# Experimentation Scenarios for Machine Learning-Based Resource Management

Alexandros Kostopoulos, Ioannis P. Chochliouros, John Vardakas, Miquel Payaró, Sergio Barrachina, Md Arifur Rahman, Evgenii Vinogradov, Philippe Chanclou, Roberto Gonzalez, Charalambos Klitis, et al.

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## To cite this version:

Alexandros Kostopoulos, Ioannis P. Chochliouros, John Vardakas, Miquel Payaró, Sergio Barrachina, et al. Experimentation Scenarios for Machine Learning-Based Resource Management. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Heraklion, Greece. pp.120-133, 10.1007/978-3-031-08341-9_11. hal-05335620

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# Experimentation Scenarios for Machine Learning-Based Resource Management

Alexandros Kostopoulos¹(✉), Ioannis P. Chochliouros¹(✉), John Vardakas²,
Miquel Payaró³, Sergio Barrachina³, Md Arifur Rahman⁴, Evgenii Vinogradov⁵,
Philippe Chanclou⁶, Roberto Gonzalez⁷, Charalambos Klitis⁸,
Sabrina De Capitani di Vimercati⁹, Polyzois Soumplis¹⁰, Emmanuel Varvarigos¹⁰,
Dimitrios Kritharidis¹¹, and Kostas Chartsias¹¹

¹ Hellenic Telecommunications Organization (OTE) S.A., 99 Kifissias Avenue, 15124 Maroussi, Athens, Greece
alexkosto@oteresearch.gr

² Iquadrat Informatica SL, Barcelona, Spain

³ Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Barcelona, Spain

⁴ IS-Wireless, Piaseczno, Poland

⁵ Katholieke Universiteit Leuven, Leuven, Belgium

⁶ Orange S.A., Paris, France

⁷ NEC Laboratories Europe GmbH, Heidelberg, Germany

⁸ EBUS Technologies Limited, Nicosia, Cyprus

⁹ Universita degli Studi di Milano, Milan, Italy

¹⁰ Institute of Communications and Computer Systems (ICCS), Athens, Greece

¹¹ Intracom S.A. Telecom Solutions, Peania, Greece

**Abstract.** 5G changes the landscape of mobile networks profoundly, with an evolved architecture supporting unprecedented capacity, spectral efficiency, and increased flexibility. The MARSAL project targets the development and evaluation of a complete framework for the management and orchestration of network resources in 5G and beyond by utilizing a converged optical-wireless network infrastructure in the access and fronthaul/midhaul segments. In this paper, we present a conceptual view of the MARSAL architecture, as well as a wide range of experimentation scenarios.

**Keywords:** 5G · Cell-free (CF) · Distributed cloud · Network automation · Machine learning (ML) · Secure multi-tenancy

## 1 Introduction

5G mobile networks will be soon available to handle all types of applications and to provide services to massive numbers of users. In this complex and dynamic network ecosystem, an end-to-end performance analysis and optimisation will be key features in order to effectively manage the diverse requirements imposed by multiple vertical industries over the same shared infrastructure.
To enable such a vision, the MARSAL [1] EU-funded project targets the develop-
ment and evaluation of a complete framework for the management and orchestration of
network resources in 5G and beyond, by utilizing a converged optical-wireless network
infrastructure in the access and fronthaul/midhaul segments.

At the network design domain, MARSAL targets the development of novel cell-free (CF) based solutions that allows the significant scaling up of the wireless Access Points (Aps) in a cost-effective manner by exploiting the application of the distributed cell-free concept and of the serial fronthaul approach, while contributing innovative functionalities to the O-RAN project [2]. In parallel, in the fronthaul/midhaul segments MARSAL aims to radically increase the flexibility of optical access architectures for Beyond-5G (B5G) Cell Site connectivity via different levels of Fixed Mobile Convergence (FMC).

At the network and service management domain, the design philosophy of MARSAL is to provide a comprehensive framework for the management of the entire set of communication and computational network resources by exploiting novel ML-based algorithms of both edge and midhaul Data Centres (DCs), by incorporating the Virtual Elastic Data Centers/Infrastructures paradigm.

Finally, at the network security domain, MARSAL aims to introduce mechanisms that provide privacy and security to application workload and data, targeting to allow applications and users to maintain control over their data when relying on the deployed shared infrastructures, while AI (Artificial Intelligence) and Blockchain technologies will be developed in order to guarantee a secured multi-tenant slicing environment.

