Tootfinder

This https://arxiv.org/abs/2403.03492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Joint User Association and Resource Allocation for Tailored QoS Provisioning in 6G HetNets
The proliferation of wireless-enabled applications with divergent quality of service (QoS) requirements necessitates tailored QoS provisioning. With the growing complexity of wireless infrastructures, application-specific QoS perceived by a user equipment (UE) is jointly determined by its association with the supporting base station in heterogeneous networks (HetNets) and the amount of resource allocated to it. However, conventional application-agnostic objective-based user association and reso…

A Survey on Security of Ultra/Hyper Reliable Low Latency Communication: Recent Advancements, Challenges, and Future Directions
Annapurna Pradhan, Susmita Das, Md. Jalil Piran, Zhu Han
https://arxiv.org/abs/2404.08160

A Survey on Security of Ultra/Hyper Reliable Low Latency Communication: Recent Advancements, Challenges, and Future Directions
Ultra-reliable low latency communication (URLLC) is an innovative service offered by fifth-generation (5G) wireless systems. URLLC enables various mission-critical applications by facilitating reliable and low-latency signal transmission to support extreme Quality of Service (QoS) requirements. Apart from reliability and latency, ensuring secure data transmission for URLLC has been a prominent issue for researchers in recent years. Using finite blocklength signals to achieve the stringent relia…

QACM: QoS-Aware xApp Conflict Mitigation in Open RAN
Abdul Wadud, Fatemeh Golpayegani, Nima Afraz
https://arxiv.org/abs/2405.07324 https://

IDEAS: Information-Driven EV Admission in Charging Station Considering User Impatience to Improve QoS and Station Utilization
Animesh Chattopadhyay, Subrat Kar
https://arxiv.org/abs/2403.06223

IDEAS: Information-Driven EV Admission in Charging Station Considering User Impatience to Improve QoS and Station Utilization
Our work delves into user behaviour at Electric Vehicle(EV) charging stations during peak times, particularly focusing on how impatience drives balking (not joining queues) and reneging (leaving queues prematurely). We introduce an Agent-based simulation framework that incorporates user optimism levels (pessimistic, standard, and optimistic) in the queue dynamics. Unlike previous work, this framework highlights the crucial role of human behaviour in shaping station efficiency for peak demand. T…

Multi-Step Traffic Prediction for Multi-Period Planning in Optical Networks
Hafsa Maryam, Tania Panayiotou, Georgios Ellinas
https://arxiv.org/abs/2404.08314

Multi-Step Traffic Prediction for Multi-Period Planning in Optical Networks
A multi-period planning framework is proposed that exploits multi-step ahead traffic predictions to address service overprovisioning and improve adaptability to traffic changes, while ensuring the necessary quality-of-service (QoS) levels. An encoder-decoder deep learning model is initially leveraged for multi-step ahead prediction by analyzing real-traffic traces. This information is then exploited by multi-period planning heuristics to efficiently utilize available network resources while min…

This https://arxiv.org/abs/2309.03628 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

OSMOSIS: Enabling Multi-Tenancy in Datacenter SmartNICs
Multi-tenancy is essential for unleashing SmartNIC's potential in datacenters. Our systematic analysis in this work shows that existing on-path SmartNICs have resource multiplexing limitations. For example, existing solutions lack multi-tenancy capabilities such as performance isolation and QoS provisioning for compute and IO resources. Compared to standard NIC data paths with a well-defined set of offloaded functions, unpredictable execution times of SmartNIC kernels make conventional approach…

Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach
Yi\u{g}it Berkay Uslu, Roya Doostnejad, Alejandro Ribeiro, Navid NaderiAlizadeh
https://arxiv.org/abs/2405.05748

Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach
Network slicing is a key feature in 5G/NG cellular networks that creates customized slices for different service types with various quality-of-service (QoS) requirements, which can achieve service differentiation and guarantee service-level agreement (SLA) for each service type. In Wi-Fi networks, there is limited prior work on slicing, and a potential solution is based on a multi-tenant architecture on a single access point (AP) that dedicates different channels to different slices. In this pa…

