IEEE Transactions on Network and Service Management
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Year: 2024 | Volume: 21 | Issue: 1
Want More WANs? Comparison of Traditional and GAN-Based Generation of Wide Area Network Topologies via Graph and Performance Metrics
Publication Year: 2024,Page(s):4 - 19
Cited by: Papers (1)
Wide Area Network (WAN) research benefits from the availability of realistic network topologies, e.g., as input to simulations, emulators, or testbeds. With the rise of Machine Learning (ML) and particularly Deep Learning (DL) methods, this demand for topologies, which can be used as training data, is greater than ever. However, public datasets are limited, thus, it is promising to generate synthe...Show More
A Fair Sharing Approach for Micro-Services Function Chains Placement in Ultra-Low Latency Services
Publication Year: 2024,Page(s):20 - 34
Cited by: Papers (1)
The evolution of Internet services, such as drone piloting or metaverse, tends toward ultra-Low Latency (LL) requirements, thus inducing new challenges in the placement of Service Function Chains (SFCs) deployed over virtualized infrastructures. On the one hand, this challenge requires novel optimization means. In particular, decomposing monolithic Network Functions (NFs) into micro-services acts ...Show More
Software Defined Layer 4.5 Customization for Agile Network Operation
Publication Year: 2024,Page(s):35 - 50
Protocol customizations primarily come in two forms: those driven by public extensions to open standard protocols; and dialecting and performance tuning driven by an enterprise network’s private security and performance needs. Current deployment of protocol customizations is mostly ad hoc, through manual configuration or script programs that are highly specialized to each customization. This metho...Show More
Benchmarking Class Incremental Learning in Deep Learning Traffic Classification
Giampaolo Bovenzi;Alfredo Nascita;Lixuan Yang;Alessandro Finamore;Giuseppe Aceto;Domenico Ciuonzo;Antonio Pescapé;Dario Rossi
Publication Year: 2024,Page(s):51 - 69
Cited by: Papers (12)
Traffic Classification (TC) is experiencing a renewed interest, fostered by the growing popularity of Deep Learning (DL) approaches. In exchange for their proved effectiveness, DL models are characterized by a computationally-intensive training procedure that badly matches the fast-paced release of new (mobile) applications, resulting in significantly limited efficiency of model updates. To addres...Show More
Unavailability-Aware Backup Allocation Model Based on Two-Stage Shared Protection for Middleboxes
Publication Year: 2024,Page(s):70 - 87
Cited by: Papers (1)
Middleboxes work as software which runs on a general-purpose server by adopting network function virtualization. The unavailability of middlebox is a key metric. The previous study considers allocating backup servers to middleboxes to reduce the unavailability. While it adopts shared protection to save backup capacity, resource sharing has not been sufficiently explored as each middlebox can only ...Show More
RT-CBCH: Real-Time VPN Traffic Service Identification Based on Sampled Data in High-Speed Networks
Publication Year: 2024,Page(s):88 - 107
Cited by: Papers (2)
Virtual Private Network (VPN) technology can bypass censorship and access geographically locked services. Some harmful information may be hidden in VPN traffic and circumvent the surveillance systems, bringing a significant challenge to network security. Considering the increasing richness of service types in VPN traffic, identifying traffic service facilitates further targeting harmful VPN traffi...Show More
DeepLS: Local Search for Network Optimization Based on Lightweight Deep Reinforcement Learning
Nicola Di Cicco;Memedhe Ibrahimi;Sebastian Troia;Massimo Tornatore
Publication Year: 2024,Page(s):108 - 119
Cited by: Papers (3)
Deep Reinforcement Learning (DRL) is being investigated as a competitive alternative to traditional techniques for solving network optimization problems. A promising research direction lies in enhancing traditional optimization algorithms by offloading low-level decisions to a DRL agent. In this study, we consider how to effectively employ DRL to improve the performance of Local Search algorithms,...Show More
Stable QoE-Aware Multi-SFCs Cooperative Routing Mechanism Based on Deep Reinforcement Learning
Publication Year: 2024,Page(s):120 - 131
Cited by: Papers (3)
The explosive development of the Internet of Things (IOT) has stimulated the sudden and dynamic demand pattern of potential Service Function Chain (SFC) traffic. The virtual network built based on the requirements of Multiple SFCs (Multi-SFCs), sharing the resource, should provide better Quality of Experience (QoE) for Multi-SFCs according to the real-time network status. However, simultaneous Mul...Show More
Joint Dataset Reconstruction and Power Control for Distributed Training in D2D Edge Network
Publication Year: 2024,Page(s):132 - 147