In this paper, we present a conceptual view of the MARSAL architecture (Sect. 2), as well as a wide range of experimentation scenarios (Sect. 3). The first domain includes a set of scenarios focused on *cell-free networking in dense and ultra-dense hotspot areas*. The second domain includes scenarios related to *cognitive assistance*, as well as *security and privacy implications* in 5G and beyond. We conclude our remarks in Sect. 4.

## 2 Architectural Framework

MARSAL aims to provide an evolved architecture towards B5G/6G, offering unprecedented degrees of flexibility and closed-loop autonomy at all tiers of the infrastructure, and significantly improved spectral efficiency via cell-free networking. The overall architecture and the structure of the envisioned B5G/6G MARSAL is depicted in the generic schematic of Fig. 1, and includes all the main infrastructure elements that are deployed within the MARSAL project. MARSAL adopts an evolved 3GPP NG-RAN (Next Generation - Radio Access Network) [3, 4] which is extended with emerging cell-free technologies for network densification. Moreover, the MARSAL architecture considers innovations at the optical transport domain and significant evolutions of the MEC (Multi-access Edge Computing) system [5] towards fully elastic Edge Computing. MARSAL deploys a distributed Edge infrastructure with DCs structured in 2 tiers, featuring Regional Edge and Radio Edge nodes. Radio Edge DCs will host the Network Functions (NFs) of the (virtualised) RAN, which fully aligned with the O-RAN specifications.

At the network level, the emphasis is on innovations at the RAN and fronthaul
domains that will “ unlock ” the potential of cell-free networking in future B5G/6G net-
works. MARSAL envisions cell-free networking as a “key component” of B5G/6G
RANs, that will offer unprecedented spectral efficiency (SE) and performance, which is not constrained by inter-cell interference. The MARSAL network architecture is based on novel cell-free networking mechanisms that will allow the significant scaling up of AP deployment in a cost-effective manner, by exploiting the distributed processing cell-free concept. The novel mechanisms are based on the disaggregation of the traditional cell-free Central Processing Unit (CPU) in Distributed Units (DUs) and a Central Unit (CU) in line with the 3GPP NG-RAN architecture. Regarding the wireless fronthaul links, the MARSAL architecture is based on an innovative mmWave Hybrid MIMO (Multiple-Input, Multiple-Output) solution [6, 7], specifically targeting cell-free networks, with advanced beamforming and beamsharing capabilities. In this way, a new AP topology adaptation in cell-free networks and advanced scenarios can be supported, with APs reassigned to different DUs, on demand. MARSAL's cell-free innovations will be implemented and integrated with existing vRAN (virtualised RAN) elements for the first time and will be contributed back to the O-RAN project. Specifically, the MARSAL network architecture will be aligned with the O-RAN Alliance architecture, which represents an evolution of Cloud RAN (C-RAN) [8], further disaggregating and complementing the 3GPP 5G standards with a foundation of vRAN network elements and packet-based interfaces. Specifically, O-RAN disaggregates the BBU (Base Band Unit) in a DU with the real-time functions, and a CU with the non-real time functions. The latter is further disaggregated into the CU-User Plane (CU-UP) and the CU-Control Plane (CUCP). MARSAL's CU User Plane function (i.e., CU-UP) and DU will be deployed at MARSAL's Radio Edge, and the CU-CP Near-RT RIC (Radio Intelligent Controller) at the Regional Edge.

<figure><img src="image_4.png" /><figcaption>Fig. 1. The MARSAL network architecture</figcaption></figure>

