William S Fall
email: william.fall@universite-paris-saclay.fr

Jörg Baschnagel

Olivier Benzerara

Olivier Lhost

Hendrik Meyer
email: hendrik.meyer@ics-cnrs.unistra.fr

Molecular Simulations of Controlled Polymer Crystallization in Polyethylene

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Molecular dynamics simulations of polyethylene crystallisation are widespread [START_REF] Ramos | Predicting experimental results for polyethylene by computer simulations[END_REF][START_REF] Zhang | Quasicontinuous melting of model polymer monolayers prompts reinterpretation of polymer melting[END_REF][START_REF] Waheed | Molecular simulation of crystal growth in neicosane[END_REF][START_REF] Meyer | Formation of chain-folded structures in supercooled polymer melts[END_REF][START_REF] Meyer | Static Rouse Modes and Related Quantities: Corrections to Chain Ideality in Polymer Melts[END_REF][START_REF] Jabbari-Farouji | Plastic deformation mechanisms of semicrystalline and amorphous polymers[END_REF][START_REF] Yamamoto | Molecular dynamics simulation of stretch-induced crystallization in polyethylene: Emergence of fiber structure and molecular network[END_REF] and have generally been restricted to short linear chains crystallised by continuous-cooling from the melt. [START_REF] Gee | Atomistic simulations of spinodal phase separation preceding polymer crystallization[END_REF][START_REF] Lacevic | Heterogeneous directional mobility in the early stages of polymer crystallization[END_REF][START_REF] Luo | Disentanglement of linear polymer chains toward unentangled crystals[END_REF][START_REF] Luo | Role of thermal history and entanglement related thickness selection in polymer crystallization[END_REF][START_REF] Luo | Frozen Topology: Entanglements Control Nucleation and Crystallization in Polymers[END_REF][START_REF] Hall | Divining the shape of nascent polymer crystal nuclei[END_REF][START_REF] Hall | A coarse-grain model for entangled polyethylene melts and polyethylene crystallization[END_REF][START_REF] Ranganathan | Atomistic modeling of plastic deformation in semicrystalline polyethylene: role of interphase topology, entanglements, and chain dynamics[END_REF][START_REF] Sanmartín | Following the crystallization process of polyethylene single chain by molecular dynamics: the role of lateral chain defects[END_REF][START_REF] Stack | Crystallization, melting, and morphology of low molecular weight polyethylene fractions[END_REF][START_REF] Vettorel | Coarse graining of short polythylene chains for studying polymer crystallization[END_REF][START_REF] Fall | Role of Short Chain Branching in Crystalline Model Polyethylenes[END_REF] Few studies have considered the effect of short chain branching [START_REF] Kumar | Effect of short chain branching on the interlamellar structure of semicrystalline polyethylene[END_REF][START_REF] Hu | Effect of short-chain branching on the tie chains and dynamics of bimodal polyethylene: Molecular dynamics simulation[END_REF][START_REF] Hu | Dominant Effects of Short-Chain Branching on the Initial Stage of Nucleation and Formation of Tie Chains for Bimodal Polyethylene as Revealed by Molecular Dynamics Simulation[END_REF][START_REF] Moyassari | Molecular dynamics simulation of linear polyethylene blends: Effect of molar mass bimodality on topological characteristics and mechanical behavior[END_REF] or bidispersity [START_REF] Moyassari | Molecular dynamics simulations of short-chain branched bimodal polyethylene: Topological characteristics and mechanical behavior[END_REF][START_REF] Zhai | Crystallization and molecular topology of linear semicrystalline polymers: simulation of uni-and bimodal molecular weight distribution systems[END_REF][START_REF] Vao-Soongnern | Molecular simulation of the structural formation of mono-and bidisperse polyethylene upon cooling from the melts[END_REF] on the crystallisation of polyethylene. Such polymers are known to offer improved mechanical properties such as flexibility and fracture-toughness, which are crucial in hazardous applications i.e. high-pressure gas pipelines. Studying crystallization of branched polydisperse systems is thus a fundamentally interesting polymer science problem, which is at the same time of high practical significance. The systems considered here are designed to mimic industrial polyethylene, with bimodal molecular weight distributions and branches concentrated in the high molecular weight portion. Even fewer studies have employed the experimental method of creating single polyethylene crystals known as self-seeding. [START_REF] Sommer | Molecular dynamics simulations of semicrystalline polymers: Crystallization, melting, and reorganization[END_REF][START_REF] Meyer | Formation of chain-folded structures in supercooled polymer melts examined by MD simulations[END_REF] In typical experiments dilute solutions of polyethylene may be viewed under a polarising optical microscope as a clear liquid. During self-seeding, it is cooled to a given temperature and held there until the sample appears sufficiently cloudy. This signals the onset of crystallisation, as small nuclei are beginning to form. Before crystallisation proceeds too far, it is heated until clear and then crystallised at fixed temperature. In this way, the growth of a few remaining nuclei may be observed as spherulites, under a polarising optical microscope, with the formation of a maltese cross. [START_REF] Blundell | A new self-nucleation phenomenon and its application to the growing of polymer crystals from solution[END_REF][START_REF] Blundell | Nature of self-seeding polyethylene crystal nuclei[END_REF] To date no simulation study has simultaneously considered the effect of self-seeding, bidispersity and the influence of short chain branching, on the formation of single polyethylene crystals as is considered here. We use large-scale computer simulations to investigate the crystallisation of bidisperse polyethylene systems, with and without branches, using two different crystallisation protocols, continuous-cooling and self-seeding. Our simulations provide a pathway to grow unprecedentedly large single crystals in simulations. In addition, branching only the long chain portion proves sufficient to control the crystal shape and 2 lamella thickness. A cross-over in crystal shape is witnessed as more short chain branches are included, from dome-like to the plate-like multi-lamella crystals seen in many textbooks on polymer crystallisation.