This https://arxiv.org/abs/2405.03101 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Double Self-Sustainable Reconfigurable Intelligent Surfaces Aided Wireless Communications
A double self-sustainable reconfigurable intelligent surfaces (RISs) assisted multi-user multiple input multiple output (MIMO) system is investigated. Two RISs are equipped with energy harvesting circuit to achieve self-sustainable transmission. The aim is to minimize the transmission power at the base station (BS), while guaranteeing the quality of service (QoS) requirements of the users and meeting the power consumption requirements of the RISs. A block coordinate descent (BCD) algorithm base…

Enabling 5G QoS configuration capabilities for IoT applications on container orchestration platform
Yu Liu, Aitor Hernandez Herranz
https://arxiv.org/abs/2403.05686

Enabling 5G QoS configuration capabilities for IoT applications on container orchestration platform
Container orchestration platform is the foundation of modern cloud infrastructure. In recent years, container orchestration platform has been evolving to cross the boundary of device, edge, and cloud. More and more IoT applications such as robotics and XR have been deployed across the device-cloud continuum through the container orchestration platform, e.g., the Kubernetes (K8s) framework. Meanwhile, the rapid expansion of advanced communication technologies like 5G has endorsed the revolution …

This https://arxiv.org/abs/2403.02093 has been replaced.
link: https://scholar.google.com/scholar?q=a

Daedalus: Self-Adaptive Horizontal Autoscaling for Resource Efficiency of Distributed Stream Processing Systems
Distributed Stream Processing (DSP) systems are capable of processing large streams of unbounded data, offering high throughput and low latencies. To maintain a stable Quality of Service (QoS), these systems require a sufficient allocation of resources. At the same time, over-provisioning can result in wasted energy and high operating costs. Therefore, to maximize resource utilization, autoscaling methods have been proposed that aim to efficiently match the resource allocation with the incoming…

Multi-Level ML Based Burst-Aware Autoscaling for SLO Assurance and Cost Efficiency
Chunyang Meng, Haogang Tong, Tianyang Wu, Maolin Pan, Yang Yu
https://arxiv.org/abs/2402.12962 <…

Multi-Level ML Based Burst-Aware Autoscaling for SLO Assurance and Cost Efficiency
Autoscaling is a technology to automatically scale the resources provided to their applications without human intervention to guarantee runtime Quality of Service (QoS) while saving costs. However, user-facing cloud applications serve dynamic workloads that often exhibit variable and contain bursts, posing challenges to autoscaling for maintaining QoS within Service-Level Objectives (SLOs). Conservative strategies risk over-provisioning, while aggressive ones may cause SLO violations, making it…

Joint User Association and Resource Allocation for Tailored QoS Provisioning in 6G HetNets
Yongqin Fu, Xianbin Wang
https://arxiv.org/abs/2403.03492 https:…

Joint User Association and Resource Allocation for Tailored QoS Provisioning in 6G HetNets
The proliferation of wireless-enabled applications with divergent quality of service (QoS) requirements necessitates tailored QoS provisioning. With the growing complexity of wireless infrastructures, application-specific QoS perceived by a user equipment (UE) is jointly determined by its association with the supporting base station in heterogeneous networks (HetNets) and the amount of resource allocated to it. However, conventional application-agnostic objective-based user association and reso…

This https://arxiv.org/abs/2401.00594 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Efficient Design for Multi-user Downlink Beamforming with Reconfigurable Intelligent Surface
This paper considers downlink multi-user transmission facilitated by a reconfigurable intelligent surface (RIS). First, focusing on the multi-group multicast beamforming scenario, we develop a fast and scalable algorithm for the joint base station (BS) and RIS beamforming optimization to minimize the transmit power subject to the user quality-of-service (QoS) constraints. By exploring the structure of this QoS problem, we show that the joint beamforming optimization can be naturally decomposed …

Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)
Masoud Shokrnezhad, Hao Yu, Tarik Taleb, Richard Li, Kyunghan Lee, Jaeseung Song, Cedric Westphal
https://arxiv.org/abs/2403.07573

Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)
In the context of advancing 6G, a substantial paradigm shift is anticipated, highlighting comprehensive everything-to-everything interactions characterized by numerous connections and stringent adherence to Quality of Service/Experience (QoS/E) prerequisites. The imminent challenge stems from resource scarcity, prompting a deliberate transition to Computing-Network Convergence (CNC) as an auspicious approach for joint resource orchestration. While CNC-based mechanisms have garnered attention, t…

Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)
Masoud Shokrnezhad, Hao Yu, Tarik Taleb, Richard Li, Kyunghan Lee, Jaeseung Song, Cedric Westphal
https://arxiv.org/abs/2403.07573

Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)
In the context of advancing 6G, a substantial paradigm shift is anticipated, highlighting comprehensive everything-to-everything interactions characterized by numerous connections and stringent adherence to Quality of Service/Experience (QoS/E) prerequisites. The imminent challenge stems from resource scarcity, prompting a deliberate transition to Computing-Network Convergence (CNC) as an auspicious approach for joint resource orchestration. While CNC-based mechanisms have garnered attention, t…

Jiagu: Optimizing Serverless Computing Resource Utilization with Harmonized Efficiency and Practicability
Qingyuan Liu, Yanning Yang, Dong Du, Yubin Xia, Ping Zhang, Jia Feng, James Larus, Haibo Chen
https://arxiv.org/abs/2403.00433

Jiagu: Optimizing Serverless Computing Resource Utilization with Harmonized Efficiency and Practicability
Current serverless platforms struggle to optimize resource utilization due to their dynamic and fine-grained nature. Conventional techniques like overcommitment and autoscaling fall short, often sacrificing utilization for practicability or incurring performance trade-offs. Overcommitment requires predicting performance to prevent QoS violation, introducing trade-off between prediction accuracy and overheads. Autoscaling requires scaling instances in response to load fluctuations quickly to red…

This https://arxiv.org/abs/2311.05189 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

An Analytical Model for Coordinated Multi-Satellite Joint Transmission System
Satellite communication is one of the key technologies that is enabling next-generation networks. However, nearest-satellite-supported downlink transmission may not meet a user's requirements due to limited signal strength, especially in emergent scenarios. In this paper, we investigate a coordinated multi-satellite joint transmission system from a system-level perspective, where a user can be served by multiple satellites to improve its quality-of-service (QoS). Furthermore, we analyze the cov…

ReinWiFi: A Reinforcement-Learning-Based Framework for the Application-Layer QoS Optimization of WiFi Networks
Qianren Li, Bojie Lv, Yuncong Hong, Rui Wang
https://arxiv.org/abs/2405.03526

ReinWiFi: A Reinforcement-Learning-Based Framework for the Application-Layer QoS Optimization of WiFi Networks
In this paper, a reinforcement-learning-based scheduling framework is proposed and implemented to optimize the application-layer quality-of-service (QoS) of a practical wireless local area network (WLAN) suffering from unknown interference. Particularly, application-layer tasks of file delivery and delay-sensitive communication, e.g., screen projection, in a WLAN with enhanced distributed channel access (EDCA) mechanism, are jointly scheduled by adjusting the contention window sizes and applica…

This https://arxiv.org/abs/2402.12777 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Application of Quantum Extreme Learning Machines for QoS Prediction of Elevators' Software in an Industrial Context
Quantum Extreme Learning Machine (QELM) is an emerging technique that utilizes quantum dynamics and an easy-training strategy to solve problems such as classification and regression efficiently. Although QELM has many potential benefits, its real-world applications remain limited. To this end, we present QELM's industrial application in the context of elevators, by proposing an approach called QUELL. In QUELL, we use QELM for the waiting time prediction related to the scheduling software of ele…

Service Level Agreements and Security SLA: A Comprehensive Survey
Serena Nicolazzo, Antonino Nocera, Witold Pedrycz
https://arxiv.org/abs/2405.00009 https:…