At the network service and management level, the MARSAL architecture will consider a novel hierarchical control plane solution, federating the SDN (Software Defined Network) controllers of the fixed and mobile segments of the network under a common orchestration subsystem. MARSAL proposes the disaggregation of the Non-RT (non-real-time) into Near-RT SDN Control function that will be hosted by the Near-RT RIC at the Regional Edge nodes. Thus, near real-time reaction to workload variations will be supported, at sub-second timescales. Moreover, MARSAL proposes the
deployment of Software-Defined Transport Network Controllers (i.e., SDTNs) [9] at the Regional Edge to control the fixed segment. Both domains will be federated under MARSAL's Core Tier NFVO (Network Function Virtual Orchestrator), based on ETSI OSM (Open Source MANO) [10], which will provide Network Slicing as a Service (NSaaS) functionality as per 3GPP TR 28.801 [11] specifications. Thus, end-to-end (E2E) slicing with centralised orchestration is supported, while still allowing innovative closed-loop (or ML-driven) control of each individual domain. The MARSAL architecture also considers a novel Fixed Mobile Convergence solution, to facilitate integrated connectivity of mobile and fixed (i.e., fiber-to-the-home - FTTH) services. MARSAL's solution involves two transmission approaches seamlessly integrated at the Regional Edge node, including a standard Point-to-Point (PtP) connection with or without WDM (Wavelength-Division Multiplexing) and a very disruptive Point-to-Multipoint (PtMP) approach based on XGS-PON (passive optical network) modules [12].

In addition, while previous approaches adopted a common Virtual Machine- (VM-) based technology stack for MEC and NFV (Network Functions Virtualisation), MARSAL approach is based on Cloud-Native technologies (i.e., Docker Containers, Kubernetes Virtual Infrastructure Managers (VIMs)) which are widely regarded as the future of vertical application development [13]. While support for Kubernetes VIMs is gradually emerging in MEC platforms (e.g., in StarlingX [14]), there is currently a gap in supporting disaggregated Cloud-Native apps. To fill this gap, MARSAL proposes extensions to the Multi-access Edge Orchestrator (MEO) [15] to support the disaggregation of application functions, that will be defined as collections of helm charts, both horizontally (i.e., across Edge sites) and vertically (i.e., from the cell site towards the core cloud). MARSAL will consider the deployment of the aforementioned functions, either at the “bare metal” of the MEC hosts’ NFVs (Network Function Virtualisation Infrastructures), or within VNFs (Virtual Network Functions), as proposed in the NFV-IFA 029 [16], thus compatible with the NSaaS sub-system. Moreover, MARSAL will extend the Mobile Edge platform at the host level, to allow MEC apps to be accessed by any UE (User Equipment), irrespective of physical location. Dynamic Virtual Network Embedding algorithms will be explored, to determine the optimal disaggregation of application functions at any Edge DC, considering Compute, Networking, and Storage constraints, thus achieving increased resource utilisation.

In parallel, the MARSAL architecture is built by considering a novel, distributed approach that involves Analytic Engines (AEs) at all tiers of the Edge infrastructure, and Decision Engines (DEs) at the two Core-Tier orchestration subsystems. Analytic Engines, the first pillar of automation, analyse and federate measurements to achieve Context Awareness, and Decision Engines, as the second pillar, Plan and React to Context changes, delegating data-driven local control decisions to the lower tiers of the hierarchy. For the first pillar of automation, MARSAL will design and implement an innovative, decentralised approach to achieve global Context Awareness, using for the first time Representation Learning such as Embedding Propagation (EP) [17] or the GraphSage algorithms [18]. State-of-the-art (SoTA) context representation methods for 1D and 2D datasets are not appropriate for MARSAL's diverse network and application data, that can be best represented with a graph-like abstraction. To this end, network slices and
MEC applications will be represented as the nodes of a knowledge graph, along with their defining variables and parameters (e.g., SLA (Service Level Agreement) requirements, latency budgets, cost considerations, energy efficiency goals). MARSAL proposes to apply EP mechanisms to build the node representations (or embeddings) of the knowledge graph, iterating over the data and minimizing the differences among neighbouring embeddings in the graph. For the second pillar of automation, the resulting embeddings that represent the current state (or context) of the MARSAL infrastructure in a highly compressed form (i.e., encoded as multidimensional normalised arrays) can be transmitted to the Core Tier Decision Engines. The embeddings are fed to downstream ML algorithms implemented by the Decision Engines that jointly orchestrate Network Slices, Network Services and MEC applications continuously and automatically evaluating current context under required policy. Due to the high number of (potentially conflicting) parameters and policy requirements involved, MARSAL will consider multi-objective optimisation techniques that achieve different trade-offs between optimality and complexity.

