Tootfinder

Energy-Efficient QoS-Aware Scheduling for S-NUCA Many-Cores
Sudam M. Wasala, Jurre Wolff, Yixian Shen, Anuj Pathania, Clemens Grelck, Andy D. Pimentel
https://arxiv.org/abs/2505.23351

Energy-Efficient QoS-Aware Scheduling for S-NUCA Many-Cores
Optimizing performance and energy efficiency in many-core processors, especially within Non-Uniform Cache Access (NUCA) architectures, remains a critical challenge. The performance heterogeneity inherent in S-NUCA systems complicates task scheduling due to varying cache access latencies across cores. This paper introduces a novel QoS management policy to maintain application execution within predefined Quality of Service (QoS) targets, measured using the Application Heartbeats framework. QoS me…

Dependability Theory-based Statistical QoS Provisioning of Fluid Antenna Systems
Irfan Muhammad, Priyadarshi Mukherjee, Wee Kiat New, Hirley Alves, Ioannis Krikidis, Kai-Kit Wong
https://arxiv.org/abs/2507.19984

Dependability Theory-based Statistical QoS Provisioning of Fluid Antenna Systems
Fluid antenna systems (FAS) have recently emerged as a promising technology for next-generation wireless networks, offering real-time spatial reconfiguration to enhance reliability, throughput, and energy efficiency. Nevertheless, existing studies often overlook the temporal dynamics of channel fading and their implications for mission-critical operations. In this paper, we propose a dependability-theoretic framework for statistical quality-of-service (QoS) provisioning of FAS under finite bloc…

DSROQ: Dynamic Scheduling and Routing for QoE Management in LEO Satellite Networks
Dhiraj Bhattacharjee, Pablo G. Madoery, Abhishek Naik, Halim Yanikomeroglu, Gunes Karabulut Kurt, Stephane Martel, Khaled Ahmed
https://arxiv.org/abs/2508.21047

DSROQ: Dynamic Scheduling and Routing for QoE Management in LEO Satellite Networks
The modern Internet supports diverse applications with heterogeneous quality of service (QoS) requirements. Low Earth orbit (LEO) satellite constellations offer a promising solution to meet these needs, enhancing coverage in rural areas and complementing terrestrial networks in urban regions. Ensuring QoS in such networks requires joint optimization of routing, bandwidth allocation, and dynamic queue scheduling, as traffic handling is critical for maintaining service performance. This paper for…

Multiple STAR-RISs-Empowered Multi-User Communications with Diversified QoS Provisioning
Junfeng Wang, Xiao Tang, Jinxin Liu, Zhi Zhai, Qinghe Du, Naijin Liu
https://arxiv.org/abs/2508.17769

Multiple STAR-RISs-Empowered Multi-User Communications with Diversified QoS Provisioning
This paper proposes a quality-of-service (QoS)-aware multi-user communication framework facilitated by multiple simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). The user groups are established based on their QoS requirements specified by the minimum data rate, which is provisioned by the optimized transmission and reflection configurations of the STAR-RISs. Particularly, we formulate an optimization problem to maximize the aggregate link rate across al…

QoS-based Intelligent multi-connectivity for B5G networks
Ali Parsa, Neda Moghim, Sachin Shetty
https://arxiv.org/abs/2508.16816 https://arxiv.org/pdf/2508…

QoS-based Intelligent multi-connectivity for B5G networks
The rapid advancement of communication technologies has established cellular networks as the backbone for diverse applications, each with distinct quality of service requirements. Meeting these varying demands within a unified infrastructure presents a critical challenge that can be addressed through advanced techniques such as multi-connectivity. Multiconnectivity enables User equipments to connect to multiple BSs simultaneously, facilitating QoS differentiation and provisioning. This paper pr…

QoS-aware Scheduling of Periodic Real-time Task Graphs on Heterogeneous Pre-occupied MECs
Ashutosh Shankar (Indian Institute Of Technology Kharagpur), Astha Kumari (Indian Institute Of Technology Kharagpur)
https://arxiv.org/abs/2506.12415

