Ioannis E Livieris
email: livieris@teiwest.gr

Niki Kiriakidou
email: niki.c.kiriakidou@gmail.com

Andreas Kanavos
email: kanavos@ceid.upatras.gr

Gerasimos Vonitsanos
email: mvonitsanos@ceid.upatras.gr

Vassilis Tampakas
email: vtampakas@teimes.gr

Employing Constrained Neural Networks for Forecasting new Product's Sales Increase

Keywords: Artificial neural networks, machine learning, sales forecasting, food retail industry, constrained optimization, limited memory BFGS

An intelligent sales forecasting system is considered a rather significant objective in the food industry, since a reasonably accurate prediction has the possibility of gaining significant profits and better stock management. Many food companies and restaurants strongly rely on their previous data history for predicting future trends in their business operations and strategies. Undoubtedly, the area of retail food analysis has been dramatically changed from a rather qualitative science based on subjective or judgemental assessments to a more quantitative science which is also based on knowledge extraction from databases. In this work, we evaluate the performance of weight-constrained neural networks for forecasting new product's sales increase. These new prediction models are characterized by the application of conditions on the weights of the network in the form of box-constraints, during the training process. The preliminary numerical experiments demonstrate the classification efficiency of weight-constrained neural networks in terms of accuracy, compared to state-of-the-art machine learning prediction models.

Introduction

From the beginning of the 21st century, there has been an increasing interest on the application of modern information technology and artificial intelligence to food retail analysis. Food retail market has grown continuously rapidly and steadily during the last decades. Nevertheless, the number of retail outlets forced out of business has also increased. In this competitive environment, it is necessary and vital for marketing managers in the food retail industry to utilize intelligent and efficient tools for better decision-making. Sales forecasting and product promotion activity constitute two of the most interesting and challenging problems in the field which significantly continue to grow in scale. As a result, a variety of promotional techniques are being used by companies in their marketing communication campaigns. A conventional measure of sales promotion effectiveness is to analyze sales patterns before and after promotion [START_REF] Peattie | Promotional competitions as a marketing tool in food retailing[END_REF].

Nowadays, many food companies and restaurants try to build their business strategy by exploiting huge information stored in a data warehouse. For example, the largest retailer in the United States, Walmart, has a customer database which contains more than 460 terabytes of transactional data stored on Teradata mainframes, made by NCR [START_REF] Pathak | Social implications of data mining techniques[END_REF]. The vigorous development of the Internet as well as the significant storage capabilities of electronic media, enabled the accumulation and storage of large repositories of data. On the one hand, many businesses and organizations realized that the knowledge in these data constitutes the key to various strategic decisions; but much of this knowledge is hidden and untapped. On the other hand, significant pressure on marketing decision-makers has been created by the intense competition. Therefore, there has emerged an attractive need to encourage customers to engage in a long-term relationship. This new trend has lead to the realization that food businesses must tailor their services and products according to customers' preferences by leveraging a large volume of transactional data.

During the last decades, several methodologies have been applied in the field of food retail marketing for extracting useful knowledge from transactional data, while most sophisticated analysis employs machine learning algorithms in order to provide a more meticulous view. Artificial Neural Networks (ANNs) constitute one of the most dominant machine learning algorithms for extracting useful knowledge; thus they have been extensively applied and evaluated across an impressive spectrum of applications (see [START_REF] Livieris | Predicting students' performance using artificial neural networks[END_REF][START_REF] Livieris | An improved spectral conjugate gradient neural network training algorithm[END_REF][START_REF] Maren | Handbook of neural computing applications[END_REF][START_REF] Shanmuganathan | Artificial neural network modelling[END_REF] and the references therein). Due to their excellent capability of self-learning and self-adapting, they are very appealing to address challenging real-world problems with poorly defined system models, noisy data and strong presence of non-linear effects.

In this work, we examine and evaluate the performance of weight-constrained neural networks for forecasting new product's sales increase. To this end, we conducted a series of experiments and compared the performance of these newly proposed prediction models against state-of-the-art machine learning prediction models. The reported numerical experiments demonstrate the classification accuracy of these models, providing evidence that the application of the bounds on the weights of the network, provides more stable and reliable learning.

The remainder of this paper is organized as follows: Section 2 presents a survey of recent studies concerning the application of machine learning in food retail analysis. Section 3 analyzes the weight-constrained neural networks, while Section 4 presents the dataset utilized in our study. Section 5 presents the numerical experiments. Finally, Section 6 presents the conclusions and our proposals for future research.

Related Work

During the last decades, a number of decision-making models have been proposed in the literature to assist marketing managers in order to decide a business strategy based on the model's outcome. Several rewarding studies have been carried out in recent years and some useful outcomes are briefly presented below.

Hasin et al. [START_REF] Hasin | An ANN approach to demand forecasting in retail trade in bangladesh[END_REF] presented a fuzzy ANN approach to predict the sales of a number of selected products (including perishable foods) in a retail chain, in Bangladesh. Based on their numerical experiments, the authors concluded that the prediction performance of fuzzy ANN is better than Holt-Winter's model. Additionally, they claimed that as the forecasting period becomes smaller, the proposed model provides more accurate predictions.

Žliobaitė et al. [START_REF] Žliobaitė | Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?[END_REF] introduced an intelligent two-level sales prediction approach which utilizes a mechanism for model switching, depending on the sales behavior of a product. For recognizing the behavior, they formulated three types of categorical features: behavioral, shape-related, and relational, which allow the categorization of the products sufficiently and accurately to beat the baseline in the final prediction. In their study, they utilized real data from the food wholesales company Sligro Food Group N.V. and illustrated the superiority of their approach compared to the baseline predictor as well as an ensemble of predictors. Additionally, they presented in detail the trade-offs between the risk and benefit to the final accuracy, while moving the categorization threshold.

Lee et al. [START_REF] Lee | Comparative study on the forecast of fresh food sales using logistic regression, moving average and bpnn methods[END_REF] evaluated the performance of three prediction models, namely Logistic Regression, Moving Average and ANNs, for forecasting fresh food sales in a point of sales database for convenience stores. The data utilized in their study were collected from 35 days of fresh food sales from the database of the Hi-Life convenience stores, including the number of sales and the amount of fresh food discarded. Their extensive experimental analysis revealed that Logistic Regression presented the highest overall classification performance.

Shukla and Jharkharia [START_REF] Shukla | Applicability of ARIMA models in wholesale vegetable market: an investigation[END_REF] investigated the applicability of ARIMA models in wholesale market models to forecast the demand for vegetables on a daily basis. In their study, they utilized sales data over a period of twenty five months from Ahmedabad wholesales market in India. Their numerical experiments showed that the proposed models are highly efficient in forecasting the demand for vegetables on a day-to-day basis. Therefore, the authors claimed that this model may be used to facilitate farmers and wholesalers in effective decision making.