Finally, at the network security level, MARSAL introduces mechanisms that guarantee privacy and security in multi-tenancy environments, targeting both end users and tenants. MARSAL aims to deliver a decentralised, blockchain-based platform that supports network slicing transactions via smart contracts, targeting multi-tenant infrastructures for the first time. In this platform, the MNO (Mobile Network Operator), MVNOs (Mobile Virtual Network Operators), and OTT (Over-The-Top) vertical application owners form a decentralised autonomous organisation, which can dynamically negotiate network slice contracts, flexibly integrating large and small players without the need for a centralised entity. Smart Contracts facilitate direct contracts among entities that can be dynamically renegotiated based on real-time supply and demand. MARSAL's smart contract platform will be implemented with a private, permissioned blockchain solution where tenants will co-own the validator nodes' network after approval and authentication. Going a step further, MARSAL will also incorporate new privacy-preserving context representations, which will allow MARSAL's data-driven NSaaS subsystem to operate without exposing tenants' business and operational data. Context awareness requires the exchange of local embeddings (via EP mechanisms) that represent the nodes of a knowledge graph, risk-ing information leakage. These embeddings have inherent anonymisation properties, as they represent nodes as compressed, high-dimensional arrays, while the application of EP algorithms iteratively minimizes the differences among neighbouring embeddings, further decreasing the risk of re-identification of the original node. MARSAL will also integrate innovative techniques to guarantee that embeddings cannot be reversed and can be shared among competing partners and the NSaaS sub-system, without any risk of disclosing confidential information.

The MARSAL architecture will also incorporate an innovative NFS (Network File System) Gateway, controlled by the OTT application provider, to serve as the foundation of trust. The DCS gateway will be the intermediary between the (trusted) OTT application, and (untrusted) DCS (Distributed Cloud Solution) infrastructure. The NFS gateway will be extended to implement a novel data pipeline for the controlled sharing of data among different parties. The gateway will support for first time a policy
language, extending SoTA solutions such as Open Digital Rights Language or JSON-LD (JavaScript Object Notation for Linking Data), effectively enforceable in the data protection context of different stakeholders that will permit the specification of sharing and processing restrictions over data. Finally, the NFS gateway will implement a novel probabilistic scheme that protects the integrity of computations based on the randomised injection of pre-computed and replicated computational tasks. Thus, a unified solution for data obfuscation and integrity assurance will be implemented for the first time, which varies the probability of randomised injections based on the degree of protection or performance required.

To improve the performance of current signature-based solutions for dealing with zero-day or evolving attacks, MARSAL considers hardware accelerated solutions for a decentralised Threat Detection Engine (TDE) and a centralised Threat Analysis Engine (TAE). ML-based threat detection, that has demonstrated an improved ability to extract complex non-linear relationships in attack data, will be leveraged for the design of MARSAL's TDE. Moreover, MARSAL will leverage the capabilities of a new generation of programmable SDN Switches, which allows MARSAL's data-plane to behave as a distributed barrier against threats, securing the entire transport infrastructure and intercepting cyber-attacks at a very early stage. Detected cyber-attacks can be isolated by the data-plane at the level of individual traffic flows, going beyond traditional slice-centric approaches and towards micro-segmentation.

Moreover, MARSAL's network security level also considers a centralised TAE, that operates as an ML Fusion Centre, collecting and correlating metadata and features extracted from the P4 pipeline of the decentralised TDE. This allows complex attacks such as Advanced Persistent Threats that simultaneously target multiple network nodes, to be detected. Furthermore, it provides system-wide consistency and correlation for events occurring within all the involved P4 pipelines. MARSAL will exploit the flows' destination IPs (Internet Protocols), and specifically their sequence, since this information is both unencrypted and readily available as part of the SDN Switches' telemetry framework. Thus, the TAE will associate observed flows (with unknown status) with malicious ones based on the sequence of IPs accessed. MARSAL's solution will involve feeding a Deep Neural Network (DNN) with sequences of IPs to build a vector representation of network flows; intuitively, flows with a small distance from Radio Edge DCs will host the Network Functions malicious flows should also be flagged as malicious.

# 3 Experimentation Scenarios

## 3.1 Dense User-Generated Content Distribution with mmWave Fronthauling

The main objective of this scenario is to demonstrate and evaluate MARSAL's distributed cell-free RAN in terms of increased capacity and spectral efficiency gains, and the adaptivity of dynamic clustering and RRM mechanisms in managing connectivity resources in a dynamic environment with varying hotspots areas. Furthermore, an additional objective of this scenario is to evaluate the Hybrid MIMO Fronthaul in terms of its ability to offer a dynamic AP topology.