QoS-aware Scheduling of Periodic Real-time Task Graphs on Heterogeneous Pre-occupied MECs
In latency-sensitive applications, efficient task scheduling is crucial for maintaining Quality of Service (QoS) while meeting strict timing constraints. This paper addresses the challenge of scheduling periodic tasks structured as directed acyclic graphs (DAGs) within heterogeneous, pre-occupied Mobile Edge Computing (MEC) networks. We propose a modified version of the Heterogeneous Earliest Finish Time (HEFT) algorithm designed to exploit residual processing capacity in preoccupied MEC enviro…

QoS-Aware Integrated Sensing, Communication, and Control with Movable Antenna
Yike Wang, Zhike Wu, Jiang Chen, Chunjie Wang, Xuhui Zhang, Yanyan Shen
https://arxiv.org/abs/2508.07799

QoS-Aware Integrated Sensing, Communication, and Control with Movable Antenna
Integrated sensing, communication, and control (ISCC) has emerged as a key enabler for low-altitude wireless networks with enhanced adaptability through resource allocation co-design and intelligent environment awareness. However, dynamic interference and channel attenuation constrain the potential of the ISCC system. To address this challenge, we propose a novel movable antenna-empowered ISCC system. An achievable data rate maximization problem is formulated while guaranteeing the sensing and …

High Order Collaboration-Oriented Federated Graph Neural Network for Accurate QoS Prediction
Zehuan Chen, Xiangwei Lai
https://arxiv.org/abs/2507.05308 htt…

High Order Collaboration-Oriented Federated Graph Neural Network for Accurate QoS Prediction
Predicting Quality of Service (QoS) data crucial for cloud service selection, where user privacy is a critical concern. Federated Graph Neural Networks (FGNNs) can perform QoS data prediction as well as maintaining user privacy. However, existing FGNN-based QoS predictors commonly implement on-device training on scattered explicit user-service graphs, thereby failing to utilize the implicit user-user interactions. To address this issue, this study proposes a high order collaboration-oriented fe…

SDAP-based QoS Flow Multiplexing Support in Simu5G for 5G NR Simulation
Mohamed Seliem, Utz Roedig, Cormac Sreenan, Dirk Pesch
https://arxiv.org/abs/2508.12785 https://

SDAP-based QoS Flow Multiplexing Support in Simu5G for 5G NR Simulation
The Service Data Adaptation Protocol (SDAP) plays a central role in 5G New Radio (NR), acting as a bridge between the core and radio networks, by enabling QoS Flow multiplexing over shared Data Radio Bearers (DRBs). However, most 5G simulation frameworks, including the popular OMNet++-based Simu5G, lack SDAP support, limiting their ability to model realistic QoS behavior. This paper presents a modular, standardscompliant SDAP extension for Simu5G. The implementation includes core elements such …

PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence
Federico Mason, Tommaso Zugno, Matteo Drago, Marco Giordani, Mate Boban, Michele Zorzi
https://arxiv.org/abs/2507.14211

PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence
Predictive Quality of Service (PQoS) makes it possible to anticipate QoS changes, e.g., in wireless networks, and trigger appropriate countermeasures to avoid performance degradation. Hence, PQoS is extremely useful for automotive applications such as teleoperated driving, which poses strict constraints in terms of latency and reliability. A promising tool for PQoS is given by Reinforcement Learning (RL), a methodology that enables the design of decision-making strategies for stochastic optimiz…

LightCom: A Generative AI-Augmented Framework for QoE-Oriented Communications
Chunmei Xu, Siqi Zhang, Yi Ma, Rahim Tafazolli
https://arxiv.org/abs/2507.17352 https://

LightCom: A Generative AI-Augmented Framework for QoE-Oriented Communications
Data-intensive and immersive applications, such as virtual reality, impose stringent quality of experience (QoE) requirements that challenge traditional quality of service (QoS)-driven communication systems. This paper presents LightCom, a lightweight encoding and generative AI (GenAI)-augmented decoding framework, designed for QoE-oriented communications under low signal-to-noise ratio (SNR) conditions. LightCom simplifies transmitter design by applying basic low-pass filtering for source codi…

Sharing is Caring: Analysis of Hybrid Network Sharing Strategies for Energy Efficient Multi-Operator Cellular Systems
Laura Finarelli, Maoquan Ni, Michela Meo, Falko Dressler, Gianluca Rizzo
https://arxiv.org/abs/2508.19130