The intrinsic nature of non-independent and identically distributed datasets on heterogeneous devices slows down the distributed model training process and reduces the training accuracy. To settle this problem, we propose a dataset reconstruction scheme to transform the data distribution of training device’s dataset into independent and identically distributed dataset via data exchange among trust...Show More
Routing, Channel, Key-Rate, and Time-Slot Assignment for QKD in Optical Networks
Qiaolun Zhang;Omran Ayoub;Alberto Gatto;Jun Wu;Francesco Musumeci;Massimo Tornatore
Publication Year: 2024,Page(s):148 - 160
Cited by: Papers (12)
Quantum Key Distribution (QKD) is currently being explored as a solution to the threats posed to current cryptographic protocols by the evolution of quantum computers and algorithms. However, single-photon quantum signals used for QKD permit to achieve key rates strongly limited by link performance (e.g., loss and noise) and propagation distance, especially in multi-node QKD networks, making it ne...Show More
Performance of Spatially Diverse URLLC and eMBB Traffic in Cell Free Massive MIMO Environments
Publication Year: 2024,Page(s):161 - 173
Cited by: Papers (4)
5G networks are designed to support both high data rate eMBB services, and low-latency high-reliability URLLC services. Multiplexing these two services on the same wireless frequency band leads to a challenging radio resource allocation problem due to their heterogeneous requirements. Usually, multiplexing is based on the puncturing mechanism by URLLC traffic at the expense of the eMBB throughput’...Show More
Proactive VNF Scaling and Placement in 5G O-RAN Using ML
Publication Year: 2024,Page(s):174 - 186
Cited by: Papers (7)
5G networks are expected to support various services and applications with more stringent latency, reliability, and bandwidth requirements than previous generations. Open Radio Access Networks (O-RAN) have been proposed to meet these requirements. The O-RAN Alliance assumes O-RAN components to be Virtualized Network Functions (VNFs). Furthermore, O-RAN allows employing Machine Learning (ML) soluti...Show More
Robust and Reliable SFC Placement in Resource-Constrained Multi-Tenant MEC-Enabled Networks
Dimitrios Michael Manias;Ibrahim Shaer;Joe Naoum-Sawaya;Abdallah Shami
Publication Year: 2024,Page(s):187 - 199
Cited by: Papers (3)
With the rapid development and incoming implementation of 5G networks, many use cases, such as Intelligent Transportation Systems (ITS), are being realized. Utilizing networking technologies, including Network Function Virtualization and Mobile Edge Computing, along with 5G network slicing, the Next-Generation Service Placement Problem (NGSPP) is gaining significant attention due to the criticalit...Show More
Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs
Fahri Wisnu Murti;Samad Ali;George Iosifidis;Matti Latva-aho
Publication Year: 2024,Page(s):200 - 216
Cited by: Papers (5)
Virtualized Radio Access Networks (vRANs) are fully configurable and can be implemented at a low cost over commodity platforms to enable network management flexibility. In this paper, a novel vRAN reconfiguration problem is formulated to jointly reconfigure the functional splits of the base stations (BSs), locations of the virtualized central units (vCUs) and distributed units (vDUs), their resour...Show More
Leveraging Real-World Data Sets for QoE Enhancement in Public Electric Vehicles Charging Networks
Mohamed Elhattab;Maurice Khabbaz;Nassr Al-Dahabreh;Ribal Atallah;Chadi Assi
Publication Year: 2024,Page(s):217 - 231
Cited by: Papers (3)
This work targets enhancing the quality of charging experience in Electric Vehicle (EV) Public Charging Infrastructure (PCI) networks. The estimation uncertainty of waiting times at charging stations (CSs) hinders the proliferation of such networks and, hence, decelerates EV adoption. Currently, most EV owners prefer to use private chargers; thus, overloading the energy distribution network leavin...Show More
Optimized Planning of DU/CU Placement and Flow Routing in 5G Packet Xhaul Networks
Publication Year: 2024,Page(s):232 - 248
Cited by: Papers (3)
Packet-switched Xhaul networks (PXNs) have been proposed as a cost-effective and scalable solution for provisioning of connectivity between densely located radio antenna (remote) units (RU) and distributed (DU) / central (CU) processing units in 5G Radio Access Networks (RANs). A PXN enables statistical multiplexing of different data flows, such as fronthaul, midhaul, and backhaul flows, and their...Show More
Efficient Remote Entanglement Distribution in Quantum Networks: A Segment-Based Method
Zhonghui Li;Jian Li;Kaiping Xue;David S. L. Wei;Nenghai Yu;Qibin Sun;Jun Lu
Publication Year: 2024,Page(s):249 - 265
Cited by: Papers (1)
