manage a sizable amount of network traffic and flow processing requirements, would be a successful strategy. The introduction of a multi-controller architecture and a consideration of the problems with its scalability, coherence as well as accessibility make up the core contribution of this study. Additionally, it identifies areas where study in this area could still be done.

关键词： Performance evaluation scalability Process control Computer architecture Telecommunication traffic Coherence Software Reliability Software defined networking Mobile computing

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Network traffic Prediction using Deep Learning Models: Reducing Latency in High-Demand Scenarios

Network Traffic Prediction using Deep Learning Models: Reduc...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International conference on

作者： B.J. Job Karuna Sagar Divya M V Namita Kale Neetu Anand Kumar Gaurav Shrividya Shashidhara Department of CSE Joginpally B.R Engineering College Hyderabad Telangana Department of Computer Science East West Institute of Technology Bangalore India Department of Information Technology MET'S Institute Of Engineering Nashik India Department of Computer Applications Maharaja Surajmal Institute Delhi India Department of CSE BMS Institute of Technology and Management Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

The availability of the internet and enhanced applications, more specifically, the utilization of more devices in networks, and improved computational applications has created complexity in the control of network traffic. Network traffic forecasting is an essential component in low-latency networks, as well as in real-time applications that deserve excellent service quality and efficient resource allocation. This study therefore focuses on identifying whether deep learning models can further improve the accuracy in forecasting traffic flows in a network. Therefore, with the help of LSTM networks and Convolutional Neural networks, the work will build temporal dependencies and spatial correlations of network traffic data. There are major and minor adaptations to some classic methodologies that conform the proposed framework; moreover, it is applied to two real datasets to highlight the capacity of the model in anticipating traffic congestion with a higher level of accuracy than the traditional methodologies. Evaluation of models in dynamic network conditions is done through common parameters such as prediction accuracy, lowered latency, and scalability. Saved models show that deep learning-based solutions reduce latency at high loads much more effectively, which will help network administrators in finding efficient solutions. This study demonstrate how deep learning can aid in the prediction of traffics, and the manner in which it is changing the efficiency and effectiveness of modern-day network systems. It makes the foundation for improved understanding of traffic patterns in order to apply it to optimize future generation of communication networks.

关键词： Deep learning Adaptation models Accuracy Telecommunication traffic Predictive models Throughput Convolutional neural networks Forecasting Long short term memory Load modeling

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QaSAL: QoS-aware State-Augmented Learnable Algorithms for Coexistence of 5G NR-U/Wi-Fi

QaSAL: QoS-aware State-Augmented Learnable Algorithms for Co...

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Annual conference on Information Sciences and Systems (CISS)

作者： Mohammad Reza Fasihi Brian L. Mark Dept. of Electrical and Computer Engineering and Wireless Cyber Center George Mason University Fairfax Virginia United States

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

With the increasing demand for wireless connectivity, ensuring the efficient coexistence of multiple radio access technologies in shared unlicensed spectrum has become an important issue. This paper focuses on optimizing Medium Access control (MAC) parameters to enhance the coexistence of 5G New Radio in Unlicensed Spectrum (NR-U) and Wi-Fi networks operating in unlicensed spectrum with multiple priority classes of traffic that may have varying quality-of-service (QoS) requirements. In this context, we tackle the coexistence parameter management problem by introducing a QoS-aware State-Augmented Learnable (QaSAL) framework, designed to improve network performance under various traffic conditions. Our approach augments the state representation with constraint information, enabling dynamic policy adjustments to enforce QoS requirements effectively. Simulation results validate the effectiveness of QaSAL in managing NR-U and Wi-Fi coexistence, demonstrating improved channel access fairness while satisfying a latency constraint for high-priority traffic.

关键词： Training Heuristic algorithms Wireless networks Simulation scalability Quality of service Telecommunication traffic Wireless fidelity Tuning Next generation networking

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Deep Reinforcement Learning for Optimizing Route Planning in Urban traffic

Deep Reinforcement Learning for Optimizing Route Planning in...

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Computational, Communication and Information Technology (ICCCIT), International conference on

作者： Mudit Mittal Archana Sehgal Neeraj Varshney Sunil Prashanth Kumar Nandini Shirish Boob R. Akhilesh Reddy Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Lovely Professional University Phagwara Department of Computer Engineering and Applications GLA University Mathura Department of Mechanical Engineering New Horizon College of Engineering Bangalore Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Urban traffic networks are efficient route design problems due to the intricacy and dynamic character of contemporary cities. Conventional methods hamper real time adjustment to continually changing traffic conditions, thereby providing suboptimal routes, a greater burden on the roadways, and greater air pollution. This thesis presents a novel deep reinforcement learning (DRL) method to optimize the route planning for urban traffic networks. With this rendering of real time traffic data, we use DRL to make autonomous decisions to reduce travel time and congestion of routes. One advantage of using sophisticated neural networks for feature extraction and policy learning is that, since the model is so adaptable to changing traffic patterns, the model guarantees that. And, in comparison to common routing algorithms, the results of simulations using real world traffic data show that this method is able to greatly enhance route efficiency and overall traffic flow management. The suggested DRL based system is feasible as a smart city program based on intelligent transportation system which can scale to grand metropolitan regions. The research presents that DRL can enhance the commuter run experience, reduce the environmental effect, and alter urban transportation. Future study will also investigate how this system can be better optimized when integrated with traffic infrastructure enabled by the Internet of Things.