This experimentation scenario will show the potential of deploying cell-free networking in 6G networks with massive AP deployments. The MARSAL innovations will
focus on distributed processing, with clusters of APs and DUs coordinating via fronthaul links. In this experimentation scenario, we will evaluate the performance of dynamic data driven clustering algorithm. It will also explore and evaluate inter-DU coordination effect in Spectral Efficiency and propose dynamic adaptability of the coordination levels jointly addressing AP-DU and DU-DU coordination for the first time. The Cell-free vRAN components in this experimentation scenario will validate the design of cell-free enable virtual O-RAN Distributed Unit (O-DU), cell-free MAC (Medium Access Control) scheduler, PHY sub-layer. Moreover, it will also validate the appropriate modification of the CP protocols and O-RANspo-duecified interfaces (i.e., E2, O1) [19] to support practical cell-free operation and fully distributed processing.

In CF networks, the environment around the user defines the set of APs serving it. These APs may: i) *be connected to different O-DU and ii) utilize different types of fronthaul links*. The latter, in its turn, defines the information to be shared between the APs and their O-DUs. Next, the involved O-DUs share necessary information with each other. The final MARSAL processing will also consider the fronthaul and midhaul constraints (feedback to the cluster formulation block).

We list below a generic description of the stakeholders involved in this experimental scenario:

**Cell-free mMIMO Infrastructure Provider:** An infrastructure provider is needed to validate the MARSAL solution, especially for a novel cell-free NG-RAN architecture. The relevant stakeholder, besides providing the infrastructure, can conduct research on the area of cell-free mMIMO (massive MIMO) and could be providing, e.g., cell-free clustering solutions. The cell-free clustering solutions are needed to cooperate with the CPUs (Central Processing Units) and the O-DUs via algorithms that integrate the cell-free RUs (Radio Units) with the O-DUs by using PHY API's (Application Programming Interfaces).

**Cell-free vRAN VNFs Provider:** The cell-free vRAN VNFs provider from the industrial stakeholder will be designing the cell-free vRAN architecture. The stakeholder will be further implementing the solution on 3GPP protocol stacks and provide the VNFs. The VNFs can be implemented in radio edge DCs and/or regional edge DCs based on the underlying user's requirements and their SLA. However, the solution provided by the stakeholders as a form of VNFs (i.e., the vO-DU and vO CU-UP) can be deployed at the Radio Edge DCs as white-box servers.

**RAN Intelligent Controller and xAPPS<fnref n="1" /> Provider:** The RAN intelligent controller and the xAPPS provider will be involving providing the cell-enabled solutions with the modifying the necessary O-RAN specified interfaces. The AI/ML models at the Near-RT RIC provided by the stakeholders will somehow provide the RAN functionalities of the underlying architecture. The stakeholder will be providing the resources of Non-RT RIC/SMO (Service Management and Orchestration), Near-RT RIC, and RAN nodes, and relevant O-RAN specified interfaces to enable cell-free in the traditional O-RAN

<footnote n="1">¹ Application available at RAN Controller for RAN Configuration, AI/ML model policy execution, Radio Resource Management (RRM) model, slice selection, etc., as per O-RAN compliance approved specifications specified by O-RAN WG-1 and WG-3.</footnote>
architecture. The stakeholder solution of the Near-RT RIC and vCU_CP can be deployed
at a Regional Edge white-box server, and it can be further connected to the radio edge
DCs through optical network unit from other stakeholders.

**mmWave Fronthaul Provider:** Stakeholders can enable scalability through the flexible utilisation of a distributed cell-free approach and an mmWave fronthaul, by relying on optical technologies (PONs, WDM rings) for their interconnection with other components of MARSAL architecture. The mmWave transceivers and the beamforming solutions from the stakeholder can be integrated in Hybrid MIMO node that will be characterised via experiments (e.g., in terms of EIRP (Effective Isotropic Radiated Power), beam pattern, Field of View and beam directivity).

**mmWave Transport Network:** The stakeholder will contribute to design of the 6G x-haul networks and provide SDN-enabled wireless transport for the interconnection of distributed vRAN components of the MARSAL architecture. The Point-to-Point (PtP) mmWave solution of the stakeholders that can enable several deployment options (e.g., Co-located O-CU and O-DU: O-RAN split 7.2x, independent O-RU (O-RAN Radio Unit), O-CU (O-RAN Central Unit), O-DU locations: O-RAN split 7.2x, and O-RU and O-DU integration on cell site: O-RAN split 2), respectively<fnref n="2" />.