Sharing is Caring: Analysis of Hybrid Network Sharing Strategies for Energy Efficient Multi-Operator Cellular Systems
This paper introduces a novel analytical framework for evaluating energy-efficient, QoS-aware network-sharing strategies in cellular networks. Leveraging stochastic geometry, our framework enables the systematic assessment of network performance across a range of sharing paradigms, including both conventional single-operator scenarios and advanced hybrid strategies that enable full integration and cooperation among multiple mobile network operators. Our framework incorporates diverse user densi…

Adaptive AI Agent Placement and Migration in Edge Intelligence Systems
Xingdan Wang, Jiayi He, Zhiqing Tang, Jianxiong Guo, Jiong Lou, Liping Qian, Tian Wang, Weijia Jia
https://arxiv.org/abs/2508.03345

Adaptive AI Agent Placement and Migration in Edge Intelligence Systems
The rise of LLMs such as ChatGPT and Claude fuels the need for AI agents capable of real-time task handling. However, migrating data-intensive, multi-modal edge workloads to cloud data centers, traditionally used for agent deployment, introduces significant latency. Deploying AI agents at the edge improves efficiency and reduces latency. However, edge environments present challenges due to limited and heterogeneous resources. Maintaining QoS for mobile users necessitates agent migration, which …

Adaptive 6G Networks-in-Network Management for Industrial Applications
Daniel Lindenschmitt, Paul Seehofer, Marius Schmitz, Jan Mertes, Roland Bless, Martina Zitterbart, Jan C. Aurich, Hans D. Schotten
https://arxiv.org/abs/2508.18902

Adaptive 6G Networks-in-Network Management for Industrial Applications
This paper presents the application of Dynamic Spectrum Management (DSM) for future 6G industrial networks, establishing an efficient controller for the Networks-in-Network (NiN) concept. The proposed architecture integrates nomadic as well as static sub-networks (SNs with diverse Quality of Service (QoS) requirements within the coverage area of an overlayer network, managed by a centralized spectrum manager (SM). Control plane connectivity between the SNs and the DSM is ensured by the self-org…

Experience-Centric Resource Management in ISAC Networks: A Digital Agent-Assisted Approach
Xinyu Huang, Yixiao Zhang, Yingying Pei, Jianzhe Xue, Xuemin Shen
https://arxiv.org/abs/2507.06436

Experience-Centric Resource Management in ISAC Networks: A Digital Agent-Assisted Approach
In this paper, we propose a digital agent (DA)-assisted resource management scheme for enhanced user quality of experience (QoE) in integrated sensing and communication (ISAC) networks. Particularly, user QoE is a comprehensive metric that integrates quality of service (QoS), user behavioral dynamics, and environmental complexity. The novel DA module includes a user status prediction model, a QoS factor selection model, and a QoE fitting model, which analyzes historical user status data to cons…

Unified Interference-Aware Water-Filling for QoS-Constrained Communication, Sensing, and JRC
Ahmed Naeem, Anastassia Gharib, H\"useyin Arslan
https://arxiv.org/abs/2506.01400

Unified Interference-Aware Water-Filling for QoS-Constrained Communication, Sensing, and JRC
Water-filling (WF) algorithms are pivotal in maximizing capacity and spectral efficiency in multiple-input and multiple-output (MIMO) systems. However, traditional WF approaches cater solely to communication requirements, neglecting the emerging heterogeneity of 6G, including sensing and joint radar-communication (JRC). As these diverse demands grow in importance and have different Quality of Service (QoS) constraints, traditional WF becomes inadequate. Therefore, in this paper, we propose a un…

Quantum Oppenheimer-Snyder Black Hole with Quintessential Dark Energy and a String Clouds: Geodesics, Perturbative Dynamics, and Thermal Properties
Faizuddin Ahmed, Ahmad Al-Badawi, \.Izzet Sakall{\i}
https://arxiv.org/abs/2508.03202

Quantum Oppenheimer-Snyder Black Hole with Quintessential Dark Energy and a String Clouds: Geodesics, Perturbative Dynamics, and Thermal Properties
In this paper, we explore a deformed Schwarzschild black hole (BH) within a loop quantum gravity (LQG) framework incorporating both a quintessence field (QF) and a cloud of strings (CS), aiming to understand how these exotic fields collectively influence various physical phenomena in the BH's vicinity. We systematically investigate the quantum Oppenheimer--Snyder (QOS) spacetime by deriving the complete metric and analyzing horizon structures, showing significant modifications to the classical …