Whilst single lamella polymer crystals have been achieved in molecular dynamics simulations via self-seeding [START_REF] Sommer | Molecular dynamics simulations of semicrystalline polymers: Crystallization, melting, and reorganization[END_REF] and two lamella very recently via tethering of artificial nuclei, [START_REF] Verho | Crystal growth in polyethylene by molecular dynamics: The crystal edge and lamellar thickness[END_REF] multi-lamella crystals have proven difficult to grow from the melt. Some previous studies have grown single lamella crystals and replicated the simulation cell to achieve well-aligned stackings. [START_REF] Higuchi | Deformation and fracture processes of a lamellar structure in polyethylene at the molecular level by a coarse-grained molecular dynamics simulation[END_REF] The resulting structures are however strongly correlated and conclusions regarding mechanical properties require carful interpretation. One major bottleneck associated with growing polymer crystals is their size, typical experiments report single PE crystals some 50nm thick which may extend as far as 25µm and thus exceptionally large simulation cells are required to grow them. The simulations reported here utilised over 4 × 10 6 CPU hours on the Jean-Zay supercomputer. The size of the simulations was sufficiently large to allow the formation of multi-lamella polymer crystals. A simple polymer model with purely repulsive interactions between polymer chains is utilised, 4,5 which has recently been reparameterized specifically for polyethylene. [START_REF] Fall | Role of Short Chain Branching in Crystalline Model Polyethylenes[END_REF] All molecular dynamics simulations are crystallised from a fully amorphous polymer melt and short chain branches were incorporated using the method outlined in our previous work. [START_REF] Fall | Role of Short Chain Branching in Crystalline Model Polyethylenes[END_REF] Monomers are represented as united-monomers, with single chemical monomers represented by single beads in the simulation. The purely bidisperse polymer melt comprised of 3,840 200-bead chains (768,000 united-monomers) and 384 2,000-bead chains (768,000 united-monomers) making a total of 1,536,000 united-monomers. Either 10 or 20 branches were then included on the longer chains, raising the branched system sizes to 1,543,680 and 1,551,360 united-monomers, respectively. The model parameters for polyethylene can be found in Ref. 19 which consist of both stretching and bending valence terms as well as nonbonded soft van der Waals interactions. See supplementary information for further details.

Each of these systems is then subjected to two different cooling protocols, continuous-cooling and self-seeding. The self-seeding procedure consists of a fast quench to a seeding temperature T s slightly below where homogeneous nucleation becomes possible. After a short time, temperature is raised above the homogeneous nucleation temperature such that most nuclei melt again and only the largest continue to grow, see Fig. 1.

The simulations show a remarkable change in morphology (see Fig. 2) both as a result of the cooling protocol employed and the number of short branches included on the long chains.

By self-seeding a single nucleus is allowed to grow, interrupted only by itself through the periodic boundary, until most of the simulation cell is consumed by the crystal. The beige or purple chains represent crystalline and amorphous chains, respectively. Without short chain branching (Fig. 2a) the morphology of the crystal is over 25nm at its thickest point but with a shallow radial extension. By adding 10 branches, the crystalline part of the domain becomes thinner, reduced by approximately 10nm at its thickest, see Fig. 2b. Consequently, the additional amorphous material not consumed by a thicker domain is made available for a marginally larger radial growth. This can be seen clearly in the final panels of Fig. 3 which reveal the shapes of the crystalline domains with the amorphous material removed. This results from the strict exclusion of branches (blue dots) from the crystallites, which restricts the maximum extension of stems on the long chains. At 20 branches per chain, the morphology is radically different and the domain appears almost completely regular in its thickness, averaging around 7nm, along the entirety of its extension (see Fig. 2c).

Furthermore, when the domain meets itself through the periodic boundary, it merges to create one single, continuous and unbroken lamella. The semi-crystalline picture seen in Fig. 2c is particularly striking, resembling the textbook cartoon lamellae taught in many physics courses on polymer crystallisation.

In comparison, when the same systems are subjected to continuous-cooling at 0.04 K/ns, multiple nucleation events destroy the regularity and orientation of the crystallites, resulting in relatively poor crystals. They appear small and quickly crash into one-another as they grow, preventing the much longer extended chains seen in the self-seeded crystals, see Fig S3. In addition, the influence of short chain branching is, at least visually, nearly completely obscured by continuous-cooling. The cross-over in domain size, from dome-like to platelike domains, seen during self-seeding is observed only very weakly in crystals grown by continuous-cooling. Whilst this observation is weak, crystallisation by continuous-cooling remains a valuable approach for branched systems, [START_REF] Fall | Role of Short Chain Branching in Crystalline Model Polyethylenes[END_REF] taking only a fraction of the time required to produce the same qualitative trends. However, self-seeding clearly produces larger densities, larger stem lengths, and higher crystallinity, all of which are desirable in view of experimental systems. It is only with the computational advances of the last decade that simulations can be large enough and run long enough to grow crystals representative of real experiments. The white box in Fig. 2d for example shows the typical lamella sizes achievable some 20 years ago. [START_REF] Meyer | Formation of chain-folded structures in supercooled polymer melts[END_REF][START_REF] Meyer | Static Rouse Modes and Related Quantities: Corrections to Chain Ideality in Polymer Melts[END_REF][START_REF] Meyer | Formation of chain-folded structures in supercooled polymer melts examined by MD simulations[END_REF] Note even with such computational advances, the crystals grown in Fig. 2 still required several months to grow, each of which consumed 320 cores of Jean-Zay. The growth of the self-seeded systems are evidently limited by the finite box size. An effect not seen in those crystals grown by continuous-cooling, which are instead limited mostly by surrounding nucleation events suppressing their growth. Development of finite-size scaling methods for polymer crystallisation are therefore required.

The differences between the two protocols is best illustrated by examining the density profiles during crystallisation, see Fig. 1. The time-temperature protocol for self-seeding begins with continuous-cooling from the melt at 227 • C to a seeding temperature T s followed by an isothermal run to allow homogeneous nucleation events to occur, see Fig. 1a. This corresponds to the first smooth increase in density followed by the nearly flat curve in Fig. 1b.

Typically the seeding of nuclei takes place for less than 60ns at a fixed temperature between 102 • C and 107 • C. Before crystallisation proceeds too far, systems are heated rapidly to melt away small nuclei, corresponding to a sharp density drop, to a carefully chosen crystallisation temperature T c . Isolating single nuclei large enough to grow is challenging and a trial and error approach is necessary to isolate the correct seeding times and temperatures. In this case, inspection of the simulation snapshots provided visual confirmation that only a single nucleus was preserved. For these very large systems, density fluctuations caused by the formation of nuclei are minute at early times due the large melt surrounding the nucleus.

A long isothermal crystallisation is then performed for upwards of 10µs between 122 • C and 127 • C. In the case of the system with 20 branches, the crystallisation temperature required decreasing from 132 • C to 127 • C to ensure crystallisation could take place on timescales accessible in our simulations as indicated by the black circle in Fig. 1b. When the crystalline domain has grown sufficiently large and the density becomes nearly flat at long times, a final continuous-cooling to room temperature (27 • C) was performed and a short isothermal run was used for taking measurements. Interestingly, during the final quench, small crystallites, much smaller than the larger crystalline domains, are observed appearing in the amorphous region oriented perpendicular to main crystal, see the white circle in Fig. 2c.