**Network Equipment Manufacturer:** Apart from traditional network devices (e.g., routers and switches), VNF-based devices must be designed and manufactured to support the MARSAL architecture, including DCs and MEC platforms.

## 3.2 Ultra-dense Video Traffic Delivery in a Converged Fixed-Mobile Network

This scenario will showcase MARSAL's solution towards Fixed-Mobile Convergence in an ultra-dense indoors context (e.g., campus, stadium, malls, etc.). Mobile clients served by a distributed Cell-Free RAN will be sharing the Optical Midhaul with third party fixed clients. The Fixed-Mobile Convergence in an Ultra-dense indoors scenario will be operated based on two operation modes: *Fixed operation* (Passive Optical LAN (Local Area Network)) and *Mobile operation* (small/pico cell with optionally Distributed Antenna System), respectively. The mobile clients served by a distributed cell-free RAN will be sharing the Optical Midhaul with third party FTTH clients. The optical fiber access equipment (Optical Line Terminal - OLT) will relate to PON and PtP interfaces where the Network urbanism organisation, e.g., OLT and CU co-localised and DU at the end face of the optical termination.

5G carriers and equipment are emitted and localised at the regular antenna locations with 2G/3G/4G. The pressure of coverage based on the requirements of the regulator is the main reason to have such deployment engineering rules. Concerning the mobile backhaul, 5G deployment coincides with a massive use of optical fiber to achieve the required backhaul throughput up to 10GEth. The fixed access network is based on PtP (Point-to-Point) topology to achieve the connectivity between antenna site and the first

<footnote n="2">² Further information about O-RAN components’ definitions can also be found at: https://docs.o-ran-sc.org/en/latest/architecture/architecture.html.</footnote>
aggregation node (central office). Due to that in parallel of 5G, FTTH is under deploy-
ment, we have more and more central offices (COs) equipped with OLT shelf. 5G back-
hauling could be addressed either by direct PtP connection to aggregation switch/router
or through OLT PtP ports and cards. In order to address the increase of 6G cells, the pre-
ferred fixed technology to collect multiple spots is the PtMP also named PON (Passive
Optical Network) based on “tree” fiber infrastructure. 6G transport challenges concern
the coordination between RAN and FAN (Radio and Fixed Access Network) networks
to “address” throughput, latency and availability issues.

We list below a generic description of the stakeholders involved in this experimental
scenario.

**Optical Access Networks and Fiber Infrastructure Provider:** This stakeholder will be providing the ONU (Optical Node Unit) and optical fiber infrastructure that will connect the Radio Edge nodes of the MARSAL architecture, which is hosting the vRAN elements. It will further enable an interconnection via PtP and PtMP (PON technology) midhaul links with the Regional Edge. The Regional Edge nodes of the MARSAL architecture will be interconnected in a WDM ring topology, will host the Radio Intelligent controller and the vCU_CP VNFs from the NVFs provider.

**Radio Intelligent Controller Provider:** The stakeholder will be providing Radio intel-
ligent controller including the Near-RT RIC, SMO, relevant O-RAN specified interfaces
to enable cell-free in the traditional O-RAN architecture.

**The RAN Components Provider:** The RAN components provider will be providing the CU and DU as monolithic VNFs that will be integrated with the cell-free RUs (APs). Furthermore, the stakeholder of the VNFs providers will modify and implement necessary interfaces to enable cell-free NG-RAN for the PoC (Proof-of-Concept) experimentation validation.

**Network Equipment Manufacturer:** Apart from traditional network devices (e.g., routers and switches), VNF-based devices, the ONU, optical fiber gateway must be designed and manufactured to support the MARSAL architecture.

## 3.3 Cognitive Assistance and Smart Connectivity for Next-Generation Sightseeing

In this scenario, the deployment of two real-time and interactive cloud-native applica-
tions for outdoors sightseeing supporting human-centered interaction via 3D cameras
is envisioned in the MARSAL’s multi-tenant elastic edge infrastructure. These appli-
cations would be offered to users equipped with untethered Augmented Reality (AR)
glasses. Both applications would endure an enhanced strolling experience by showing
overlaid information relevant to their surroundings (APP#1) and enabling virtual arti-
facts manipulation (APP#2), while considering background traffic from other applica-
tions and services. This experimentation scenario envisaged within MARSAL leverages
smart glasses to furnish a full B5G/6G sightseeing experience. To better showcase the
scope of the proposed scenario, let us conceive a general use case consisting of a next-
generation sightseeing tour enhanced with AR in a big city. Throughout the tour, the
user is provided with information of interest as she/he walks by the streets of the city
(APP#1). The tour also proposes a predefined route where the user visits multiple points
of interest (POI) empowered with artifact manipulation applications (APP #2).

