FRADIS - A Machine Learning-based Multipath Solution for Differentiated Services in a Network Slicing-enhanced Delivery Environment

Aug 5, 2026

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The growth of wearable and IoT devices is driving a rapid increase in data generation and creating new demands that existing single technologies and providers cannot satisfy. To address this, FRADIS proposes a programmable, flexible 5G-and-beyond architecture centered on Network Slicing, enabling isolated logical slices with service-specific functionality. FRADIS integrates machine learning to select optimized approaches spanning traffic engineering and transport/protocol mechanisms, supporting adaptive delivery for many service types. The described architecture targets lower-layer traffic control via NetSli, transport-layer dynamic protocol behavior, and application-layer ML-based content adaptation, with preliminary results indicating improved flexibility for rich-media applications.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Abstract

Introduction

Data growth and diverse traffic demands

Network slicing as a programmable infrastructure concept

FRADIS framework overview and architecture intent

FRADIS: A Machine Learning-based Multipath Solution for Differentiated Services in a Network Slicing-enhanced Delivery Environment Anderson Augusto Simiscuka∗ , Abid Yaqoob§ and Gabriel-Miro Muntean∗∗ School of Electronic Engineering, Dublin City University, Dublin, Ireland E-mails:∗[andersonaugusto.simiscuka@dcu.ie](andersonaugusto.simiscuka@dcu.ie),§[abid.yaqoob@dcu.ie](abid.yaqoob@dcu.ie),∗∗ gabriel.muntean@dcu.ie Abstract—The increase in number of devices per person driven by the latest wearable and IoT devices poses challenges for network support. Despite advancements like IEEE 802.11ax and 5G New Radio, no single technology or provider can handle the growing data surge and diverse service demands. To meet this demand, a programmable and flexible network infrastructure is essential, supporting various technologies and adopting a software-based architecture with open interfaces. A key concepts in achieving this is Network Slicing (NetSli). In this context, the FRAmework for performance-aware Differentiated Innovative Services (FRADIS) was designed as a comprehensive framework for the 5G and beyond heterogeneous network environment, aiming to facilitate the differentiated delivery of services with diverse requirements. FRADIS integrates machine learning to optimize service-specific approaches, choosing between infrastructure-dependent (traffic engineering) and protocol-based solutions. The framework targets a wide range of services, including smart city monitoring, e-health information, emergency messages, infotainment, targeted advertisements, IoT and sensor data, road traffic navigation, agriculture monitoring, and touristic virtual reality. This paper describes the architecture of FRADIS, aiming to achieve traffic control through NetSli at lower network layers, employing a dynamic traffic characteristics-oriented protocol at the transport layer, and using machine learning for adaptive content delivery at the application layer. Preliminary results indicate the benefits of the proposed framework and its flexibility to support multiple types of rich-media applications. Index Terms—Network Slicing, MPTCP, Machine Learning I. INTRODUCTION Globally, there is a significant rise in the generation and exchange of data. Cisco’s projection [1] anticipated that global Internet traffic would surpass 350 Exabytes per month (4.8 Zettabytes per year) by the end of 2022 . However, IDC reported that in 2020 alone, 64.2 Zettabytes of data were created or replicated, mostly transmitted over existing networks [2] . Cisco, IDC, and Ericsson observed a diversification of traffic types and an increase in traffic requirements, attributed to the growing number of high-end mobile devices and user consumption of bandwidth-intensive services like HD, 4K, UHD, and 360° videos. Ericsson estimates that video will constitute over 77% of traffic by 2026 [3] . The increase in the number of devices per person, from 2.4 to 3.6 in 2022, is also driven by the inclusion of wearable devices and IoT devices, expected to have reached 28 billion in 2023 [1] . Despite advancements such as IEEE 802.11ax for wireless broadband and 3GPP 5G New Radio for cellular networks, no single network technology or provider can handle the anticipated surge in data and support services with diverse characteristics. Relying solely on entirely new technologies for 5G networks is unrealistic. The proponents argue that achieving the vision of 5G networks and services requires a combination of innovative solutions and diverse technologies [4] . This places pressure on researchers to find solutions that can support the latest applications with diverse requirements, while balancing conflicting user demands for high-quality services and industry and societal needs for performance and energy efficiency. Enabling applications with distinct service requirements necessitates a programmable and flexible network infrastructure that can be utilized by various network technologies

FAQs

What problem does FRADIS address in next-generation networks?

FRADIS targets the inability of a single network technology or provider to handle surging data from wearables and IoT while meeting diverse service requirements.

How does Network Slicing (NetSli) contribute to FRADIS?

NetSli partitions a shared physical network into independently controlled slices, isolating functionality per slice and enabling service-specific tailoring.

Where does FRADIS apply machine learning in the delivery pipeline?

FRADIS uses machine learning to optimize service-specific approaches, selecting between infrastructure-dependent traffic engineering and protocol-based solutions, and to support adaptive content delivery at the application layer.

Aug 5, 2026

Upload by Aria