In more recent works, Tsoumakas [START_REF] Tsoumakas | A survey of machine learning techniques for food sales prediction[END_REF] provided an excellent survey presenting the most elaborate machine learning algorithms, which have been applied for food sales prediction and the appropriate measures for evaluating their accuracy. Additionally, the significant design decisions of a data analyst working on forecasting food sales were discussed in detail, such as the temporal granularity of sales data, the input variables for predicting sales and the representation of the sales output variable. Finally, the main challenges and opportunities in the domain of food sales prediction were also presented.

Weight-constrained Neural Networks

Recently, Livieris [START_REF] Livieris | Improving the classification efficiency of an ANN utilizing a new training methodology[END_REF] proposed a new approach for improving the generalization of ability of ANNs by applying conditions on the weights of the network in the form of box-constraints, during the training process. The motivation behind this approach focuses on defining the weights of the trained network more consistently, in order to explore and exploit all inputs and neurons of the network. More specifically, by placing constraints on the values of weights, the likelihood that some weights will "blow up" to unrealistic values is considerably reduced, improving, the generalization ability of the network.

Therefore, the mathematical problem of training an ANNs is re-formulated as the following constrained optimization problem:

min{E(w) : w ∈ B}, (1)
with

B = {w ∈ R n : l ≤ w ≤ u}, (2)
where E(w) is the error function which depends on the connection weights w of the network, l ∈ R n is the vector of the lower bounds on the weights and u ∈ R n is the vector of the upper bounds on the weights.

Furthermore, in order to evaluate the efficacy and the efficiency of this approach, Livieris proposed the Weight-Constrained Neural Network (WCNN) training algorithm which is based on the L-BFGS-B method [START_REF] Morales | Remark on "Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization[END_REF].

At each iteration, WCNN algorithm approximates the error function E(w) by a quadratic model m k (w) at a point w k , namely

m k (w) = E k + g T k (w -w k ) + 1 2 (w -w k ) T B k (w -w k ), (3)
where E k = E(w k ), g k is the gradient of the error function at w k and B k is a positive-definite L-BFGS Hessian approximation [START_REF] Morales | Remark on "Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization[END_REF].

Next, the algorithm performs a minimization procedure of the approximation model m k (w) to compute the new vector of weights, which consists of three distinct stages. In Stage I, the model ( 3) is approximately minimized subject to the feasible domain B utilizing the gradient projection method in order to compute the generalized Cauchy point w C . Eventually, the active set A(w C ) is calculated which consists of the indices of weights whose values at w C are at lower or upper bound. In Stage II, the quadratic model m k (w) is minimized with respect to the non-active variables utilizing a direct primal method [START_REF] Morales | Remark on "Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization[END_REF]. It is worth noticing that the minimization is performed at a subspace of the feasibility domain B by considering as free variables, the variables which are not fixed on limits while the rest variables are fixed on their boundary value obtained during the previous stage. Finally, in Stage III, the algorithm calculates the new vector of weights w k+1 by performing a line search procedure. Calculate the error function value E k and its gradient ∇E k at w k .

5.

Set the quadratic model [START_REF] Domingos | On the optimality of the simple Bayesian classifier under zero-one loss[END_REF] where

D S = w ∈ R | l i ≤ w i ≤ u i , ∀i ∈ A(w C ) . /* Stage III: Line search */ 9. Set d k = w k+1 -w k . 10.
Compute the learning rate η k satisfying the strong Wolfe line search conditions

E k+1 ≤ E k + c 1 η k ∇E T k d k , |∇E T k+1 d k | ≤ c 2 |∇E T k d k |. with 0 ≤ c 1 ≤ c 2 < 1.

11.

Update the weights w k+1 = w k + η k d k . 12.

Set k = k + 1. 13. until (stopping criterion).

Dataset

The data collected from the IBM Watson Analytics 1 concerning a self-service fast food restaurant, located in the Connaught Place area of New Delhi with several branches all over Delhi. The chain plans to add a new product to its menu, therefore it decided three possible marketing campaigns for promoting the new product. In order to determine which promotion has the greatest effect on sales, the new item is introduced at locations in several randomly selected markets. A different promotion is used at each location and the weekly sales of the new item are recorded for the first four weeks. Table 1 presents a set of the six (6) specific attributes utilized in our study. The first two attributes concern a unique identifier for each market and the size of market area based on sales. The following two attributes concern a unique identifier for store location and the age of store in years. The last attribute concerns one of three tested promotions. Finally, the sales for each promotion and each store were classified utilizing a three-level classification scheme:

-"Low ": the total amount of sales is less than 170 thousands (30 instances).

-"Average": the total amount of sales is between 170 and 200 thousands (44 instances). -"High": the total amount of sales is more than 200 thousands (63 instances). Additionally, the class distribution is presented in Figure 1.

Attribute

Low

Experimental Results

In this section, we present an experimental analysis in order to evaluate the performance of weight-constrained neural networks for forecasting new product's sales increase against the most popular and efficient prediction models.

The classification accuracy was evaluated using the standard procedure called stratified 10-fold cross-validation [START_REF] Russell | Artificial intelligence: a modern approach[END_REF] i.e., this approach involves randomly dividing the set of instances into ten groups (folds), of approximately equal size, so that each fold has the same distribution of classes as the entire data set. Each fold is treated as a testing set, and the classification algorithm is fit on the remaining nine folds. The results of the cross-validation process are summarized with the mean of the prediction model skill scores.

The performance of all classification models was evaluated utilizing the following two performance metrics: F 1 -score and accuracy. It is worth noticing that F 1 -score consists of a harmonic mean of precision and recall while accuracy is the ratio of correct predictions of a classification model [START_REF] Livieris | Improving the classification efficiency of an ANN utilizing a new training methodology[END_REF].

Our experimental results were obtained by conducting a two-phase procedure: In the first phase, the classification performance of the WCNN algorithm was evaluated against the most popular state-of-the-art neural network training algorithms; while in the second phase, we compare the performance of the weight-constrained neural networks trained with WCNN algorithm against the most popular and frequently utilized machine learning classification algorithms.

Performance Evaluation of Weight-constrained Neural Networks against Classical Neural Networks

In the sequel, we compare the classification accuracy of WCNN algorithm against state-of-the-art ANN training algorithms, i.e. Resilient backpropagation [START_REF] Riedmiller | A direct adaptive method for faster backpropagation learning: The RPROP algorithm[END_REF] and Levenberg-Marquardt training algorithm [START_REF] Hagan | Training feed-forward networks with the marquardt algorithm[END_REF]. In other words, we evaluate the performance of weight-constrained neural networks against classical neural networks. The implementation code was written in Matlab 7.6 and the simulations have been carried out on a PC (2.66GHz Quad-Core processor, 4Gbyte RAM) running Linux operating system, while the results have been averaged over 100 simulations. All networks had logistic activation functions and received the same sequence of input patterns and the weights were initiated using the Nguyen-Widrow method [START_REF] Nguyen | Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights[END_REF]. It is worth noticing that all training algorithms were utilized with their default parameter settings.