Continuous-cooling curves are shown below the self-seeding in Figs. 1c andd. Whilst the final density of the systems is, on the whole, lower compared to self-seeded crystals, the density monotonically decreases with an increasing number of branches for both cooling protocols, consistent with previous studies. [START_REF] Fall | Role of Short Chain Branching in Crystalline Model Polyethylenes[END_REF] Remarkably, the density of the unbranched system grown by continuous-cooling is similar to the system with 20 branches grown via self-seeding. Post-cooling, all systems were heated at 0.1 K/ns, see Fig 1 (d), continuouslycooled and self-seeded systems correspond to solid and dashed lines respectively. Note the monotonic increase in melting temperature with decreasing SCB content and the much higher melting temperature of all systems grown via self-seeding as opposed to continuous-cooling.

For reference T m ≈ 157 • C for 200 united-monomer long chains, crystallised by continuouscooling. [START_REF] Fall | Role of Short Chain Branching in Crystalline Model Polyethylenes[END_REF] To further interrogate the morphology of the different crystalline forms, the stem length distribution and structure factors are examined in Fig. 4. Stem length distributions are calculated by examining the local alignment of bond vectors using the local P 2 order parameter, commonly employed to assess the crystallinity in simulations of PE crystallisation, [START_REF] Zhang | Quasicontinuous melting of model polymer monolayers prompts reinterpretation of polymer melting[END_REF][START_REF] Hall | Divining the shape of nascent polymer crystal nuclei[END_REF][START_REF] Hall | A coarse-grain model for entangled polyethylene melts and polyethylene crystallization[END_REF][START_REF] Fall | Role of Short Chain Branching in Crystalline Model Polyethylenes[END_REF][START_REF] Zhang | Roughening Transition and Quasi-continuous Melting of Monolayers of Ultra-long Alkanes: Why Bulk Polymer Melting Is Strongly First-Order[END_REF] Figure 3: Survival of single nuclei after self-seeding revealed by removing amorphous chain segments. The blue window corresponds the last frame of the respective seeding runs for each system at T s , where multiple nuclei are present in the box. The red window shows snapshots at crystallisation temperature T c from beginning to end. In some cases, nuclei are still melting away at T c , due to the rapid heating from the seeding temperature and consequently the system falls out of equilibrium. Note the small number of nuclei in the system with 10 branches results from the higher seeding temperature used. which may be defined as

P 2 = 3 cos 2 θ i,j -1 2 (1)
where θ i,j is the angle between an arbitrary bond i with all neighbouring bonds j in immediate vicinity. The neighbouring cutoff is chosen identical to our previous work, for further details see Ref. The static structure factor S(q), was also examined which allows a direct comparison with experiment [START_REF] Fall | Role of Short Chain Branching in Crystalline Model Polyethylenes[END_REF] and is defined as

S(q) = 1 M tot Mtot i,j=1 e iq•(r i -r j ) |q|=q±dq ( 2
)
where the sum is performed over all united-monomers M tot in the system and the angular brackets indicate averaging over all q-vectors of length q ± dq. A running average is applied in the interval q r = π/L box with bin size 0.01σ -1 . Only q-vectors compatible with the finite box size may be considered, hence the precision becomes increasingly poor as q approaches the inverse box size. [START_REF] Meyer | Formation of chain-folded structures in supercooled polymer melts examined by MD simulations[END_REF] The self-seeded systems show multiple peaks in both the SAXS (low-q) and WAXS (highq) ranges, which reflect either large-scale or small-scale features respectively. In the WAXS range, peaks arise from the hexagonal packing of the chains in the crystalline lattice and are strongest for the unbranched system, becoming slightly weaker as more branches are included. Strong peaks occur at (1 : √ 3 : 2) times the q-value of the first sharp peak at q 0 = 1.506 Å-1 due to the very high crystallinity of the self-seeded systems. The SAXS range also develops a series of strong sharp peaks, resulting from the large single crystals which fill the simulation box. The cross-over in domain shape can be observed by the change in intensity and position of the strongest peak. With thicker domains, the unbranched system and system with 10 branches have their strongest peaks around q = 0.025, after the addition of 20 branches, this peak shifts to q = 0.035 due to the thinner domains enforced by exclusion of the branches. In comparison, the systems crystallised by continuous-cooling have weaker WAXS peaks due to the relatively poor crystallinity, but more interestingly the strong SAXS peaks are completely lost. Instead these curves are diffuse and broad and reduce marginally in both intensity and position with an increasing number of branches. The same trends are in keeping with the stem length distributions in Fig. 4a and c and consistent with our previous work. [START_REF] Fall | Role of Short Chain Branching in Crystalline Model Polyethylenes[END_REF] Such findings may provide insights into structure factors observed in experiments.

The fact that a small fraction of SCBs allows control over the lamellar thickness is in line with our findings on shorter monodisperse systems. [START_REF] Fall | Role of Short Chain Branching in Crystalline Model Polyethylenes[END_REF] It is interesting to note that this property is still valid with the co-crystallization of the unbranched LMW fraction in this work. The large-scale well-aligned multi-lamella crystals now provide an opportunity to study the structural response arising from changes in molecular weight distribution, and branch content on morphological features, i.e tie-chains or entanglements which may be precisely determined in a well-aligned system. Moreover, well-aligned systems are ideal candidates for probing material strength when subjected to shear stresses in a given direction, i.e. parallel or perpendicular to the chain backbones. To date this has only been performed reported in a forthcoming study. Such studies are highly relevant for branched bidisperse PE which offers desirable properties, that still remain poorly understood from a theoretical standpoint. In future it would be interesting to apply this methodology to monodisperse systems to reveal the effect of different branching protocols such as randomly or regularly placed branches.

Figure 1 :

 1 Figure 1: Time-temperature protocols [panels (a) & (c)] and density curves [panels (b) & (d)] for all systems with 0, 10 and 20 branches during the self-seeding and continuous-cooling simulations. (a) Time-temperature protocol during self-seeding. T s , T c and T q correspond to the seeding, crystallisation and final quench temperatures, respectively. (b) Density-time evolution for all systems during self-seeding, the black circle corresponds to the small temperature decrease around 1µs for the 20 branch system to speed up crystallisation. (c) Continuous-cooling time-temperature protocol where T m and T c are the melt and crystallisation temperature respectively, T b corresponds to the bifurcation temperature (152 • C) where the cooling rate is instantaneously switched from 0.1 K/ns to 0.04 K/ns for computational expedience. (d) Density-temperature profile during continuous-cooling, note T 0 = 500K and τ = 2.7ps correspond to the reference temperature of the melt and the LJ time unit respectively, see Supplementary Information for the derivation of SI units.