Service Level Agreements and Security SLA: A Comprehensive Survey
A Service Level Agreement (SLA) is a formal contract between a service provider and a consumer, representing a crucial instrument to define, manage, and maintain relationships between these two parties. The SLA's ability to define the Quality of Service (QoS) expectations, standards, and accountability helps to deliver high-quality services and increase client confidence in disparate application domains, such as Cloud computing and the Internet of Things. An open research direction in this cont…

Intent-Aware DRL-Based Uplink Dynamic Scheduler for 5G-NR
Salwa Mostafa, Mateus P. Mota, Alvaro Valcarce, Mehdi Bennis
https://arxiv.org/abs/2403.18364 htt…

Intent-Aware DRL-Based Uplink Dynamic Scheduler for 5G-NR
We investigate the problem of supporting Industrial Internet of Things user equipment (IIoT UEs) with intent (i.e., requested quality of service (QoS)) and random traffic arrival. A deep reinforcement learning (DRL) based centralized dynamic scheduler for time-frequency resources is proposed to learn how to schedule the available communication resources among the IIoT UEs. The proposed scheduler leverages an RL framework to adapt to the dynamic changes in the wireless communication system and t…

A Comprehensive Evaluation of the Impact of ATM QoS Mechanisms on Network Performance for Multimedia and Data Applications
Mahdi Manavi
https://arxiv.org/abs/2403.19914

A Comprehensive Evaluation of the Impact of ATM QoS Mechanisms on Network Performance for Multimedia and Data Applications
The Asynchronous Transfer Mode (ATM) network is crucial due to its ability to efficiently transmit data, provide reliable connections, and support various service classes with specific Quality of Service (QoS) requirements. In this paper, we utilize the OPNET network simulation software to model an ATM network and analyze the impact of QoS classification on network performance. We investigate the effects of Constant Bit Rate (CBR), Variable Bit Rate (VBR), Available Bit Rate (ABR) and Unspecifi…

Prioritized Multi-Tenant Traffic Engineering for Dynamic QoS Provisioning in Autonomous SDN-OpenFlow Edge Networks
Mohammad Sajid Shahriar, Faisal Ahmed, Genshe Chen, Khanh D. Pham, Suresh Subramaniam, Motoharu Matsuura, Hiroshi Hasegawa, Shih-Chun Lin
https://arxiv.org/abs/2403.15975

Prioritized Multi-Tenant Traffic Engineering for Dynamic QoS Provisioning in Autonomous SDN-OpenFlow Edge Networks
This letter indicates the critical need for prioritized multi-tenant quality-of-service (QoS) management by emerging mobile edge systems, particularly for high-throughput beyond fifth-generation networks. Existing traffic engineering tools utilize complex functions baked into closed, proprietary infrastructures, largely limiting design flexibility, scalability, and adaptiveness. Hence, this study introduces a software-defined networking (SDN)-based dynamic QoS provisioning scheme that prioritiz…

This https://arxiv.org/abs/1507.01546 has been replaced.
link: https://scholar.google.com/scholar?q=a

Autonomic Cloud Computing: Research Perspective
As the cloud infrastructure grows, it becomes more challenging to manage resources in such a massive, diverse, and distributed setting, despite the fact that cloud computing provides computational capabilities on-demand. Due to resource variability and unpredictability, resource allocation issues arise in a cloud setting. A Quality of Service (QoS) based autonomic resource management strategy automates resource management, delivering trustworthy, dependable, and cost-effective cloud services th…

Rate-Splitting Multiple Access for Transmissive Reconfigurable Intelligent Surface Transceiver Empowered ISAC System
Ziwei Liu, Wen Chen, Qingqing Wu, Jinhong Yuan, Shanshan Zhang, Zhendong Li, Jun Li
https://arxiv.org/abs/2402.12127