关键词： Adaptation models Smart cities Computational modeling scalability Transportation traffic control Deep reinforcement learning Real-time systems Planning Internet of Things

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Optical Cross-Connect Technologies for Flexible Optical networks

Optical Cross-Connect Technologies for Flexible Optical Netw...

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Automation and Computation (AUTOCOM), International conference on

作者： Prateek Aggarwal Sireesha Nanduri Poonguzhali S B.P. Singh Satya Ranjan Das Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Various optical cross-connect technologies are being developed for flexible next-generation optical networks to ensure the efficiency of real-time optical network routing. Demand for larger bandwidth and faster data transmission continues to grow. Therefore, these networks are becoming more and more significant. Conventional optical cross-connects offer limited scalability, and they need to be more flexible by nature due to their traditional architecture, hindering fast adaptation to altering traffic patterns. A solution to this problem is the new OXC technologies, which allow dynamic and reconfigurable optical networks. These technologies use high-end optics and electronics, including wavelength-selective switches and coherent receivers, to offer routing and switching flexibility. One key benefit of flexible OXC technologies is that their capabilities to support different transmission formats and data rates cater well to specific service requisites. They also allow for greater implementation of software-defined networking principles, creating centralized control and management over the network. Moreover, flexible OXCs enable efficient tower best practices and O&M savings through reduced manual network intervention.

关键词： Optical fibers Optical switches scalability Bandwidth Optical fiber networks Wavelength division multiplexing Optical receivers Real-time systems Network function virtualization Software defined networking

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Detection of Malicious and Abnormal Users for Policy Enforcement in a Zero Trust Network

Detection of Malicious and Abnormal Users for Policy Enforce...

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Emerging Systems and Intelligent Computing (ESIC), International conference on

作者： Jayashree Rana Pramod Kumar Meher Rojalina Priyadarshini Department of Computer Science and Engineering C. V. Raman Global University Bhubaneswar Odisha India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The zero-trust security model is based on the philosophy of distrust and performs strict identity verification for every user/device attempting to access the system resources. It maintains strict access control and grants the least access privilege to minimize the risk of unauthorized access and data breaches. Users authorized to access any resource in a zero-trust system, are subjected to continuous verification. Although continuous monitoring is crucial for zero-trust systems, there is no literature on its implementation. In this paper, we show that the policy-enforcement-point (PEP) of a zero-trust system could be integrated with an intrusion detection system like Snort to help it with continuous monitoring tasks. Snort is an open-source tool that can capture and log the ip addresses of both the source and destination in any data packet in the network traffic. By packet inspection and analysis, Snort can detect malicious and suspicious activities. We have demonstrated how appropriate Snort alerts can be generated and integrated with a PEP to identify malicious and abnormal users for policy enforcement in a zero-trust system.

关键词： Access control Philosophical considerations Authentication Intrusion detection Telecommunication traffic Inspection Real-time systems Zero Trust ip networks Monitoring

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AI-Driven traffic Congestion Management: A Predictive Analytics Approach for Smart Cities

AI-Driven Traffic Congestion Management: A Predictive Analyt...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Saurabh Pratap Singh Rathore Yousef Farhaoui Ebuka Emmanuel Aniebonam Tapsi Nagpal Thanuja M Priyanka Kaushik Dept. of Management & Research ICAPSR New Delhi India Dept. of Computer Science Moulay Ismail University Errachidia Morocco Dept. of Business University Marshall MN Dept. of Computer Science & Engineering Lingayas Vidyapeeth Haryana India Sai Vidya Institute of Technology Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Rapid urbanization, increased vehicle usage, and the growth of developing cities have resulted in severe traffic congestion, leading to economic losses, environmental harm, and a decline in quality of life. Traditional traffic management systems tend to be reactive, focusing on addressing traffic flow issues after they occur, rather than proactively managing them. To tackle this challenge, this paper proposes an AI-driven system designed to predict and manage traffic congestion. The system leverages continuous traffic data from IoT devices, such as images, GPS, and inductive loop sensors, to monitor real-time traffic conditions. traffic predictions are made using a combination of CNN and LSTM networks. Additionally, Geographic Information System (GIS) technology is integrated for spatial analysis and dynamic traffic control, enabling real-time traffic light adjustments and providing drivers with alternative routes to avoid congestion. Preliminary experiments show that intelligent traffic management systems can significantly improve traffic flow and reduce congestion in large cities. The paper also explores challenges related to data privacy, scalability, and future advancements, including the integration of self-driving vehicles and the application of reinforcement learning. This system offers a practical solution for alleviating congestion and optimizing urban transportation systems.