Figure 2 :

 2 Figure 2: System snapshots, at room temperature (27 • C) showing the crossover in domain shape and lamella thickness when more than 10 branches are included. Chains are coloured according to their local alignment P 2 = ⟨(3 cos 2 θ-1)/2⟩, purple and beige regions correspond to amorphous (P 2 = 0) and crystalline (P 2 = 1) regions respectively. Cyan dots represent the branches. The top 3 crystals (a-c) have been grown via self-seeding and the bottom 3 crystals(d-f) via continuous-cooling at 0.04 K/ns. For self-seeding, note the change in lamella thickness and transition from dome-like domains to plate-like ones when more than 20 branches are included. This is only weakly observed in the continuous-cooling simulations. The white box in panel (d) highlights the typical lamella thickness achievable in MD simulations some 20 years ago.[START_REF] Meyer | Formation of chain-folded structures in supercooled polymer melts examined by MD simulations[END_REF]

Figure 4 :

 4 Figure 4: Morphological analysis of the final crystals at room temperature (a,c). Crystalline stem length distributions of the extended stems shown in the beige regions of the snapshots. (b,d) Total structure factor S(q) in the crystalline phase at room temperature (27 • C). The simulation box size is indicated by (2π/L box ) and the sharp peaks of the (2d) hexagonal lattice appear strongest for the systems without branches grown via self-seeding, q 0 corresponds to 1.506 Å-1 . The arrows in panel (b) highlight the shift of the most intense peak as more SCBs are incorporated.

19 .

 19 All unbroken stems containing bonds with P 2 > 0.85 are then isolated to produce a histogram. The stem length distributions highlight the contrast between systems as more branches are included. Most notably the unbranched self-seeded system achieves stems as long as 25nm with 3 distinctive peaks appearing centering around 10, 15 and 20nm. With the addition of 10 branches the stem length is much reduced and appears limited to a maximum extension of 15nm, enforced by the branches. It appears as though the longest stems have folded in two by the branch and shifted into the first and second peaks. With the addition of 20 branches almost all of the longest stems are shifted into the first peak and the stem length is further reduced and the distribution resembles a gaussian curve. The same systems, when crystallised by continuous-cooling, are very different with all curves resembling that of a gaussian distribution. Without branches a broader distribution of crystalline stem lengths is achieved, which becomes systematically narrower as the number of branches increases.

Acknowledgement W.S.F., J.B., O.B. and H.M. acknowledge funding support from TotalEnergies via grant IPA-6352. The authors would like to acknowledge Dr Kamila Kazmierczak for stimulating discussion and administering the project. A generous grant of computer time, on the Jean-Zay supercomputer, at the GENCI/IDRIS national computing centre of CNRS is gratefully acknowledged (grant no A0110913034).

Supporting Information Available

Details of molecular dynamics simulations and system preparation. Video of the largest system considered comprising 1,551,360 united-monomers, with 20 branches crystallising from the melt at the crystallisation temperature of 127 • C.


Introduction

In the dynamic and rapidly evolving landscape of telecommunications and e-commerce, the need for robust, scalable, and efficient technological solutions has considerably grown. This document provides a comprehensive study of the technical and functional requirements of a 5G-core network service and a web store application in e-commerce. It also provides insights into the SCALER platform's capabilities and the execution infrastructure designed to support these use cases.

In Section 2 the document first explores the shift towards cloud-native network functions and service-based architectures for 5G core networks. It gives insights into the specific technical and functional requirements of the 5G-core network service, detailing the network functions and their roles in ensuring seamless, secure, and efficient network operations.

In the second part of the document (Section 3), we focus on the e-commerce sector, where the adoption of microservices architecture is revolutionizing the way web store applications are developed and managed. The document gives an overview of the key components of an e-commerce application, the technical requirements for a microservices-based approach, and the functional requirements that ensure a user-friendly and secure online shopping experience.

In the last part (Section 4), the SCALER use case execution infrastructure is introduced, highlighting two environments: i) the private cloud environment provided by Eolas and ii) Grid'5000 national research infrastructure.

Real-world UC # 1 -5G-core network service

The integration of 5G technology into telecommunications has triggered a shift in the potential of Telco's cellular networks. The advent of this advanced network generation has not only triggered unprecedented speeds but has also paved the path for a swift evolution of envisioned services. Among these services are innovative advancements such as augmented reality, virtual reality, autonomous vehicles, and Internet of Things (IoT). This transformative wave requires a substantial overhaul from telecom operators, compelling them to reconfigure their architectures and fortify their infrastructures to seamlessly align with these burgeoning demands.

From this viewpoint, 5G technology has introduced a groundbreaking concept that advocates for the disaggregation of networks, aiming to make them fully cloud-native. This innovative paradigm promotes the breakdown of network services into a collection of adaptable, autonomous, elastic, and segregated microservices, each securely enclosed within its container. These microservices, representing distinct network functions, aim to form a novel breed of scalable, self-repairing, and nimble network function known as CNF (Cloud Native Network Function) [1]. This evolution drives towards a more resilient, dynamic, and efficient network architecture poised to meet the evolving demands of modern connectivity. We remind the reader that a network function represents a software entity dedicated to executing specific tasks related to the management, control, or facilitation of network services

The 5G Core brings a significant transformation by replacing traditional network architectures with service-based interactions between Network Functions. This eliminates the need for nodes or network elements connected by interfaces. In the 5G Core architecture, each Network Function offers services to other Network Functions in the network, encompassing both control plane and data plane functionalities. Control services are accessed through Network Function interfaces connected to the common Service-Based Architecture (SBA). This means that the specific functionality provided by a Network Function is exposed and accessible through an API (Application Programming Interface) [2].

Among the network functions that the 5G core network is relying on, we find : D 4.1 : Use Cases Requirements

• AMF-Access and Mobility management Function is the gateway to the core network that ensures all control interactions with gNB (i.e., base station) and end users (i.e., UE).

• SMF-Session Management Function controls packet data unit (PDU) sessions on one or more instances of the User Plane Function (UPF), providing end-to-end connectivity to the UE.

• UPF-User Plane Function processes and forwards user data. The functionality of the UPF is controlled by the SMF. It interconnects with external IP networks and acts as an anchor point for the UEs towards external networks.

• NRF-Network Repository Function is the function responsible for discovering services in the 5G control plan. It is used by all other network functions in the control plane to record or retrieve information about other functions.

• UDM-Unified Data management Function is a front-end for the user subscription data stored in the UDR (see below). The UDM uses subscription data that may be stored in the UDR to execute application logic like access authorization, registration management and reachability for terminating event e.g., SMS.

• UDR-Unified Data Repository is the database where various types of data are stored. Important data is of course the subscription data or data defining network or user policies. UDR storage and access to data is a service offered to other network functions, specifically UDM, PCF, and NEF.

• UDSF-Unstructured Data Storage Function is an optional function that allows other NFs to store dynamic context data outside the NF itself. This is sometimes referred to as a "stateless" implementation.