Furthermore, since a small number of simulations tend to dominate these results, the cumulative total for a performance metric over all simulations does not seem to be too informative. For this reason, we utilize the performance profiles of Dolan and Morè [START_REF] Dolan | Benchmarking optimization software with performance profiles[END_REF] to present perhaps the most complete information in terms of robustness, efficiency and solution quality. The use of performance profiles eliminates the influence of a small number of simulations on the benchmarking process and the sensitivity of results associated with the ranking of solvers [START_REF] Dolan | Benchmarking optimization software with performance profiles[END_REF]. The performance profile plots the fraction P of simulations for which any given method is within a factor τ of the best training method.

More analytically, let us assume that there exist n s solvers and n p problems for each solver s and problem p, requiring a baseline for comparisons, Dolan and Morè compared the performance α p,s (based on a metric) by solver s on problem p with the best performance by any solver on this problem; namely, using the performance ratio r p,s = a p,s min{a p,s : s ∈ S} .

The performance of solver s on any given problem might be of interest, but we would like to obtain an overall assessment of the performance of the solver. Next, the function ρ s was the (cumulative) distribution function for the performance ratio is defined by

ρ s (α) = 1 n p size {p ∈ P : r p,s ≤ a} .
where P is the set of all problems. Notice that the performance profile ρ s : R → [0, 1] for a solver was a non-decreasing, piecewise constant function, continuous from the right at each breakpoint [START_REF] Dolan | Benchmarking optimization software with performance profiles[END_REF].

In other words, the performance profile plots the fraction P of problems for which any given algorithm is within a factor α of the training algorithm. The horizontal axis of each figure gives the percentage of the simulations for which a training algorithm achieved the best performance (efficiency). Regarding the above rules and discussion, we can conclude that one solver whose performance profile plot lies on top right, outperforms the rest of the solvers.

-"Rprop" stands for Resilient back propagation.

-"LM" stands for Levenberg-Marquardt training algorithm.

-"WCNN 1 " stands for Algorithm 1 and bounds on the weights -1 ≤ w i ≤ 1.

-"WCNN 2 " stands for Algorithm 1 and bounds on the weights -2 ≤ w i ≤ 2.

-"WCNN 3 " stands for Algorithm 1 and bounds on the weights -5 ≤ w i ≤ 5.

Figures 2 and3 present the performance profiles based on F 1 -score and accuracy, respectively, investigating the efficiency and robustness of each training method. Firstly, it is worth noticing that all versions of the WCNN outperformed the classical training algorithms, presenting the highest probabilities of being the optimal solvers, relative to both performance metrics. More specifically, WCNN 1 , WCNN 2 and WCNN 3 report 28%, 52% and 42% of simulations with the best F 1 -score, respectively; while Rprop and LM report 26% and 15%, respectively. As regards the classification accuracy, WCNN 1 , WCNN 2 and WCNN 3 exhibit 55%, 63% and 59% of simulations with the highest accuracy, respectively; while Rprop and LM report 35% and 20%, respectively. Thus, we conclude that the application of the bounds on the weights of the neural network, increased the overall classification accuracy.

Furthermore, regarding the performance of the proposed algorithm, WCNN 2 illustrates the best performance, followed by WCNN 1 and WCNN 3 . Therefore, from the interpretation of Figures 2 and3, we are able to conclude that the application of the bounds on the weights of a neural network significantly increased the overall classification accuracy. Nevertheless, in case the bounds were too tight, this did not substantially benefit much the classification performance of the networks.

Performance Evaluation of Weight-constrained Neural

Networks against state-of-the-art Machine Learning Algorithms Next, we evaluate the performance of the weight-constrained neural networks trained with WCNN against state-of-the-art machine learning algorithms. The Naive Bayes algorithm [START_REF] Domingos | On the optimality of the simple Bayesian classifier under zero-one loss[END_REF] was the representative of the probabilistic classifiers while kNN algorithm [START_REF] Aha | Lazy learning[END_REF] was selected as instance-based learner with Euclidean distance as distance metric. From the support vector machines, we have selected the Sequential Minimal Optimization (SMO) algorithm [START_REF] Platt | Using sparseness and analytic QP to speed training of support vector machines[END_REF] using Pearson VII function-based universal kernel [START_REF] Üstün | Facilitating the application of support vector regression by using a universal pearson vii function based kernel[END_REF]. From the decision trees, C4.5 algorithm [START_REF] Quinlan | C4.5: Programs for machine learning[END_REF] was chosen in our study. These classification algorithms constitute some of the most effective and widely utilized machine learning algorithms for classification [START_REF] Wu | Top 10 algorithms in data mining[END_REF] and are implemented in Java using WEKA Machine Learning Toolkit [START_REF] Witten | Data Mining: Practical machine learning tools and techniques[END_REF]. Table 2 presents the performance comparison of the weight-constrained neural networks, which were trained with all versions of WCNN against state-ofthe-art classification algorithms, relative to F 1 -score and accuracy. Notice that the best performance is highlighted in bold for each performance metric. Firstly, it is worth mentioning that the neural networks trained with WCNN 2 exhibited the highest average F 1 -score. As regards the classification accuracy, the ANNs trained with all version of WCNN presented the best performance, outperforming all state-of-the-art classification algorithms.

Based on the above discussion, we are able to conclude out that the weightconstrained neural networks perform significantly better than any presented classification algorithm, reporting the best overall performance.

Conclusions

In this work, we evaluated the classification accuracy of weight-constrained neural networks for forecasting new product's sales increase. By placing constraints on the values of weights, the likelihood that some weights will "blow up" to unrealistic values is considerably reduced; thus all inputs and neurons of the network are efficiently explored. These modified prediction models are efficiently trained with a new training algorithm, called WCNN, which exploits a gradientprojection strategy for handling the bounds on the weights together with the numerical and theoretical advantages of the limited memory BFGS matrices. Our preliminary numerical experiments illustrate the classification efficiency of weight-constrained neural networks in terms of accuracy, compared to state-ofthe-art machine learning prediction models.

Nevertheless, the a-priori determination of the optimal bounds on the weights of the networks is a rather challenging open problem; thus more research and experiments are needed. To this end, the question of what should be the values of the bounds for each benchmark or which additional constraints should be applied is still under consideration. Probably, the required research to answer these questions may reveal additional and crucial information and questions.

Our future work is concentrated on incorporating the proposed methodology to more advanced and complex neural network architectures. Additionally, since our experimental results are quite encouraging, a next step could be the evaluation of the proposed framework in challenging real-world regression problems.

Algorithm 1: WCNN 1 . 2 .

 12 Initiate w 0 , c 1 , c 2 and vectors l and u. Set k = 0. 3. repeat 4.

Fig. 1 .

 1 Fig. 1. Class distribution

Fig. 2 .Fig. 3 .

 23 Fig. 2. Log10 scaled performance profiles for Rprop, LM, WCNN1, WCNN2 and WCNN3 based on F1-score

  at w k .