Entanglement distribution between distant quantum nodes plays an essential role in realizing quantum networks’ capabilities. In addition to path selection, remote entanglement distribution involves two pivotal quantum operations, i.e., entanglement generation and entanglement swapping. The existing studies mainly adopt two methods, i.e., Tell-and-Generation (TAG) and Tell-and-Swapping (TAS), to ma...Show More
A Bilateral Game Approach for Task Outsourcing in Multi-Access Edge Computing
Zhao Tong;Xin Deng;Zheng Xiao;Dan He;Anthony Theodore Chronopoulos;Schahram Dustdar
Publication Year: 2024,Page(s):266 - 279
Cited by: Papers (3)
Multi-access edge computing (MEC) is a promising architecture to provide low-latency applications for future Internet of Things (IoT)-based network systems. Together with the increasing scholarly attention on task offloading, the problem of servers’ resource allocation has been widely studied. The limited computational resources of edge servers (ESs) cannot meet the different demands of terminal e...Show More
Multi-UAV-Assisted Federated Learning for Energy-Aware Distributed Edge Training
Jianhang Tang;Jiangtian Nie;Yang Zhang;Zehui Xiong;Wenchao Jiang;Mohsen Guizani
Publication Year: 2024,Page(s):280 - 294
Cited by: Papers (8)
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has largely extended the border and capacity of artificial intelligence of things (AIoT) by providing a key element for enabling flexible distributed data inputs, computing capacity, and high mobility. To enhance data privacy for AIoT applications, federated learning (FL) is becoming a potential solution to perform training tasks l...Show More
Load Profiling via In-Band Flow Classification and P4 With Howdah
Antonino Angi;Alessio Sacco;Flavio Esposito;Guido Marchetto;Alexander Clemm
Publication Year: 2024,Page(s):295 - 309
Data center traffic management challenges increase with the complexity and variety of new Internet and Web applications. Efficient network management systems are often needed to thwart delays and minimize failures. In this regard, it seems helpful to identify in advance the different classes of flows that (co)exist in the network, characterizing them into different types based on different latency...Show More
Transfer Learning for Real-Time Surface Defect Detection With Multi-Access Edge-Cloud Computing Networks
Hui Li;Xiuhua Li;Qilin Fan;Qingyu Xiong;Xiaofei Wang;Victor C. M. Leung
Publication Year: 2024,Page(s):310 - 323
Cited by: Papers (4)
The development of deep learning and edge computing provides rapid detection capability for surface defects. However, components produced in actual industrial manufacturing environments often have tiny surface defects and training data for each specific defect type is limited. Meanwhile, network resources at the edge of industrial networks are difficult to guarantee. It is challenging to train a p...Show More
UAV Base Station Network Transmission-Based Reverse Auction Mechanism for Digital Twin Utility Maximization
Publication Year: 2024,Page(s):324 - 340
Cited by: Papers (5)
Digital twin (DT) technology uses Internet of Things (IoT) devices to collect real-world data and build a virtual world in the DT cloud. However, many IoT devices collect data in harsh natural environments, and these data cannot be transmitted through fixed base stations. Thus, many DT services adopt dynamic data transmission methods, such as transmission through unmanned aerial vehicle base stati...Show More
Playing With a Multi Armed Bandit to Optimize Resource Allocation in Satellite-Enabled 5G Networks
Antonio Galli;Vincenzo Moscato;Simon Pietro Romano;Giancarlo Sperlí
Publication Year: 2024,Page(s):341 - 354
Cited by: Papers (4)
In this paper, we address issues associated with the effective management of handover events in satellite-enabled 5G network infrastructures. Namely, we devise a strategy for dynamically allocating 5G gNB available resources in the presence of a constellation of LEO satellites, based on several parameters collected and dispatched by an ad hoc orchestration platform. We propose to leverage a Combin...Show More
DR-PIFO: A Dynamic Ranking Packet Scheduler Using a Push-In-First-Out Queue
Mostafa Elbediwy;Bill Pontikakis;Alireza Ghaffari;Jean-Pierre David;Yvon Savaria
Publication Year: 2024,Page(s):355 - 371
Cited by: Papers (2)
Software-defined Networking (SDN) introduced the decoupling of control and data forwarding planes. Despite advances in the programmability of SDNs, there remains a strong need for a fully programmable packet scheduler in the data plane. In this context, the ability to adapt to various traffic patterns and the expressiveness of schedulers are of paramount importance. This paper introduces the Dynam...Show More
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The University of Western Ontario, Department of Computer Science
London
Canada
hananlutfi@gmail.comContact Information
Editor-in-Chief
The University of Western Ontario, Department of Computer Science
London
Canada
hananlutfi@gmail.comIEEE Account
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