• AUSF-Authentication Server Function is responsible for authentication of UEs requesting 5G network services. Specifically, it communicates with the Unified Data Management (UDM) function to retrieve subscriber information, which is recorded by the telco operator.

• PCF-Policy Control Function provides policy control for session management, for access and mobility, for UE access selection and PDU session selection.

• NSSF-Network Slice Selection Function The NSSF selects the set of network slice instances for the UE and the set of AMFs that should serve the UE.

• NEF-Network Exposure Function exposes events and capabilities from the 5G system towards applications and network functions inside and outside the operator's network.

• SEPP-Security Edge Protection Proxy is used to protect the signaling between operators in roaming scenarios.

• N3IWF-Non-3GPP Inter Working Function is used for integrating non-3GPP accesses with the 5G core.

• LMF-Location Management Function serves the purpose of determining the User Equipment's (UE) location. It achieves this by retrieving location estimates directly from the UE and obtaining additional location measurements and data from the Next-Generation Radio Access Network (NG-RAN).

Key 5G Core Technical Requirements and KPIs

As the next generation of mobile communication, the 5G Core network brings forth a range of technical requirements and Key Performance Indicators (KPIs) that are specifically addressed to meet the evolving needs of the industry. These requirements and KPIs are crucial in ensuring the successful deployment and operation of the 5G Core network. Let's delve into the key technical requirements and KPIs that the 5G Core network focuses on:

• Seamless mobility: The 5G Core network is designed to support seamless mobility across various cell types and radio access technologies. It enables smooth handovers and transitions between different network elements, ensuring uninterrupted connectivity for users.

• Context-aware optimal connection: Leveraging contextual information such as device, user, environment, and network conditions, the 5G Core network aims to provide the best possible connection and services at all times. This ensures optimal user experiences and personalized connectivity.

• • Single ID for multiple access: Irrespective of the access network, the 5G Core network recognizes a mobile terminal as a single entity. This means that users can maintain a consistent identity and experience regardless of the network they are connected to, providing a seamless and unified connectivity experience.

• Lightweight signaling: The 5G Core network requires efficient signaling protocols capable of accommodating various terminals, including massive Machine Type Communication (MTC) devices. These protocols are designed to be lightweight, minimizing overhead and optimizing network resources.

• Distributed architecture: To handle the expected traffic increase of up to 1000 times, the 5G Core network maintains a distributed network structure. This architecture ensures scalability and efficient resource management, enabling the network to handle the growing demands of future communication.

• Multiple radio access technologies interworking: The 5G Core network requires an architecture that enables seamless flow over multiple radio access technologies. This ensures uninterrupted service continuity, even in scenarios with limited bandwidth in wireless access.

The network efficiently interworks with different radio access technologies to deliver highvolume services cost-effectively.

• Fine-grained location tracking: The 5G Core network provides a virtualization environment WP 4 T0+12 D 4.1 : Use Cases Requirements and robust support for reconfiguration and upgrades within the core network. This allows for cost-effective alterations without the need to make changes to the physical network infrastructure. The network can adapt and evolve efficiently to meet changing requirements and technological advancements.

• Flexible reconfiguration and upgrade: The 5G Core network provides a virtualization environment and robust support for reconfiguration and upgrades within the core network. This allows for cost-effective alterations without the need to make changes to the physical network infrastructure. The network can adapt and evolve efficiently to meet changing requirements and technological advancements.

• Network on-demand: The 5G Core network is capable of constructing the network infrastructure based on specific parameters such as Quality of Service (QoS), Quality of Experience (QoE), charging mechanisms, and distinct service attributes. This ensures a tailored network configuration that meets various requirements and improves the user experience. The network can be dynamically provisioned to meet specific needs, optimizing resource allocation and enhancing service delivery.

Examples for the technical requirements KPIs and their values are given in Table 1.

Key UC Functional Requirements and KPIs

In the following are described some major scenarios of the 5G network functions.

Registration

Registration is the initial procedure that a User Equipment (UE) undergoes after being powered on. Its purpose is to establish a connection with the network and enable the UE to receive services. The registration procedure can be summarized in the following steps:

1. The UE sends a NAS Registration Request message to AMF via the radio access network (R)AN. If temporary UE identities (5G-S-TMSI or GUAMI) are included and the (R)AN can map these to a valid AMF, the (R)AN forwards the NAS message to that AMF. Otherwise the (R)AN selects an AMF, based on Requested NSSAI [ reference to slicing] or a configured default AMF, and forwards the NAS message to that AMF.

2. In case a new AMF is selected (e.g., because the UE registers in an area not served by the old AMF), and the UE provided a temporary UE identity containing the identity of the old AMF, the new AMF retrieves the UE context from the old AMF.

3. Authentication within the network is executed through the utilization of either the 5G Authentication and Key Agreement (AKA) protocol or the Extensible Authentication Protocol for AKA (EAP-AKA), ensuring robust security measures for user verification and access control.

4. In case a new AMF has been selected, the new AMF indicates to the old AMF that it is now taking over as serving AMF for the UE.

5. The AMF registers as serving AMF for the UE in the specific access technology (3GPP access or non-3GPP access) and requests subscription data and subscribes to subscription data updates using from the UDM. The UDM notifies the old AMF that it is deregistered in the UDM. D 4.1 : Use Cases Requirements 6. In case Access and Mobility management policies are deployed, the AMF initiates establishment of the AM policy association with PCF and retrieves the AM policies.

7. If the UE has indicated that it wants to activate User Plane connection for existing PDU Sessions, the AMF in the invokes the required SMF operation for those PDU Sessions.

8. If the Registration procedure is successful so far, the AMF provides a NAS Registration Accept to the UE. In some cases, the UE sends a NAS Registration Complete message to the AMF.

PDU Session Establishment

The PDU (Packet Data Unit) Session Establishment procedure is a process that starts when a User Equipment (UE) wants to create a new PDU Session or handover a PDU Session between non-3GPP access to 3GPP access. The PDU Session Establishment procedure is always initiated by the 2. The AMF directs the 5GSM container, which holds the PDU Session Establishment message, to the SMF. Upon receiving this container, the SMF retrieves the UE's subscription data related to Session Management from the UDM and also subscribes to subscription data updates from UDM.

3. A secondary authentication may be performed between the UE and an external AAA (Authentication, Authorization, and Accounting service) on the Data Network.

4. The SMF selects a PCF (Policy Control Function) and initiates a policy session to obtain the initial set of PCC (Policy and Charging Control) rules. The SMF also selects a UE IP address and UPF and initiates a N4 session establishment towards that UPF.

5. The SMF transmits a 5GSM NAS PDU Session Establishment accept message to the UE, providing both the UPF GTP-U tunneling endpoint specifics and QoS details directed to the (R)AN. The AMF serves as the conduit for sending this message.