		/* Stage I: Cauchy point computation */
	6.	Calculate the generalized Cauchy point w C .
	7.	Define the active set A(w C ).
		/* Stage II: Subspace minimization */
	8.	Minimize the quadratic model m k (w)
		w k+1 = arg min

w∈DS m k (w)

Table 1 .

 1 List of attributes

		Type	Values
	Market ID	integer	1-10
	Market size	nominal	{Small, Medium, Large}
	Location ID	integer	1-10
	Age of store	integer	1-28
	Promotion	nominal	1, 2, 3
	Sales (in thousand)	nominal	{Low, Average, High}

Table 2 .

 2 Performance comparison of the weight-constrained neural network trained with WCNN1, WCNN2 and WCNN3 against state-of-the-art classification algorithms, regarding F1-score and accuracy

	Algorithm	F1-score	Accuracy
	WCNN1	77.18%	79.56%
	WCNN2	79.04%	80.22%
	WCNN3	79.17%	79.78%
	Naive Bayes	63.82%	62.77%
	kNN	71.33%	71.53%
	SMO	74.67%	74.45%
	C4.5	77.55%	77.37%

https://www.ibm.com/communities/analytics/watson-analytics-blog/ marketing-campaign-eff-usec_-fastf//


Ioannis P Chochliouros
email: ichochliouros@oteresearch.gr

Anastasia S Spiliopoulou

Alexandros Kostopoulos

George Agapiou

Maria Belesioti

Evangelos Sfakianakis

Michail-Alexandros Kourtis

Mike Iosifidis

Marinos Agapiou

Pavlos Lazaridis

Inclusion of Telemetry and Data Analytics in the Context of the 5G ESSENCE Architectural Approach

Keywords: 5G, edge cloud computing, Mobile Edge Computing (MEC), Network Functions Virtualization (NFV), network management, Small Cell (SC), Network Softwarisation, Telemetry and Analytics, Virtual Network Function (VNF)

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The 5 th Generation of Mobile and Wireless Communications (the so called as "5G") represents a complete revolution of mobile networks for accommodating the overgrowing demands of users, services and application. In contrast to previous transitions between mobile networks generations, in 5G there will be a much complex management requirements based on the softwarisation of network resources [1]. This ultimately will lead to a "system" that requires real-time management based on a hierarchy of complex decision-making techniques that analyse historical, temporal and frequency network data. Simultaneously, the softwarisation feature will contribute to automating processes, optimising costs, reducing time-to-market, providing better services.

Modern 5G networks represent a "shift" in networking paradigms, purely implicating to a transition from today's "network of entities" to a sort of "network of functions". Indeed, this "network of (virtual) functions" resulting in some cases in the decomposition of current monolithic network entities can be a pillar for constituting the unit of networking for next generation systems. The above mentioned functions should be able to be composed upon an "on-demand", "on-the-fly" basis. In fact, a research challenge for managing virtual network functions (VNFs) consists in designing solutions, which identify a set of elementary functions -or blocks-to compose network functions, while today they usually implemented as monolithic. In this framework, uniform management and operations for NVFs are becoming part of the dynamic design of software architectures for 5G. The 5G will not only be a kind of progress of mobile broadband networks but will also create a set of novel and unique network and service capabilities, structuring a form of a sustainable and scalable technology. Consequently, this will support towards establishing a proper ecosystem for technical and business innovation [START_REF]European Commission: 5G: Challenges, Research Priorities, and Recommendations -Joint White Paper[END_REF]. Among current 5G's priorities is also to incorporate advanced automation, autonomicity and cognitive management features [START_REF] Blanco | Design of Cognitive Cycles in 5G Networks[END_REF] to advance operators' efficiency, having a positive impact on the broader competitiveness of the European ICT industry [START_REF] Chochliouros | Challenges for Defining Opportunities for Growth in the 5G Era: The SESAME Conceptual Model[END_REF].

During 5G-PPP Phase-1, the SESAME project [START_REF] Sesame | Small cEllS coordinAtion for Multitenancy and Edge services") 5G-PPP Project[END_REF] evolved the Small Cell (SC) concept by integrating processing power (i.e., a low-cost micro-server) and by enabling the execution of applications and network services, in accordance to the Mobile Edge Computing (MEC) paradigm [START_REF] Chochliouros | A Model for an Innovative 5G-oriented Architecture, based on Small Cells Coordination for Multi-Tenancy and Edge Service[END_REF]. It also provided network intelligence and applications by leveraging the Network Function Virtualisation (NFV) concept [START_REF] Chochliouros | Putting Intelligence in the Network Edge through NFV and Cloud computing: The SESAME Approach[END_REF]. Within the actual scope of 5G-PPP Phase 2, the ongoing 5G ESSENCE project [START_REF]Embedded Network Services for 5G Experiences") 5G-PPP Project, Grant Agreement[END_REF] mainly leverages results from the prior SESAME project as well as from other Phase 1 projects, in order to cover the specific network needs of the vertical sectors and their inter-dependencies. The 5G ESSENCE Project "addresses" the paradigms of Edge Cloud computing and Small Cell as-a-Service (SCaaS) by fueling the drivers and removing the barriers in the Small Cell (SC) market, forecasted to perform play a major role in the 5G ecosystem [START_REF] Chochliouros | A Model for an Innovative 5G-oriented Architecture, based on Small Cells Coordination for Multi-Tenancy and Edge Service[END_REF]. Thus, the 5G ESSENCE project provides a highly flexible and scalable platform, able to support new business models and revenue streams by creating a "neutral" host market and reducing operational costs, by providing new opportunities for ownership, deployment, operation and amortisation. The 5G ESSENCE enhances the processing capabilities for data that have immediate value beyond locality; it also addresses the processing-intensive small cell management functions, such as Radio Resource Management (RRM)/Self Organising Network (SON) and, finally, it culminates with real-life demonstrations. For all the above, the project suggests clear breakthroughs in the research fields of wireless access, network virtualisation and end-to-end (E2E) service delivery.

The 5G ESSENCE's technical approach exploits the benefits of the centralisation of Small Cell functions as scale grows through an edge cloud environment based on a two-tier architecture, that is: a first distributed tier for providing low latency services and a second centralised tier for providing high processing power for computingintensive network applications. This allows decoupling the control and user planes of the Radio Access Network (RAN) and achieving the benefits of Cloud-RAN (C-RAN) without the enormous fronthaul latency restrictions [START_REF]The Benefits of Cloud-RAN Architecture in Mobile Network Expansion[END_REF]. The use of E2E network slicing mechanisms will allow sharing the 5G ESSENCE infrastructure among multiple operators/vertical industries and so customising its capabilities on a per-tenant basis. The versatility of the architecture is enhanced by high-performance virtualisation techniques for data isolation, latency reduction and resource efficiency, and by orchestrating lightweight virtual resources enabling efficient Virtualised Network Function placement and live migration.