Enabling On-demand Guaranteed QoS for Real Time Video Streaming from Vehicles in 5G Advanced with CAPIF & NEF APIs
Pietro Piscione, Leonardo Lossi, Maziar Nekovee, Chathura Galkandage, Phil O Connor, Simon Davies
https://arxiv.org/abs/2508.09150

Enabling On-demand Guaranteed QoS for Real Time Video Streaming from Vehicles in 5G Advanced with CAPIF & NEF APIs
This paper presents the design and implementation of a Proof of Concept (PoC) that demonstrates how 5G Advanced Network Functions can be integrated with the Common API Framework (CAPIF) to support enhanced connectivity for automotive applications. The PoC shows the continuous monitoring of the mobile network performance and the on-demand and dynamic adaptation of Quality of Service (QoS) for selected 5G User Equipment (UE) video streaming traffic flows using standard 3GPP Network Exposure Funct…

QoS-aware State-Augmented Learnable Algorithm for Wireless Coexistence Parameter Management
Mohammad Reza Fasihi, Brian L. Mark
https://arxiv.org/abs/2506.22652

QoS-aware State-Augmented Learnable Algorithm for Wireless Coexistence Parameter Management
Efficient and fair coexistence in unlicensed spectrum is essential to support heterogeneous networks such as 5G NR-U and Wi-Fi, which often contend for shared wireless resources. We introduce a general framework for wireless Coexistence Parameter Management (CPM) based on state-augmented constrained reinforcement learning. We propose a novel algorithm, QaSAL-CPM, which incorporates state-augmentation by embedding the dual variables in the constrained optimization formulation directly into the a…

Cognitive-Radio Functionality: A Novel Configuration for STAR-RIS assisted RSMA Networks
Saeed Ibrahim, Yue Xiao, Dimitrios Tyrovolas, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, Zheng Ma, George K. Karagiannidis, Pinghzi Fan
https://arxiv.org/abs/2505.24583

Cognitive-Radio Functionality: A Novel Configuration for STAR-RIS assisted RSMA Networks
Cognitive radio rate-splitting multiple access (CR-RSMA) has emerged as a promising multiple access framework that can efficiently manage interference and adapt dynamically to heterogeneous quality-of-service (QoS) requirements. To effectively support such demanding access schemes, programmable wireless environments have attracted considerable attention, especially through simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), which can enable full-space con…

Data Scheduling Algorithm for Scalable and Efficient IoT Sensing in Cloud Computing
Noor Islam S. Mohammad
https://arxiv.org/abs/2508.04334 https://arxiv.o…

Data Scheduling Algorithm for Scalable and Efficient IoT Sensing in Cloud Computing
The rapid growth of Internet of Things (IoT) devices produces massive, heterogeneous data streams, demanding scalable and efficient scheduling in cloud environments to meet latency, energy, and Quality-of-Service (QoS) requirements. Existing scheduling methods often lack adaptability to dynamic workloads and network variability inherent in IoT-cloud systems. This paper presents a novel hybrid scheduling algorithm combining deep Reinforcement Learning (RL) and Ant Colony Optimization (ACO) to ad…

Achieving Fair-Effective Communications and Robustness in Underwater Acoustic Sensor Networks: A Semi-Cooperative Approach
Yu Gou, Tong Zhang, Jun Liu, Tingting Yang, Shanshan Song, Jun-Hong Cui
https://arxiv.org/abs/2508.07578

Achieving Fair-Effective Communications and Robustness in Underwater Acoustic Sensor Networks: A Semi-Cooperative Approach
This paper investigates the fair-effective communication and robustness in imperfect and energy-constrained underwater acoustic sensor networks (IC-UASNs). Specifically, we investigate the impact of unexpected node malfunctions on the network performance under the time-varying acoustic channels. Each node is expected to satisfy Quality of Service (QoS) requirements. However, achieving individual QoS requirements may interfere with other concurrent communications. Underwater nodes rely excessive…