This scenario would fit within the “Immersive smart city” use case under the category “Massive twinning” and also within the “Fully-merged cyber-physical worlds” use case under the category “Immersive telepresence for enhanced interactions” as described in [20]. In our vision: i) the sightseeing application (APP#1) could be part of the different services that would be managed under the Immersive smart city, e.g., under the ambi- ence/environment (for example, climate, air quality) and cultural aspects. and; ii) the virtual artifact manipulation (APP#2) would be linked with the enhanced interactions to be addressed by B5G/6G networks. As explained in [20], the use of B5G/6G networks are needed in these use cases as they improve some existing KPIs (Key Performance Indicators) of 5G networks (such as service availability, coverage, and network energy efficiency); this also happens because B5G/6G networks bring new capabilities leading to new KPIs among which we highlight integrated sensing, local compute integration, integrated intelligence and flexibility (ability of the system to be adapted and tailored to specific use cases and environments as a consequence of disaggregation, softwarisation, and automation/orchestration, which are concepts dealt with in MARSAL).

Below we list a generic description of the stakeholders involved in this experimen-
tation scenario. These are the following entities:

**End-user:** The end-user is the consumer of the applications, participating as a spectator and active player, in the respective applications.

**User Equipment Manufacturer:** These are the companies in charge of designing and manufacturing user equipment for VR (Virtual Reality) / AR (Augmented Reality) devices like smart glasses.

**Network Equipment Manufacturer:** Apart from traditional network devices (e.g., routers and switches), VNF-based devices must be designed and manufactured to support the MARSAL architecture, including DCs and MEC platforms.

**Connectivity Provider:** Large enterprises (such as telecom operators) play this role by offering resources (network, computing, and storage) in large-scale and cloud-based environments for service deployment. It is their duty to safeguard the integrity and long-term viability of their infrastructures in order to accommodate virtualised services. NFV Infrastructures are provided/leased by connectivity providers, where VIMs are in charge of the infrastructures, connected with the Service Virtualisation Platform (SVP) and controlled by the Service Platform Provider. The NFVIs address not only the requirements for a static, centralised cloud environment, but also the dynamic and mobility-related requirements.

**Service Provider:** This is a generic role that can be assigned to many types of companies, covering a plethora of services in vertical domains that may potentially be benefited from
the MARSAL architecture. Service Providers are companies that offer services to end-users or other companies, considered as the consumers of a service, for example, a company offering a MEC platform for processing of video files captured through the smart glasses or charging end-users.

**Service Virtualisation Platform Operator:** This stakeholder is in charge of running and maintaining a secure, scalable, and efficient SVP for the deployment of media services. The SVP Operator's responsibilities include general control of daily operations, provisioning and maintenance processes, security practices, disaster recovery planning and execution. The SVP operator is also responsible for analysing and optimizing resource allocation, as well as ensuring that charges are made in accordance with agreed-upon SLAs.

**Applications Developer:** Companies that develop novel 6G-related applications (or services or functions that can be integrated to make a service). They may use the SVP's Service Development Kit (SDK) for deploying, configuring, and managing their services.

## 3.4 Data Security and Privacy in Multi-tenant Infrastructures

The goal of this scenario is to demonstrate and evaluate MARSAL's privacy and security mechanisms. They guarantee the isolation of slices and ensure collaboration of participants in multi-tenant 6G infrastructures without assuming trust. These mechanisms will also be evaluated in terms of their ability to mitigate the increased privacy risks of NGI (Next Generation identification) applications that process Personally Identifiable Information (PII). To this end, this scenario will demonstrate the application of security and privacy mechanisms in four different layers, namely: *secure and private sharing of information among tenants, legal security using smart contracts, security of the data stored in the cloud and security of the end-users*.

The development of each of the different layers presents different challenges ranging from the implementation of smart contracts among different tenants to the real time analysis of network data to allow the protection of the end-users in a timely manner.