Telemetry and Analytics in the 5G Ecosystem

Among other issues, the modern service-driven 5G network architectures intent to deal with and cover a wider set of diversified mobile service requirements, via a flexible and reliable way. With Software Defined Networking (SDN) and Network Functions Virtualisation efficiently supporting the underlying physical infrastructure, the related 5G environment can "cloudify" access, transport, and core networks in a very comprehensible and via a quite reliable manner ( [START_REF] Chochliouros | A Novel Architectural Concept for Enhanced 5G Network Facilities[END_REF], [START_REF] Van Lingen | The Unavoidable Convergence of NFV, 5G, and Fog: A Model-Driven Approach to Bridge Cloud and Edge[END_REF]). Cloud adoption allows for better support for diversified 5G services and, furthermore, it enables the "key technologies" of end-to-end network slicing, on-demand deployment of service anchors and component-based network functions. The extended adoption of SDN, NFV, Cloud and Edge Computing in 5G actual architectures results in environments which are primarily software-driven in nature. In turn, this sort of transformation towards "softwarisation" leads to the emergence of a great variety of challenges in how we can effectively manage and monitor these environments. The respective challenges are further "amplified" when these software functions need to "run" on infrastructure environments built from standard high-volume servers hosting multilayer software functions.

On the other hand [START_REF] Mullins | Cognitive Network Management for 5G. 5G-PPP Network Management & Quality of Service Working Group[END_REF] the intended 5G Network Management is a non-trivial endeavor that faces a host of new challenges beyond 3G and 4G, covering all radio and non-radio segments of the network. Several factors like the number of nodes, the heterogeneity of the access technologies, the conflicting management objectives, the resource usage minimization, and the division between limited physical resources and elastic virtual resources are implicating for a critical change in the respective methodology for realising an efficient network management. In the past frameworks covering previous generations, a distinction was typically made between the control and data plane of the network. Nevertheless, the model of the actual 5G networks can be extended in terms of a "Service and Softwarisation plane", where the management of the network services and the virtualised devices is an integral and indispensable part of the overall network. This approach can be used in order to propose a model for extending the idea of network management to the reliance on an increased overall capacity of computational resources creating a robust solution.

Within the above novel framework, telemetry and analytics are expected to perform a fundamental role in the realisation and management of 5G network architectures [START_REF] Santhosh | Telemetry and Analytics Best Practices and Lessons Learned[END_REF]. Telemetry is an automated communications process (recording and transmission) by which measurements are made and other data collected at remote or inaccessible points and transmitted to receiving equipment for monitoring. Telemetry data may be relayed by using radio, infrared, ultrasonic, GSM, satellite or cable depending on the application (in fact, telemetry is not only used in software development, but also in meteorology, intelligence, medicine and other fields). Telemetry provides the visibility into what is actually happening in real-time within the compute, network and storage infrastructures and at the service layers. Thus, telemetry can offer insights on which features end-users use most, detection of bugs and issues, and offering better visibility into performance without the need to solicit feedback directly from users [START_REF] Pérez-Romero | Monitoring and Analytics for the Optimisation of Cloud Enabled Small Cells[END_REF]. Analytics, in turn, provide actionable insights based on the telemetry data collected. Thus, analytics is the discovery and communication of meaningful patterns in data. Being especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualisation to communicate insight.

The quality and appropriate exploitation of these insights determines the level of intelligence that exists within a relevant 5G-like system architecture, particularly in the ability to automate and to provide fine-grained management of the network infrastructures. Based on actual trends and following to the results of research being in progress, it appears that the 5G architectures will be highly distributed (in particularly with (SC-) small cell-based edge deployments), heterogeneous and dynamic in nature [START_REF] Chochliouros | Using Small Cells from enhancing 5G Networks[END_REF]. These particular characteristics can have significant implications for the design and use of telemetry and analytics systems. For example, when and where analysis takes place will be constrained by the available bandwidth, processing power, network bandwidth etc. Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics will be required to support a wide variety of usage scenarios such as identifying the bandwidth required to deliver a given level of user experience, based on application or user profiles. Analytics will also evolve beyond looking at what has happen historically within the system to provide views on "what will happen in the future", e.g. predictive service KPIs. This type of capability can support smarter and more dynamic capacity planning in order to "size" the infrastructure resources, implicating for more efficient asset utilisation in-line with the needs of the business and customers. Besides, analytics will be required to underpin faster service deployments and more responsive service behaviours based on changes in service context (e.g. bubble loads).

The use of telemetry for infrastructure and network monitoring is a wellestablished discipline. Yet, until today telemetry solutions have been based on a variety of either open source or proprietary tools such as Internet Protocol Flow Information (IPFix)/NetFlow [START_REF] Ranjbar | Using Specialized Maintenance and Troubleshooting Tool in Troubleshooting and Maintaining Cisco IP Networks (TSHOOT)[END_REF] or the Simple Network Management Protocol (SNMP) [START_REF]SNMP: Simple Network Management Protocol in Network Protocol Handbook[END_REF] (e.g. traps and pulling), etc. In fact, SNMP is the de facto telemetry protocol for collecting data from network devices. Nevertheless, its applicability diminishes significantly in NFV/SDN environments. Here, computer infrastructure type metrics have more significance, however not completely at the expensive of standard network oriented metrics. In the forthcoming 5G environments there will be significant more diversity in the sources of telemetry data which must consumed due to a large network edge footprint and wider variety of use cases. The situation becomes even more exacerbated in multi-vendor environments, due to the complexity of device heterogeneity in different network segments. The variety and diversity of potential data sources results in different data format, data models, resolution, nonstandard interfaces, etc., making it challenging to have a fully correlated and end-toend view of the infrastructure environment and the services running in the environment. Addressing these challenges requires a telemetry fabric which provides an end-to-end platform for metrics collection and processing. The fabric also needs to have multiple hierarchical analytical and actuation points in order to efficiently exploit the metrics [START_REF] Mcgrath | Evolving End to End Telemetry Systems to Meet the Challenges of Softwarized Environments[END_REF].

In 5G the use of approaches such as network slicing, push data strategies, embedded metadata, dimensional reduction, in-situ data processing etc., will be used to provide better level of control over the availability and quality of telemetry data. These capabilities lay the foundation for supporting critical analytics which makes possible to achieve real-time and automated intelligence that can seamlessly travel from the cloud to a large number of distributed end-points [START_REF] Barrett | Building 5G: Data Analytics and Artificial Intelligence[END_REF]. To date, the application of telemetry metrics and analytics has focused primarily on the general health and performance of services and host infrastructures. In the 5G this focus is expected to be extended so that to support the efficient deployment, management and optimisation of services and infrastructures. Thus, the range of expected use cases can include [START_REF] Mande | SDN-NFV for Telco DC[END_REF], inter-alia: (i) Infrastructure, network health; (ii) troubleshooting, forensic system observations, resolution; (iii) SLA (Service Level Agreement) compliance, KPI monitoring, prediction, performance tuning; (iv) dynamic reconfiguration (e.g. scaling, migration, load balancing); (v) security threat identification/remediation; (vi) capacity planning; (vii) resource/feature allocation optimisation, and; (viii) deployment model performance, topology confirmation.