This https://arxiv.org/abs/2506.02814 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Adaptive Configuration Selection for Multi-Model Inference Pipelines in Edge Computing
The growing demand for real-time processing tasks is driving the need for multi-model inference pipelines on edge devices. However, cost-effectively deploying these pipelines while optimizing Quality of Service (QoS) and costs poses significant challenges. Existing solutions often neglect device resource constraints, focusing mainly on inference accuracy and cost efficiency. To address this, we develop a framework for configuring multi-model inference pipelines. Specifically: 1) We model the de…

Adaptive Configuration Selection for Multi-Model Inference Pipelines in Edge Computing
Jinhao Sheng, Zhiqing Tang, Jianxiong Guo, Tian Wang
https://arxiv.org/abs/2506.02814

Adaptive Configuration Selection for Multi-Model Inference Pipelines in Edge Computing
The growing demand for real-time processing tasks is driving the need for multi-model inference pipelines on edge devices. However, cost-effectively deploying these pipelines while optimizing Quality of Service (QoS) and costs poses significant challenges. Existing solutions often neglect device resource constraints, focusing mainly on inference accuracy and cost efficiency. To address this, we develop a framework for configuring multi-model inference pipelines. Specifically: 1) We model the de…

TraGe: A Generic Packet Representation for Traffic Classification Based on Header-Payload Differences
Chungang Lin, Yilong Jiang, Weiyao Zhang, Xuying Meng, Tianyu Zuo, Yujun Zhang
https://arxiv.org/abs/2506.14151

TraGe: A Generic Packet Representation for Traffic Classification Based on Header-Payload Differences
Traffic classification has a significant impact on maintaining the Quality of Service (QoS) of the network. Since traditional methods heavily rely on feature extraction and large scale labeled data, some recent pre-trained models manage to reduce the dependency by utilizing different pre-training tasks to train generic representations for network packets. However, existing pre-trained models typically adopt pre-training tasks developed for image or text data, which are not tailored to traffic d…

Coexistence analysis of Wi-Fi 6E and 5G NR-U in the 6 GHz band
Navid Keshtiarast, Marina Petrova
https://arxiv.org/abs/2506.22844 https://

Coexistence analysis of Wi-Fi 6E and 5G NR-U in the 6 GHz band
The ever-increasing demand for broadband and IoT wireless connectivity has recently urged the regulators around the world to start opening the 6 GHz spectrum for unlicensed use. These bands will, for example, permit the use of additional 1.2 GHz in the US and 500 MHz in Europe for unlicensed radio access technologies (RATs) such as Wi-Fi and 5G New Radio Unlicensed (5G NR-U). To support QoS-sensitive applications with both technologies, fair and efficient coexistence approaches between the two …

Dynamic Preference Multi-Objective Reinforcement Learning for Internet Network Management
DongNyeong Heo, Daniela Noemi Rim, Heeyoul Choi
https://arxiv.org/abs/2506.13153

Dynamic Preference Multi-Objective Reinforcement Learning for Internet Network Management
An internet network service provider manages its network with multiple objectives, such as high quality of service (QoS) and minimum computing resource usage. To achieve these objectives, a reinforcement learning-based (RL) algorithm has been proposed to train its network management agent. Usually, their algorithms optimize their agents with respect to a single static reward formulation consisting of multiple objectives with fixed importance factors, which we call preferences. However, in pract…

RIDAS: A Multi-Agent Framework for AI-RAN with Representation- and Intention-Driven Agents
Kuiyuan Ding, Caili Guo, Yang Yang, Jianzhang Guo
https://arxiv.org/abs/2507.13140

RIDAS: A Multi-Agent Framework for AI-RAN with Representation- and Intention-Driven Agents
Sixth generation (6G) networks demand tight integration of artificial intelligence (AI) into radio access networks (RANs) to meet stringent quality of service (QoS) and resource efficiency requirements. Existing solutions struggle to bridge the gap between high level user intents and the low level, parameterized configurations required for optimal performance. To address this challenge, we propose RIDAS, a multi agent framework composed of representation driven agents (RDAs) and an intention dr…

Enhancing Open RAN Digital Twin Through Power Consumption Measurement
Ahmed Al-Tahmeesschi, Yi Chu, Josh Shackleton, Swarna Chetty, Mostafa Rahmani, David Grace, Hamed Ahmadi
https://arxiv.org/abs/2507.00928