6. The AMF generates an N2 message that carries both the NAS message (PDU Session Establishment accept) and the essential PDU Session specifics for the (R)AN. This includes details about GTP-U tunneling and QoS information. Subsequently, the (R)AN configures the necessary resources toward the UE and replies to the AMF, providing information about the (R)AN's GTP-U tunnel endpoint. Once this process is completed, the up-link data path becomes operational and available for use.

7. The AMF forwards the PDU Session information received from (R)AN to the SMF so that SMF can provide the (R)AN GTP-U tunnel endpoint to the UPF for downlink forwarding. With this action completed, the downlink data path is now operational and available for use.

(2)

(4) (5)
Nsmf_PDUSession_UpdateSMContext Response

N4 Session Establishment

Deregistration

The deregistration process lets the UE tell the network it doesn't want 5G system access any longer, and vice versa-the network can inform the UE it no longer has 5G system access. The procedure contains the following steps: 2. The AMF notifies every SMF that holds an active Session Management context, about the release of this specific context. Subsequently, each SMF informs other Network Functions about the PDU Session release, involving actions such as terminating N4 sessions and associated resources, ending policy associations with the PCF, and deregistering from the UDM for that specific PDU Session ID.

3. The AMF terminates the AM (Access and mobility) policy association with the PCF, if such an association exists.

4. The AMF terminates the UE policy association with the PCF, if such an association exists.

Handover

Handover procedures in 5G system are used to handover a UE from a source NG-RAN node to a target NG-RAN node. The procedure is briefly described in the following.

1. Before starting the handover process toward the core network, NG-RAN conducts essential handover preparation and executes signaling both within NG-RAN and between NG-RAN and the UE.

2. Once the handover is confirmed within NG-RAN, a N2 Path Switch Request is transmitted to notify that the UE has moved to a new target cell. This message contains details regarding the PDU Session to be switched.

3. The AMF informs every relevant SMF holding a PDU Session impacted by the handover. These SMFs subsequently provide the NG-RAN N3 tunnel details to the UPFs. This enables the transmission of down-link data to the new NG-RAN node.

4. Following the down-link path shift to the target NG-RAN, the UPF directs down-link packets to this new destination. To help the Target NG-RAN's reordering process, the UPF transmits "end marker" GTP-U packets for each N3 tunnel on the old path (toward the source NG-RAN). This action signals the arrival of the final down-link packet on the old path, enabling the removal of the forwarding tunnel between the source and target NG-RAN. Consequently, the target NG-RAN ensures sequential delivery of down-link packets to the UE.

5. Upon receiving responses from all SMFs in step 3, the AMF consolidates the gathered UPF N3 tunnel information and transmits this compiled data to the NG-RAN. This action enables the NG-RAN to set up the necessary up-link N3 data path(s).

6. The target NG-RAN then informs source NG-RAN that the handover is successfully completed so that source NG-RAN can release the associated resources.

7. At times, following the completion of a handover, the UE may need to trigger a Mobility Registration procedure. This situation can arise if the handover causes the UE to relocate beyond its designated Registration Area. In the ever-evolving e-commerce industry, an increasing number of companies are turning to a microservices-based software development approach to replace their legacy monolithic applications. This migration represents a significant shift in how companies design, develop, and deploy their online commerce platforms. The transition to microservices offers numerous benefits, including improved scalability, agile development, enhanced error management, and greater technological flexibility. However, it is essential to acknowledge the expected complexities, such as management, inter-service communication, testing, and security. Companies considering this transition must engage in careful planning and execution to maximize the advantages while minimizing potential challenges.

Microservices have introduced several aspects that have proven to be very beneficial for e-commerce applications, namely:

1. Scalability on Demand: e-commerce websites often experience fluctuating levels of traffic, typically during sales, holidays, or special events. Microservices allow e-commerce platforms to scale individual services to meet increased demand, ensuring a smooth and responsive shopping experience for customers.

2. Enhanced Resilience: applications need to be highly available and resilient. Microservices, with their isolated nature, make it easier to design redundancy and fail-over mechanisms, ensuring uninterrupted service even if one component fails. Different versions of a service can be deployed and tested independently to measure their impact on user engagement, conversion rates, and overall user experience.

5. API-First Design: microservices are designed with well-defined APIs, making it easier for different services to interact with each other. This API-first approach simplifies the integration of new services or third-party tools.

Adaptation to Market

Trends: e-commerce is a dynamic industry, and market trends change rapidly. Microservices provide the flexibility to adapt to these changes by allowing for the introduction of new services or the modification of existing ones without overhauling the entire system.

The microservice paradigm thus ultimately leads to improved customer experiences and increased competitiveness in the e-commerce sector.

An e-commerce application typically consists of various key functions, that will be detailed later on this document, each serving a specific purpose to enable a smooth shopping experience for users. These functions collectively create a comprehensive e-commerce application that offers a user-friendly, secure, and feature-rich shopping environment, meeting the demands of both customers and businesses in the digital marketplace. The experiments for the web store application use case will use one or more open source e-commerce projects adapted to industry standards and the needs of the SCALER project. We can cite the following: WP 4 T0+12 D 4.1 : Use Cases Requirements • Teastore [3,4] -Emulates a basic web store that contains 6 microservices (5 services and 1 registry) -Each service may be replicated without limit and deployed on separate devices as desired

• DeathStarBench [5,[START_REF]DeathStarBench Github[END_REF] -Includes multiple use cases like Social Network, Hotel reservation or E-commerce site -Focuses on large-scale microservice applications that stress typical datacenter design (inspired by Amazon.com, Netflix and Twitter web applications)

• OpenTelemetry Astronomy Shop [START_REF]The application should manage the logistics of shipping, including options for different shipping methods, costs, and tracking of deliveries[END_REF]8] -Created by OpenTelemetry team to illustrate code instrumentation -Composed of microservices written in different programming languages that talk to each other over gRPC and HTTP; and a load generator which uses Locust [9] to fake user traffic

• TrainTicket [10,11] -Proposes a service architecture that contains 41 microservices -Can be used with tracing software like Jaeger [12] • Online Boutique [13] -Proposes a service architecture that contains 11 microservices.

-Google uses this application to demonstrate the use of technologies like Kubernetes, GKE, Istio, Stackdriver, and gRPC.

• Robot Shop [START_REF]Robot Shop[END_REF] -Stan's Robot Shop is a sample microservice application you can use as a sandbox to test and learn containerised application orchestration and monitoring techniques.

-Instana1 uses this application to demonstrate the use of its tracing techniques.