While many of these use cases not necessarily knew their operational implementation, this will require different behaviours from the telemetry and analytics systems in order to "meet" the significantly different needs of 5G. First is the need to "address" the big data challenges of volume, variety and velocity. Telemetry will also need to become more software-defined. A trend which has already been embraced within the network domain in the form of software-defined networking is the ability to dynamically change the behaviour of telemetry systems (i.e. enable/disable metrics, change data resolution, add/remove metadata, etc.) and this will become important. This capability is necessary in order to adapt to dynamic service environments, particularly at the network edge in response to new services, architectural changes, changes in user behaviour or service provider business needs. While, initially, this can primarily be a human-driven activity, telemetry systems in concrete with management and orchestrations systems (MANO) are expected to evolve to higher levels of self-autonomy in order to "dynamically adapt", based on analytically driven behaviour evolution [START_REF]ETSI): NFV Management and Orchestration -An Overview[END_REF]. Telemetry systems will also need to become hierarchical in nature, following to the wider 5G evolutionary trends. While there is a trend towards centralisation, this unlikely seems to be scalable particularly in high distributed multi-domain edge networks.

Furthermore, telemetry systems are more likely to be domain specific with first level data centralisation within that domain. Selected data from lower level domains can then be aggregated at higher level domains within a dedicated hierarchical systems architecture, as shown in Fig. 1. The higher level analytics can also provide specific inputs into lower level analytics -or vice versa-in order to improve either local or global optimisation requirements. A hierarchical approach also distributes issues such as data normalisation, transport mechanisms data models and timestamps in more manageable blocks.

Telemetry, Analytics and Orchestration Framework in the Revised 5G ESSENCE Architecture

Fig. 2 provides a high-level architecture of the 5G ESSENCE according to the two-tier approach explained in detail in [START_REF] Chochliouros | Enhancing Network Management via NFV, MEC, Cloud Computing and Cognitive Features: The "5G ESSENCE" Modern Architectural Approach[END_REF]. At the network's edge, each CESC is able to host one or more service VNFs, directly applying to the users of a specific operator. Similarly, VNFs can be instantiated inside the Main DC (Data Centre) and be parts of a Service Function Chaining (SFC) procedure. The Light DC can be used to implement different functional splits of the Small Cells as well as to support the mobile edge applications of the end-users. At the same time, the 5G ESSENCE proposes the development of small cell management functions as VNFs, which run in the Main DC and coordinate a fixed pool of shared radio resources, instead of considering that each small cell station has its own set of resources. The CESC (Cloud Enabled Small Cell) offers virtualised computing, storage and radio resources and the CESC cluster is considered as a cloud from the upper layers. This cloud can also be "sliced" to enable multi-tenancy. The execution platform is used to support VNFs that implement the different features of the Small Cells as well as to support for the mobile edge applications of the end-users. the 5G ESSENCE architecture allows multiple network operators (tenants) to provide services to their users through a set of CESCs deployed, owned and managed by a third party. The CESC Manager (CESCM) is responsible for coordinating and supervising the use, the performance, and the delivery of both radio resources and services. It controls the interactions between the infrastructure (CESCs, Edge DC) and the network operators. In addition, it handles Service Level Agreements (SLAs) while, on an architectural basis, CESCM encompasses telemetry and analytics as fundamental tools for efficiently managing the overall network. The Virtualised Infrastructure Manager (VIM) is responsible for controlling the NFV Infrastructure (NFVI), which includes the computing, storage and network resources of the Edge DC.

It should be mentioned that the 5G ESSENCE does not only propose the development and adaptation of the multitenant CESC platform, the virtualisation infrastructure and the centralisation of the software-defined radio resource management described above; it also "addresses" several aspects that affect performance in 5G virtualised environments such as virtual switching, VNF

UPF

migration, and Machine Learning (ML) algorithms, which allow orchestrating diverse types of lightweight virtual resources. Last but not least, it is worth noting that the abovementioned two-tier architecture of the 5G ESSENCE is well aligned with the current views on 5G architecture described by 5G-PPP in [START_REF] Redana | Views on 5G Architecture, White Paper of the 5G-PPP Architecture WG[END_REF], where the infrastructure programmability and the split of control and user planes are identified as two key logical architecture design paradigms for 5G. First, the 5G ESSENCE achieves infrastructure programmability by leveraging the virtualised computation resources available at the Edge DC. These resources will be used for hosting VNFs tailored according to the needs of each tenant, on a per-slice basis. Second, the Main DC allows centralising and softwarising control plane SC functions to enable more efficient utilisation of radio resources coordinated among multiple CESCs. In addition to the abovementioned aspects, the 5G ESSENCE contributes to other 5G architectural concepts identified in [START_REF] Redana | Views on 5G Architecture, White Paper of the 5G-PPP Architecture WG[END_REF] such as, for example, the realisation of the network slicing concept, which is a fundamental requirement of the 5G ESSENCE for enabling that multiple tenants and vertical industries share the same CESC infrastructure. The 5G ESSENCE system presents a high degree of dynamicity, due to the constantly changing behaviour of services and workloads to be supported by the radio and cloud infrastructure. This perspective implicates for a proper monitoring system, able to adapt to the different supported scenarios. Data collected by this system shall be used for visualisation purposes (for human consumption) and it is also provided to a set of analytics techniques capable of extracting insights from the data and, via feedback loop, enabling the realisation of efficient resource allocation across the infrastructure through the orchestration system. These are the functionalities provided from the 5G ESSENCE CESCM.

In the scope of the revision and extension of the original 5G ESSENCE architecture, below we discuss the proposed components of telemetry, analytics and orchestration that can further enrich the original architectural approach.

Telemetry Module

One of the challenges for telemetry in the 5G small cell deployments considered in the 5G ESSENCE is the degree of distribution resulting from the distribution of infrastructure functionalities across different nodes of the network going from a centralised location (the Main DC) to the very edge of the network (Light DC) where the small cells are deployed (also see Fig. 2). Then, to achieve a full end-to-end view of both infrastructure and services, the proposed for inclusion telemetry system should be able to instrument and monitor the different devices composing the overall infrastructure as well as to provide a unique and simple-to-access view of the system that can be exposed to both dashboards and analytical techniques. Another challenge is the high complexity which characterises the overall telemetry system as the huge number of metrics and volume of data to be collected, processed and analysed, increases the complexity of the decision-making modules, slowing down the reaction time of the system and implicitly increasing service latency. The telemetry system is then required to be distributed in nature and to adapt to the constantly changing needs of the infrastructure. For this purpose, two "key" desired characteristics of the 5G ESSENCE telemetry platform are: (i) The capability to generate aggregated and derived metrics, and; (ii) the capability to store and, consequently, access the data locally by using a distributed monitoring approach.