Enhancing Open RAN Digital Twin Through Power Consumption Measurement
The increasing demand for high-speed, ultra-reliable and low-latency communications in 5G and beyond networks has led to a significant increase in power consumption, particularly within the Radio Access Network (RAN). This growing energy demand raises operational and sustainability challenges for mobile network operators, requiring novel solutions to enhance energy efficiency while maintaining Quality of Service (QoS). 5G networks are evolving towards disaggregated, programmable, and intelligen…

Real-Time In-Network Machine Learning on P4-Programmable FPGA SmartNICs with Fixed-Point Arithmetic and Taylor
Mohammad Firas Sada, John J. Graham, Mahidhar Tatineni, Dmitry Mishin, Thomas A. DeFanti, Frank W\"urthwein
https://arxiv.org/abs/2507.00428

Real-Time In-Network Machine Learning on P4-Programmable FPGA SmartNICs with Fixed-Point Arithmetic and Taylor
As machine learning (ML) applications become integral to modern network operations, there is an increasing demand for network programmability that enables low-latency ML inference for tasks such as Quality of Service (QoS) prediction and anomaly detection in cybersecurity. ML models provide adaptability through dynamic weight adjustments, making Programming Protocol-independent Packet Processors (P4)-programmable FPGA SmartNICs an ideal platform for investigating In-Network Machine Learning (IN…

On-Demand Multimedia Delivery in 6G: An Optimal-Cost Steiner Tree Approach
Zien Wang, Xiucheng Wang, Nan Cheng, Wenchao Xu, Wei Quan, Ruijin Sun, Conghao Zhou
https://arxiv.org/abs/2507.04589

On-Demand Multimedia Delivery in 6G: An Optimal-Cost Steiner Tree Approach
The exponential growth of multimedia data traffic in 6G networks poses unprecedented challenges for immersive communication, where ultra-high-definition, multi-quality streaming must be delivered on demand while minimizing network operational costs. Traditional routing approaches, such as shortest-path algorithms, fail to optimize flow multiplexing across multiple destinations, while conventional Steiner tree methods cannot accommodate heterogeneous quality-of-service (QoS) requirements-a criti…

PRIME: Plasticity-Robust Incremental Model for Encrypted Traffic Classification in Dynamic Network Environments
Tian Qin, Guang Cheng, Zihan Chen, Yuyang Zhou
https://arxiv.org/abs/2508.02031

PRIME: Plasticity-Robust Incremental Model for Encrypted Traffic Classification in Dynamic Network Environments
With the continuous development of network environments and technologies, ensuring cyber security and governance is increasingly challenging. Network traffic classification(ETC) can analyzes attributes such as application categories and malicious intent, supporting network management services like QoS optimization, intrusion detection, and targeted billing. As the prevalence of traffic encryption increases, deep learning models are relied upon for content-agnostic analysis of packet sequences. …

Autonomous Vision-Aided UAV Positioning for Obstacle-Aware Wireless Connectivity
Kamran Shafafi, Manuel Ricardo, Rui Campos
https://arxiv.org/abs/2506.23190

Autonomous Vision-Aided UAV Positioning for Obstacle-Aware Wireless Connectivity
Unmanned Aerial Vehicles (UAVs) offer a promising solution for enhancing wireless connectivity and Quality of Service (QoS) in urban environments, acting as aerial Wi-Fi access points or cellular base stations. Their flexibility and rapid deployment capabilities make them suitable for addressing infrastructure gaps and traffic surges. However, optimizing UAV positions to maintain Line of Sight (LoS) links with ground User Equipment (UEs) remains challenging in obstacle-dense urban scenarios. Th…

Towards an Optimized Multi-Cyclic Queuing and Forwarding in Time Sensitive Networking with Time Injection
Rubi Debnath, Mohammadreza Barzegaran, Sebastian Steinhorst
https://arxiv.org/abs/2506.22671

Towards an Optimized Multi-Cyclic Queuing and Forwarding in Time Sensitive Networking with Time Injection
Cyclic Queuing and Forwarding (CQF) is a Time-Sensitive Networking (TSN) shaping mechanism that provides bounded latency and deterministic Quality of Service (QoS). However, CQF's use of a single cycle restricts its ability to support TSN traffic with diverse timing requirements. Multi-Cyclic Queuing and Forwarding (Multi-CQF) is a new and emerging TSN shaping mechanism that uses multiple cycles on the same egress port, allowing it to accommodate TSN flows with varied timing requirements more e…