关键词： Smart cities Reinforcement learning traffic control Real-time systems Sensor systems Internet of Things Vehicle dynamics traffic congestion Long short term memory Geographic information systems

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Advancements in Pedestrian and Vehicle Detection: A Comprehensive Review of Deep Learning Techniques

Advancements in Pedestrian and Vehicle Detection: A Comprehe...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Pranavsinh Thakor Bharati Dixit MITWPU Pune

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Detecting pedestrians and vehicles is a critical task in applications such as traffic management, surveillance, and autonomous driving. Recent advancements in deep learning, particularly Convolutional Neural networks (CNNs), have significantly improved object detection, enabling revolutionary progress in the field. This paper reviews state-of-the-art techniques for pedestrian and vehicle detection, tracing their evolution from traditional feature-based approaches to advanced deep learning architectures such as YOLO, SSD, and R-CNN. Key datasets, including COCO, KITTI, and Cityscapes, are examined for their relevance in training and evaluating these models, alongside performance metrics like Mean Average Precision (mAP) and Intersection over Union (IoU). The paper identifies key challenges, such as detecting small and occluded objects, achieving real-time performance, and ensuring transferability across diverse domains. It emphasizes the importance of addressing scalability issues and deployment constraints, particularly for embedded systems and resource-limited environments. Emerging technologies, including attention mechanisms and transformer-based models, are explored for their potential to enhance detection capabilities. By integrating discussions on practical applications and real-world deployment, this paper bridges the gap between theoretical advancements and industry needs. It provides insights into future research directions, such as multi-modal data fusion and robust algorithms, to inspire innovation in pedestrian and vehicle detection systems.

关键词： Deep learning YOLO Training Technological innovation Pedestrians Accuracy Surveillance scalability traffic control Autonomous vehicles

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Quantum External Border Gateway Protocol as a classic high-level control plane for Quantum Internet

Quantum External Border Gateway Protocol as a classic high-l...

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Argentine conference on Electronics (CAE)

作者： Leandro Delgado José Canelón Claudia M. Sarris L. Facundo Caram Facultad de Ingeniería y Ciencias Agrarias Universidad Católica Argentina Buenos Aires Argentina Profesor Emérito Universidad Del Zulia Venezuela Docente E-Learning UTN Buenos Aires Argentina Departamento de Matemática Facultad de Ingeniería Universidad de Buenos Aires Buenos Aires Argentina Departamento de Electrónica Facultad de Ingeniería Universidad de Buenos Aires Buenos Aires Argentina

ISBN: (数字)9798331522636

ISBN: (纸本)9798331522643

The rapid advancement of quantum networking technologies requires a robust and scalable control plane to effectively manage the complexities of quantum communication. This paper presents a novel framework designed to enhance the scalability of the control plane in Quantum Internet architectures. The proposed solution integrates a hierarchical control structure that leverages the Border Gateway protocol (BGP) for adaptive slow high level control plane and a fast low control with virtual circuits in the network in or between AS boundaries (external Quantum BGP), ensuring efficient data quantum transmission while minimizing latency. Additionally, we implement a decentralized approach that enables individual AS to make localized decisions (using quantum control over MPLS, RuleSet, Segment Routing, SDN, etc), thus reducing the burden on central controllers. This work provides a foundational step towards realizing a scalable and efficient Quantum Internet, paving the way for future research and development in quantum communication systems.

关键词： Quantum entanglement Autonomous systems scalability Circuits traffic control Routing Internet Border Gateway Protocol Quantum networks Research and development

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A2P: A Scalable OFDMA Polling Algorithm for Time-Sensitive Wi-Fi networks

A2P: A Scalable OFDMA Polling Algorithm for Time-Sensitive W...

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IEEE conference on Wireless Communications and Networking

作者： Douglas Dziedzorm Agbeve Andrey Belogaev Wim Sandra Carl Lylon Jeroen Famaey IDLab University of Antwerp - IMEC Belgium Televic Group Belgium

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Over the years, advancements such as increased bandwidth, new modulation and coding schemes, frame aggregation, and the use of multiple antennas have been employed to enhance Wi-Fi performance. Nonetheless, as network density and the demand for low-latency applications increases, contention delays and retransmissions have become obstacles to efficient Wi-Fi deployment in modern scenarios. The introduction of Orthogonal Frequency-Division Multiple Access (OFDMA) in the IEEE 802.11 standard allows simultaneous transmissions to and from multiple users within the same transmission opportunity, thereby reducing the contention. However, the AP must efficiently manage the resource allocation, particularly in uplink scenarios where it lacks prior knowledge of users' buffer statuses, thus making polling a critical bottleneck in networks with a high number of users with sporadic traffic pattern. This paper addresses the polling problem and introduces the A2P algorithm, designed to enable scalable and efficient polling in high-density OFDMA-based time sensitive Wi-Fi networks. Simulation results show that A2P outperforms the alternative schemes by maintaining significantly lower delay and packet loss in dense time-sensitive teleconferencing scenario.

关键词： Teleconferencing Simulation scalability Packet loss Bandwidth traffic control Delays Resource management Uplink Wireless fidelity

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