The telemetry functionality is implemented through a number of agents that are distributed in different parts of the architecture, both at the Light and the Main DC, while the analytics functions are to be placed at the Main DC. Something similar occurs with the OSM (Open Source MANO) orchestrator, which is logically associated to the CESCM, but it physically resides at the Main DC. The telemetry module is organised in two main components, that is: Network Service Monitoring and Landscaper. The former is in charge of collecting counters and events, in the form of metrics, from the NFVI, the services running on the 5G ESSENCE infrastructure and the SD-RAN (Software Defined -Radio Access Network) controller. This is supported by the Prometheus monitoring tool and a set of exporters, which provide the capability to extract from the system the required information and send that to Prometheus. In fact, Prometheus natively supports hierarchical federation capabilities that provide to the 5G ESSENCE monitoring system the required flexibility and reliability. Another very important functionality provided by the above fundamental component is the Alerting system, which, based on the continuous monitoring of metrics, can identify deviations from normal behaviours of the services and the infrastructure and so to send a signal to the orchestration system. An important aspect of the 5G ESSENCE monitoring framework is the capability of monitoring the RAN, as a difference from most of the existing telemetry platforms, which have typically been used for the collection of measurements related to Information Technology (IT) infrastructure components, e.g., CPU (Central Processing Unit) usage, memory, operating systems, etc. In contrast, the monitoring of the RAN involves collecting radio interface-related measurements from the CESCs.

The 5G ESSENCE considers multi-RAT CESCs that support 4G, 5G and Wi-Fi technologies. The 4G LTE small cells collect a number of measurements at the physical (PHY) layer [START_REF]The 3 rd Generation Partnership Project[END_REF] and at the Layer 2 [START_REF]The 3 rd Generation Partnership Project[END_REF]. These measurements can be collected by the small cell or by the User Equipment (UE) that reports them to the small cell. Similarly, for 5G NR (New Radio) the list of PHY measurements is given in [START_REF]The 3 rd Generation Partnership Project[END_REF], while Wi-Fi measurements are described in [START_REF]The 3 rd Generation Partnership Project[END_REF].

Although the above measurements are available at the small cells, the capability of a telemetry platform -like Prometheus-to collect them depends on the actual configuration of measurements that each small cell exposes to an external system, typically through management interfaces defined between the small cell and the EMS (Element Management System) and/or NMS (Network Management System). The interface between the small cells and their EMS is typically vendor specific, but there have been some efforts in defining open standard interfaces such as TR-196 [START_REF]FAP Service: 2[END_REF] supported by multiple vendors. Similarly, 3GPP has also standardised different Performance Measurement (PM) metrics [START_REF]The 3 rd Generation Partnership Project[END_REF] and Key Performance Indicators combining these metrics [START_REF]The 3 rd Generation Partnership Project: 3GPP TS 32[END_REF] to be transferred from the small cells (or their EMS) to the NMS. These PM metrics are provided in the form of XML files following the format of [START_REF]The 3 rd Generation Partnership Project[END_REF] and produced according to a configured reporting interval. Each file can contain one or more granularity periods, which define the time across which measurements are collected and aggregated. In the 5G ESSENCE, the PM files are generated by the cSD-RAN controller. It exposes the relevant metrics to a Representational State Transfer (REST) Application Programming Interface (API) that generates a JavaScript Object Notation (JSON) service that is then translated by a specific exporter, which consumes and translates the data to be understandable by Prometheus. Telemetry data obtained from the RAN are then subject to analytics approaches to support the decisions made by different RRM/SON functions for which the cSD-RAN Controller is in charge (and being the core objective of the WP3 of the 5G ESSENCE effort), as seen in Fig. 3. The monitoring framework of the 5G ESSENCE is designed to also collect information about the cloud resources. First the framework requires supporting the identification of available physical resources, which will be allocated to VNF Components and then it requires monitoring the interdependency between virtual and physical resources. In the 5G ESSENCE monitoring framework, this functionality is covered by the Landscaper shown in Fig. 3. The Landscaper is responsible for collecting information related to the resources available in the system. It gathers all the available resources on the NFVI and structures them in the form of a graph, where nodes represent the resources and edges represent relationships between them. Nodes are organised in layers (physical, virtual and service) in order to enable a logical classification of the resources.

Analytics Module and Orchestration Module

The analytics module in Fig. 3 is envisioned as a collection of tools and approaches to support and execute Analytics/Machine Learning (ML) tools on the telemetry data to generate models to be used for provisioning of insights to the orchestration system (i.e., OSM), and to the cSD-RAN controller of the small cells. The analytics module is characterised by two main components: The Contextual Information component and the Analytics Framework. The Contextual Information component is responsible for the storing of contextual data for services and NFVI and receives its input from the Landscaper (resource mapping), the Network Service Monitoring (telemetry metrics) and the Analytics Framework (models generated over time). This information is exposed to the Analytics Framework where it is processed to generate actionable knowledge, in terms of models, that will support orchestration decisions. This information is sent to the Orchestration system which exploits it for efficient placement and intelligent resource allocation and to the cSD-RAN Controller, which, as mentioned, makes informed and intelligent decisions on radio resource allocation.

The Orchestration module includes the Service Orchestrator (SO) and the Resource Orchestrator (RO) as part of Open Source MANO (OSM), and the Virtual Infrastructure Manager (VIM) as the combination of OpenStack and OpenDaylight (ODL). Moreover, two extra modules are envisioned in the final implementation of the overall orchestration system, which are the Placement Assistant and the Alert Mitigation Manager. The Placement Assistant intercepts the deployment requests sent to the Orchestrator from the 5G ESSENCE User Portal and translate that into an efficient deployment of components based on hints received from the Analytics Framework. The Placement Assistant is responsible for decisions related to the resource allocation for the service components at the deployment time (i.e. where to allocate resource, Main DC vs. Light DC). The Alert Mitigation Manager is instead responsible for managing the service at the runtime, according to anomalous behaviour identified by the Network Service Monitoring. The Network Service Monitoring sends triggers to the Alert Mitigation Manager when something anomalous is measured and the latter decides how to react to the event, on the basis of hints received from the Analytics Framework. The Alert Mitigation Manager is responsible for decisions related to the dynamic adjustment of service configuration and resource allocation.

Conclusion

In the present work we have identified the importance of development and inclusion of network softwarisation features within the wider 5G evolutionary scope which, in turn, can lead to more effective network management promoting cognitive features and related behaviors. Based on the specific novel context of the actual 5G ESSENCE research program, being part of the 5G-PPP Phase 2 effort, we have summarised the proposed innovative features of the respective architecture allowing for a dynamic management of the involved network resources as well as for the promotion of a variety of facilities via dedicated VNFs. The wide implementation of SDN, NFV, Cloud and Edge Computing in present 5G architectures, and within the 5G ESSENCE in particular, leads to network operating environments being software-driven to a great extent. The transition towards "softwarisation" schemes implicates for greater challenges, regarding management and monitoring of the related infrastructures.

In this framework, both telemetry and analytics are expected to realise a very important role as they can both offer various insights upon a variety of features affecting both network and service performance within the intended 5G architectures. This can be a very important factor towards contributing in the ability to automate and provide fine-grained management of the related 5G network infrastructures.