Rate-Splitting Multiple Access for Transmissive Reconfigurable Intelligent Surface Transceiver Empowered ISAC System
In this paper, a novel transmissive reconfigurable intelligent surface (TRIS) transceiver empowered integrated sensing and communications (ISAC) system is proposed for future multi-demand terminals. To address interference management, we implement rate-splitting multiple access (RSMA), where the common stream is independently designed for the sensing service. We introduce the sensing quality of service (QoS) criteria based on this structure and construct an optimization problem with the sensing…

Seer: Proactive Revenue-Aware Scheduling for Live Streaming Services in Crowdsourced Cloud-Edge Platforms
Shaoyuan Huang, Zheng Wang, Zhongtian Zhang, Heng Zhang, Xiaofei Wang, Wenyu Wang
https://arxiv.org/abs/2402.14619

Seer: Proactive Revenue-Aware Scheduling for Live Streaming Services in Crowdsourced Cloud-Edge Platforms
As live streaming services skyrocket, Crowdsourced Cloud-edge service Platforms (CCPs) have surfaced as pivotal intermediaries catering to the mounting demand. Despite the role of stream scheduling to CCPs' Quality of Service (QoS) and throughput, conventional optimization strategies struggle to enhancing CCPs' revenue, primarily due to the intricate relationship between resource utilization and revenue. Additionally, the substantial scale of CCPs magnifies the difficulties of time-intensive sc…

Integrating UAV-Enabled Base Stations in 3D Networks: QoS-Aware Joint Fronthaul and Backhaul Design
Salim Janji, Piotr Wawrzyniak, Piotr Formanowicz, Adrian Kliks
https://arxiv.org/abs/2404.17547

Integrating UAV-Enabled Base Stations in 3D Networks: QoS-Aware Joint Fronthaul and Backhaul Design
The emerging concept of 3D networks, integrating terrestrial, aerial, and space layers, introduces a novel and complex structure characterized by stations relaying backhaul loads through point-to-point wireless links, forming a wireless 3D backhaul mesh. A key challenge is the strategic placement of aerial platform such as drone base stations (DBSs), considering the locations and service demands of ground nodes and the connectivity to backhaul gateway nodes for core network access. This paper a…

This https://arxiv.org/abs/2311.17347 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Data-driven Bandwidth Adaptation for Radio Access Network Slices
The need to satisfy the QoS requirements of multiple network slices deployed at the same base station poses a major challenge to network operators. The problem becomes even harder when the desired QoS involves packet delays. In that case, network utility maximization is not directly applicable since the utilities of the slices are unknown. As a result, most related works learn online the utilities of all slices and how to split the resources among them. Unfortunately, this approach does not sca…

This https://arxiv.org/abs/2307.09691 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Joint Service Caching, Communication and Computing Resource Allocation in Collaborative MEC Systems: A DRL-based Two-timescale Approach
Meeting the strict Quality of Service (QoS) requirements of terminals has imposed a signiffcant challenge on Multiaccess Edge Computing (MEC) systems, due to the limited multidimensional resources. To address this challenge, we propose a collaborative MEC framework that facilitates resource sharing between the edge servers, and with the aim to maximize the long-term QoS and reduce the cache switching cost through joint optimization of service caching, collaborative offfoading, and computation a…

Cross-Cluster Networking to Support Extended Reality Services
Theodoros Theodoropoulos, Luis Rosa, Abderrahmane Boudi, Tarik Zakaria Benmerar, Antonios Makris, Tarik Taleb, Luis Cordeiro, Konstantinos Tserpes, JaeSeung Song
https://arxiv.org/abs/2405.00558

Cross-Cluster Networking to Support Extended Reality Services
Extented Reality (XR) refers to a class of contemporary services that are intertwined with a plethora of rather demanding Quality of Service (QoS) and functional requirements. Despite Kubernetes being the de-facto standard in terms of deploying and managing contemporary containerized microservices, it lacks adequate support for cross-cluster networking, hindering service-to-service communication across diverse cloud domains. Although there are tools that may be leveraged alongside Kubernetes in…