Key UC Technical Requirements and KPIs

In a microservices architecture for an e-commerce web application, several technical requirements are crucial for successful deployment and operation. These requirements include:

• Scalability: the ability to scale individual components of the application independently to handle varying loads, especially during high-traffic events like sales, promotions or daily peak load.

• Performance and Efficiency: optimized services to ensure fast response times and efficient resource usage, which is crucial for maintaining a positive user experience (i.e. E2E latency, number of requests per seconds, core web vitals [START_REF]Core Web Vitals[END_REF]). • Reliability and Availability: high availability and fault tolerance are vital. This includes strategies like replication, load balancing, and the use of circuit breakers to prevent failures from cascading. To improve reliability, stakeholders can implement custom health check or status page that generate alerts in the event of an technical outage (e.g. : valid database connection, valid third-party service, etc ...).

• Security: robust security measures to protect sensitive data, such as user information and payment details. This includes secure communication between services, data encryption, and adherence to compliance standards like PCI DSS for payment processing.

• API Gateway: a single entry point for all clients (web, mobile, etc.), which routes requests to the appropriate microservice and handles cross-cutting concerns like authentication, SSL termination, and rate limiting.

• Monitoring and Logging: comprehensive monitoring and logging to track the health of each microservice, user activities, and system anomalies.

• Database Management: each microservice should ideally have its own database to ensure loose coupling, but this must be balanced with the complexity of managing multiple databases.

• Inter-Service Communication: efficient and reliable communication mechanisms, like RESTful APIs or messaging queues, depending on the use case.

• Resilience and Error Handling: implementing strategies for graceful degradation in case of partial system failure, including retries, timeouts, and fallback methods.

Depending on the use case, Table 2 shows some examples of KPIs that could be defined to achieve efficient e-commerce application.

Key UC Functional Requirements and KPIs

An e-commerce application typically consists of various key functions, each serving a specific purpose to enable a smooth shopping experience for users. Functional requirements for an ecommerce web application describe what the system should do and how it should perform specific functions. These requirements are typically centered around user interactions, business processes, and data management. Here's a non exhaustive list of common functional requirements for an e-commerce web application: 15. Recommendation Engine. This function uses algorithms to suggest products to users based on their behavior, preferences, and purchase history, enhancing personalization. [START_REF]Core Web Vitals[END_REF]. Returns and Refunds. The application should be able to manage the process for customers who wish to return products, request refunds, or exchange items. 17. Multi-Language and Currency Support. To cater to a global audience, e-commerce applications may offer multiple language options and the ability to display prices in various currencies.

18. Social Media Integration. Integration with social media platforms allows users to share products and reviews, increasing brand visibility and traffic.

19. Multi-platform Compatibility. Ensuring the application is compatible across various devices and platforms (like desktops, mobile devices, and tablets) is necessary for broad accessibility.

Additionally, stakeholders use custom dashboards to track e-commerce application performance from multiple perspectives such as sales, marketing, customer service, manufacturing and project management [17]. Figure 6 shows a dashboard used by Eolas customers. Each indicator focuses on the financial goal to be achieved on a daily, weekly or monthly basis. Here are some examples of KPIs :

• Sales (in local currency) for the desired period (day, week, month, quarter, year).

• Gross profit (in local currency) is calculated from the total sales minus the cost of goods.

• Profit margin (in local currency) is calculated from the total sales minus the cost of the business.

• Number of orders with the evolution (in %) compared to last year.

• Number of new customers with the evolution (in %) compared to last year.

• Conversion rate (in %) tracks the rate of users purchasing items in the shopping cart.

• Bounce rate (in %) tells how many users exit your site after viewing only one page.

• Shopping cart abandonment rate (in %) tells how many users are adding products to their shopping catr but not checking out.

• Website traffic refers to the total number of visits to the site during a specific period.

SCALER Use Case Testbeds

This section delves into the technical setup and infrastructure that underpins the SCALER project's testbeds. These testbeds are pivotal components of the SCALER project, providing the necessary infrastructure for the development, deployment, and evaluation of the use cases discussed earlier in this document. These testbeds are designed to replicate real-world conditions as closely as possible, offering a controlled environment where the project's innovative solutions can be rigorously tested and refined. The testbeds are equipped with state-of-the-art hardware and software, enabling the SCALER team to simulate the complex interactions between various components of 5G-core network services and e-commerce web applications. This section will outline the technical setup of these testbeds, including the virtual private cloud (VPC) hosted by Eolas and the Grid'5000 testbed, detailing their respective roles in supporting the project's objectives.

Eolas Testbed

The testbed proposed by Eolas is designed to provide a secure and controlled environment for the SCALER project team to rigorously test the functionalities and performance of the use cases. It includes a description of the hardware specifications of the bare-metal servers, the virtualization software used, and the network configurations that support the deployment of multiple Kubernetes clusters and virtual machines. This section is essential for stakeholders to understand the technical capabilities and resources available for the SCALER project, which will facilitate the development, deployment, and scaling of the use cases. Private Cloud As depicted in Figure 7, the Scaler testbed is hosted by Eolas as a virtual private cloud (VPC). The VPC consists of a VMware vSphere vCenter and two hypervisors running VMware vSphere ESXi, with sufficient compute resources to run multiple Kubernetes clusters and virtual machines (VM) for the project needs. For security purposes, all accesses to the testbed are secured by VPN.

Each bare-metal server includes the following hardware :

• 2 processors Intel Gold 6226 2. • Minio (S3 storage) Minio is a high-performance, Kubernetes-native object storage service that is compatible with Amazon S3 cloud storage service. It is generally used in private cloud environments due to its scalability and ease of deployment. Minio provides a secure way to store large amounts of unstructured data, such as photos, videos, log files, backups, and container/VM images. It is designed for high availability and supports features such as distributed mode, erasure coding, and bitrot detection to protect data integrity.

• Grafana (Dashboard tool) Grafana is an open-source analytics and interactive visualization web application that provides charts, graphs, and alerts for the web when connected to supported data sources. In the context of the SCALER project, Grafana will be used to create dashboards that visualize metrics, logs, and traces from the various components of the testbed. It helps the project team to understand the system's behavior and performance through real-time data visualization, making it easier to detect and diagnose potential issues.

• Loki (Logs storage) Loki is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by Prometheus. It is designed to be cost-effective and simple to operate. Unlike other logging systems, Loki does not index the contents of the logs, but rather a set of labels for each log stream. This approach allows for more efficient querying, especially when paired with Grafana for visualizing the log data. Loki is particularly well-suited for storing and querying large volumes of log data.