We approach the security and privacy in 6G networks in a holistic way. Contrary to the previous scenarios, offering a solution tailored for the MARSAL architecture, here we present a modular design to offer four different layers of security and privacy that could be applied in very different contexts. Moreover, we adapt them to work in the context of the *Cognitive Assistance scenario in 5G and beyond* described above.

This scenario assumes a multi-tenant infrastructure with one MNO and two MVNOs, each serving an OTT application provider. MARSAL technology will ensure the isolation of the different slices while offering the possibility of collaboration among the different tenants. To this end, we will demonstrate how the usage of smart contracts can be paired with the private representation of data, allowing the sharing of information among different tenants and the owner of the infrastructure that can be interested in the optimisation of different ML models.

Moreover, the scenario will also cover the security (and privacy) at different levels of the MARSAL architecture. First, we will demonstrate how policies can be used to safely
store data in the cloud (either at the core or the edge of the network), testing different
allocation strategies that ensure the perpetual security of the data.

Then, we move our focus to the network, and we will demonstrate how the brows-
ing patterns of users can be analysed in real time to alert end-users against malicious
behaviours they may have, before they get in trouble.

We list below a generic description of the stakeholders involved in this experimen-
tation scenario.

*   **Network operator:** The network operator is in charge of the last 2 layers of security: the security of the end-users and the security of the network itself. It will be in charge of deploying the engines in charge of the protection.

*   **Cloud providers:** The cloud providers should apply the storage policies defined by the users and tenants to ensure the security of the data.

*   **Slice tenants:** The slice tenants will be responsible for the correct execution of the privacy transformation of the data and the signature of the smart contracts.

*   **Network equipment manufacturers:** They should be able to generate equipment able to execute complex tasks, such as ML models execution or on-line rule adaptation to allow the protection of the end-users.

*   **End-users:** They are the most benefited from the execution of the different privacy and security layers that allows them to use the MARSAL architecture in a safer way.

# 4 Discussion

In this paper, we presented a conceptual view of the MARSAL architecture, as well as a wide range of experimentation scenarios.

In particular, the *first domain* is focused on *cell-free networking in dense and ultra-dense hotspot areas*. The first experimentation scenario considers *dense User-Generated Content (UGC) distribution with mmWave fronthauling*. The main objective of this scenario is to demonstrate and evaluate MARSAL distributed cell-free RAN in terms of increased capacity and spectral efficiency gains, and the adaptivity of dynamic clustering and RRM mechanisms in managing connectivity resources in a dynamic environment with varying hotspots areas.

The second experimentation scenario investigates *ultra-dense video traffic delivery in a converged fixed-mobile network*. This scenario showcases MARSAL's solution towards Fixed Mobile Convergence in an ultra-dense indoors context. Mobile clients served by a distributed Cell-Free RAN will be sharing the Optical Midhaul with third party FTTH clients.

The *second domain* is focused on *cognitive assistance and its security and privacy implications in 5G and Beyond*. The third experimentation scenario is about *cognitive assistance and smart connectivity for next-generation sightseeing*. In this scenario, the deployment of two real-time and interactive cloud-native applications for outdoors sightseeing supporting human-centered interaction via 3D cameras is envisioned in the MARSAL's multi-tenant elastic edge Infrastructure. These applications would be offered to users equipped with untethered AR glasses. Both applications would endure
an enhanced strolling experience by showing overlaid information relevant to their surroundings and enabling virtual artifacts manipulation, while considering background traffic from other applications and services.

The fourth experimentation scenario addresses *data security and privacy technical challenges in multi-tenant infrastructures*. We approach the security and privacy in 6G networks in a holistic way. We present a modular design to offer four different layers of security and privacy that could be applied in very different contexts.

Our future work will include a list of testing set-ups for the evaluation process, as well as a set of preliminary targeted KPIs. It should be noted here that these KPIs will be under continuous reconsideration. Furthermore, we will focus on the network architecture specifications, the requirements of management and security components, as well as the finalisation of MARSAL architecture.

**Acknowledgments.** The paper has been based on the context of the "MARSAL" ("Machine Learning-Based, Networking and Computing Infrastructure Resource Management of 5G and Beyond Intelligent Networks") Project, funded by the EC under the Grant Agreement (GA) No. 101017171.

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