Multi-User Multi-Application Packet Scheduling for Application-Specific QoE Enhancement Based on Knowledge-Embedded DDPG in 6G RAN
Yongqin Fu, Xianbin Wang
https://arxiv.org/abs/2405.01007

Multi-User Multi-Application Packet Scheduling for Application-Specific QoE Enhancement Based on Knowledge-Embedded DDPG in 6G RAN
The rapidly growing diversity of concurrent applications from both different users and same devices calls for application-specific Quality of Experience (QoE) enhancement of future wireless communications. Achieving this goal relies on application-specific packet scheduling, as it is vital for achieving tailored QoE enhancement by realizing the application-specific Quality of Service (QoS) requirements and for optimal perceived QoE values. However, the intertwining diversified QoE perception me…

An FPTAS for Shortest-Longest Path Problem
Jianwei Zhang
https://arxiv.org/abs/2404.13488 https://arxiv.org/pdf/2404.13488

An FPTAS for Shortest-Longest Path Problem
Motivated by multi-domain Service Function Chain (SFC) orchestration, we define the Shortest-Longest Path (SLP) problem, prove its hardness, and design an efficient Fully Polynomial Time Approximation Scheme (FPTAS) using the scaling and rounding technique to compute an approximation solution with provable performance guarantee. The SLP problem and its solution algorithm have theoretical significance in multicriteria optimization and also have application potential in QoS routing and multi-doma…

Open Datasets for AI-Enabled Radio Resource Control in Non-Terrestrial Networks
Husnain Shahid, Miguel Angel Vazquez, Laurent Reynaud, Fanny Parzysz, Musbah Shaat
https://arxiv.org/abs/2404.12813

Open Datasets for AI-Enabled Radio Resource Control in Non-Terrestrial Networks
By effectively implementing the strategies for resource allocation, the capabilities, and reliability of non-terrestrial networks (NTN) can be enhanced. This leads to enhance spectrum utilization performance while minimizing the unmet system capacity, meeting quality of service (QoS) requirements and overall system optimization. In turn, a wide range of applications and services in various domains can be supported. However, allocating resources in a multi-constellation system with heterogeneous…

Multi-Objective Offloading Optimization in MEC and Vehicular-Fog Systems: A Distributed-TD3 Approach
Frezer Guteta Wakgra, Binayak Kar, Seifu Birhanu Tadele, Shan-Hsiang Shen, Asif Uddin Khan
https://arxiv.org/abs/2404.12584

Multi-Objective Offloading Optimization in MEC and Vehicular-Fog Systems: A Distributed-TD3 Approach
The emergence of 5G networks has enabled the deployment of a two-tier edge and vehicular-fog network. It comprises Multi-access Edge Computing (MEC) and Vehicular-Fogs (VFs), strategically positioned closer to Internet of Things (IoT) devices, reducing propagation latency compared to cloud-based solutions and ensuring satisfactory quality of service (QoS). However, during high-traffic events like concerts or athletic contests, MEC sites may face congestion and become overloaded. Utilizing offlo…

This https://arxiv.org/abs/2401.10253 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Hybrid-Task Meta-Learning: A Graph Neural Network Approach for Scalable and Transferable Bandwidth Allocation
In this paper, we develop a deep learning-based bandwidth allocation policy that is: 1) scalable with the number of users and 2) transferable to different communication scenarios, such as non-stationary wireless channels, different quality-of-service (QoS) requirements, and dynamically available resources. To support scalability, the bandwidth allocation policy is represented by a graph neural network (GNN), with which the number of training parameters does not change with the number of users. …

This https://arxiv.org/abs/2401.10253 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Hybrid-Task Meta-Learning: A Graph Neural Network Approach for Scalable and Transferable Bandwidth Allocation
In this paper, we develop a deep learning-based bandwidth allocation policy that is: 1) scalable with the number of users and 2) transferable to different communication scenarios, such as non-stationary wireless channels, different quality-of-service (QoS) requirements, and dynamically available resources. To support scalability, the bandwidth allocation policy is represented by a graph neural network (GNN), with which the number of training parameters does not change with the number of users. …