Grid'5000 Testbed

The partners of the SCALER project also have access to the experimental infrastructure Grid'5000 [START_REF]Grid'5000: a large-scale and flexible testbed for experiment-driven research in all areas of computer science[END_REF]. Grid'5000 is a French national research infrastructure for all areas in computer science with a particular focus on HPC, Cloud, BigData and AI. It spans over 9 sites and counts 69 clusters with 770 nodes (cf. Figure 8). The computational power is provided by 17016 CPU cores and 395 GPUs with 1876672 GPUs cores. It has 111.27 TiB of RAM and 6.0 TiB of PMEM. The storage is composed of 564 SSDs and 904 HDDs for a total of 1.48 PB.

The maximum computational capacity is 725.8 TFLOPS (excluding GPUs).

Figure 8: Grid'5000 Backbone Architecture Grid'5000 comes with a set of useful tools allowing for the reservation and for the complete control over a set of resources. It is possible to deploy a full software stack (from OS to applications) on nodes, as well as to configure a virtual network.

Together, these tools form a comprehensive observability stack that enables the SCALER project team to store, monitor, and analyze the vast amounts of data generated by the use cases in the testbed environment. This ensures that the team can maintain high levels of service quality, reliability, and performance throughout the development and testing phases of the project.

Conclusion

This document serves as a cornerstone for future developments within the SCALER project. It gives insights about the framework that will guide the different SCALER partners through the implementation and optimization of 5G-core network and e-commerce applications.

Figure 1 :

 1 Figure 1: 5G System Architecture

Figure 2 :

 2 Figure 2: UE Registration Procedure

WP 4 T0+12D 4 . 1 :

 441 Use Cases Requirements UE, but sometimes the network might send a signal (a device trigger message) to an application on the UE. When the UE's application receives this message, it might decide to start the PDU Session Establishment procedure based on the information it received. The actual steps involved in this procedure are the following : 1. The UE transmits a 5GSM (5G Session Management) NAS PDU Session Establishment message to the AMF. This message contains crucial details like the PDU Session Id, the requested DNN (Data Network Name) along with the desired S-NSSAI (Single Network Slice Selection Assistance Information), and the type of PDU Session needed. The AMF, upon receiving this message, handles the NAS security. If the PDU Session Establishment is a request for a new PDU Session, the AMF selects a new SMF. The AMF may use the NRF to discover available SMFs serving the specific DNN and S-NSSAI. If the PDU Session Establishment is a request for handing over an existing PDU Session, the AMF uses its context about the UE to identify which SMF currently manages that particular PDU Session Id.

8 .

 8 Finally, the SMF then registers itself in UDM, indicating its role as the entity serving the specific PDU Session Id.

Figure 3 :

 3 Figure 3: PDU Session Establishment Procedure

D 4 . 1 :

 41 Use Cases Requirements 1. The UE sends a NAS Deregistration Request message to the AMF through the (R)AN.

5 .

 5 The AMF sends a Deregistration Accept message to the UE 6. Finally, the AMF instructs (R)AN to release the N2 UE context.

  Figure 4: UE Deregistration Procedure

Figure 5 2 -

 52 Figure 5: Handover Procedure

3 .

 3 Faster Development Cycles: the microservices architecture encourages agile development practices. E-commerce companies can release updates, new features, and bug fixes more frequently, responding quickly to market changes and customer feedback. 4. A/B Testing and Experimentation: microservices facilitate A/B testing and experimentation.

14 .

 14 Wishlist and Favorites users can create wishlists or mark products as favorites for future reference or as a way to keep track of items of interest.

Figure 6 :

 6 Figure 6: Eolas custom business metrics dashboard

Figure 7 :

 7 Figure 7: Eolas SCALER Testbed Architecture

WP 4 T0+12D 4 . 1 :

 441 Use Cases Requirements • Mimir (timeseries aggregation, storage and retention) Mimir (formerly known as Cortex) is a long-term storage solution for Prometheus, which is a widely-used open-source system monitoring and alerting toolkit. Mimir allows the horizontal scaling of Prometheus across multiple nodes and provides long-term storage capability for Prometheus metrics. It is designed to be highly available and supports multi-tenancy, which means it can serve multiple separate Prometheus sources. Mimir is useful for aggregating, storing, and querying large amounts of time-series data generated by the SCALER testbed.

Wired/wireless terminal switching: The

	WP 4	T0+12
	D 4.1 : Use Cases Requirements
	Requirement KPI	Value
	E2E latency	The time needed to transfer a given piece of information from a source to a destination measured at the commu-	User plane (1 ms) Control plane (20 ms)
		nication interface from the moment it is transmitted by
		the source to the moment it is successfully received at
		the destination.
	Reliability	The percentage of packets successfully delivered within	99.999 %
		the required time constraint out of all transmitted pack-
		ets.
			99.999 %
	Mobility
	Interruption	The shortest time duration supported by the system dur-	0 ms
	Time	ing which a user terminal cannot exchange user plane
		packets with any base station during transitions
	Peak Data Rate	The maximum achievable data rate under ideal condi-tions (in bit/s)	Downlink 20 Gbit/s Uplink 10 Gbits/s
	Packet Error Rate	processed by the sender of a link layer protocol but that The upper bound of the rate of packets that have been	10 ´6
		are not successfully delivered for the corresponding re-
		ceiver of the upper layer
	Energy Efficiency	mance and data speeds while minimizing power con-The capability of the network to deliver high perfor-	Qualitative
		sumption
		5G Core network facilitates fast and efficient switch-
	ing between wired and wireless devices, enabling seamless device and session mobility. This
	capability allows for quick transfers between different types of devices, enhancing user con-
	venience.

Table 1 :

 1 Technical Requirements KPIs Examples for 5GC Use Case

Table 2 :

 2 4.1 : Use Cases Requirements Technical Requirements KPIs Examples for the e-commerce Usecase

	Requirement	KPI	Metric	Value
	Scalability	Service Scaling Time Time taken to spin up additional in-	< 5 minutes to scale up
			stances of a service.
	Performance and Efficiency	Response Time	quests. Average response time of service re-	< 300 milliseconds
	Reliability and Availability	SLA, SLO [15]	Percentage of time the application is op-erational.	SLA : 99.9% uptime SLO : 99.5% uptime
	Security	Number of Security Breaches	Count of security incidents per year.	0 incident
	API Gateway	API Error Rate	Percentage of API requests that result in	<0.5%
			errors.
	Monitoring and Logging	Mean Time to Detection (MTTD)	Average time to detect an issue.	< 30 minutes
	Database Management	Database Query Performance	queries. Average response time for database	< 50 milliseconds
	Inter-Service Communication	Message Failure Rate	services. Percentage of failed messages between	< 0.1%
	Resilience and Error Handling	Error Rate	tions. Number of errors per thousand transac-	< 10 errors

https://www.ibm.com/fr-fr/products/instana

Availability

The percentage of time during which the network is operational and accessible to the users.