Within the context of the updated and revised 5G ESSENCE architecture, the CESCM is the central service management and orchestration component. Under a more generalised approach, the CESCM integrates all the traditional network management elements and the novel recommended functional blocks to realise NFV operations and it is responsible for coordinating and supervising the use, performance and delivery of both radio resources and services. In addition, it is also responsible for controlling interactions between the infrastructure and the network operators and it handles SLAs. The CESCM incorporates telemetry and analytics as necessary tools for properly managing the overall network. Aiming to extend and refine the original architectural scope of the 5G ESSENCE, and towards achieving a full end-to-end view of both infrastructure and services, we have proposed for inclusion a suitable telemetry module. The related functionality can be implemented through a number of agents, while the telemetry module is organised in two main components (the Network Service Monitoring and Landscaper), each one serving specific purposes.

In the same approach, we have proposed the inclusion of an analytics module, with the purpose of supporting and performing Analytics/Machine Learning (ML) tools on the telemetry data to produce models for offering of insights to the orchestration system and to the cSD-RAN controller of the small cells. Aiming to "widen" all potentially expected benefits, we have also proposed an orchestration module, for better supporting dynamic adjustment of service configuration and resource allocation. These modules can enhance the original architectural 5G ESSENCE considerations to a more reliable level for a more effective cognitive management of the network resources and of the proposed facilities.

Fig. 1 .

 1 Fig. 1. General View of Hierarchical Telemetry Domains.

Fig. 2 .

 2 Fig. 2. The 5G ESSENCE Refined High Level Architecture.

Fig. 3 .

 3 Fig. 3. Components of the Telemetry, Analytics and Orchestration Framework in the 5G ESSENCE Refined High Level Architecture.

Acknowledgments. This work has been performed in the scope of the 5G ESSENCE European Research Project and has been supported by the Commission of the European Communities (5G-PPP/H2020, Grant Agreement No.761592).


Maria Rita Spada

Alessandro Vincentini

Trustworthy AI for 5G: Telco Experience and Impact in the 5G ESSENCE

Keywords: 5G Networks, AI, Agents, BSS, Neutral Host, OSS, Trust

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The telecommunications are one of the most interesting contexts involved in the process of the AI (Artificial Intelligence) development, because in real life situations it might happen that sensors, actuators and AI agents are away from each other and one -or more-telecommunications network(s) lay in between and connect them. In general, the communications networks are clearly the infrastructure that transfers the information generated by the sensors to the remote AI agents. The new 5G and FTTH (Fibre-to-the-Home) networks are the main connective tissue of the artificial intelligence systems located throughout the territory.

When a Telco operator wants to use the new solution based on AI, it is important to take into account some "values" with which AI should comply to set out several concrete requirements (such as privacy, accountability and transparency) for AI systems integrated with other systems used to create value and to deal with the complexity of the information and communication aspects.

Given the complexity of the services offered, it is necessary to use policies on different levels so that to evaluate AI models and their integration in the normal context. When a Telco includes AI solutions it is necessary to develop governance frameworks in order to make sure that AI development does not infringe upon different aspects, like human rights, freedoms, dignity and privacy; there will be the possibility of programming, by using the AI solutions, co-robot ethics to legal and other questions, including liability and privacy concerns. In this framework, the Telecom Operator must have a reliable approach on trustworthy AI so that to offer solutions that "people can trust", according to transparency and responsibility requirements.

The present paper is organized as follows: Section 2 describes the new approach for realizing trustworthy AI for 5G solutions; Section 3 describes some AI applications in the network, with a particular focus upon the 5G network; an overview of the general architecture and how this can be deployed is introduced in Section 4, taking into account as reference the 5G ESSENCE solution. Section 5 underlines the benefit of the new approach and the improvement of the realization and management of the network slicing concept -or of other aspects-correlated when using the AI solution.

Approaches for Realizing Trustworthy AI for 5G Solutions

It is important to take into account the concept of the model for network management that can be implemented by using the 5G capabilities, when it is also possible to integrate the AI capabilities.

In fact, when we design the network slicing model for a Telco, or more generally for a Neutral Host, an intermediate provider between manufacturer or RF equipment provider and Mobile Operators/Service Providers, we have to deal with the possible conflict in the network between special services and equally worthy of protection, such as that which is given to an electro-medical device or to a machine that can manage the braking system of a vehicle. Which of the two applications is more appropriate to give priority in case of problems? How is this decision made? To date, there is no clear answer; perhaps the only principle that can be considered is that it should be established a priori in order to define some rules that will be applied, when we have millions of people using such services. The first aspect to be defined is that of the relationship(s) between the network operators, the software and hardware developers, the deployers and the customer(s). Everyone among these has a central but different role, and everyone has different responsibilities.

The network indeed can experience congestion or outages and, when this occurs, the transferred information can be delayed or even discarded. Networks, in fact, are dimensioned when using the criteria adopted to define the slices.

From a Telco's point of view, two important safety properties must be guaranteed and these have already been explored by the researchers, that is: the "safe interruptibility" and the "distributional shift", where:

1. Safe interruptibility is the ability to interrupt an intelligent agent and override its actions at any time. 2. Distributional shift is the ability of the AI system to behave robustly when the environment where it operates differs from the training environment. According to this vision, a Telco is not responsible for any failure not being able to achieve the two properties, as mentioned above. The reason why networks cannot be considered as responsible of the possible fallacies of the ensemble "sensors-actuators-AI agent" is that zeroing the probability of congestion would require infinite network resources, which is clearly impossible. A fall-back strategy for the AI agent in this scenario is in charge to developers, expected "by design": it is important to create a strong ecosystem to share this approach. The AI equipment should sense the "hiccup information" of the surrounding congested networks and swiftly stop what it is doing, thus applying the principle of safe interruptibility.

This applies even more when the consequences of wrong decisions made by an AI used are well diverse, depending on the field these are made. Wrong decisions in a medical context are clearly much worse than a wrong suggestion provided to a consumer by a recommender system. It is important to share common guidelines to cope with these aspects; besides it should be requested to have clear and unequivocal criteria to support an electronic communications network operator so that to make choices when network resources are insufficient to manage one or more mission critical services that transit networks simultaneously.

In the event an inconvenience is unavoidable anyway, e.g. when humans are damaged, injured or killed due to a decision taken by AI systems, AI system must decide with fairness.

Another issue, important from a Telco's point of view, is the correct assessment of the security measures to be provided for AI systems when we include this in the operational context integrating the new features in the different systems. In particular, it might be useful to define a classification of AI systems according to their intended use in order to define the security measures adequate to the context of applications, as discussed in "Ethics guidelines for the development and use of artificial intelligence" Errore. L'origine riferimento non è stata trovata..

It is also important to define security measures to ensure the availability and integrity of data throughout the life cycle of the entire AI system. Among the measures that help to ensure the robustness of the AI system is necessary to foresee the segregation of all environments, including those provided for the AI training.

According to Telco's experience, the human supervision is only one of the possible instances of governance of all these elements; with more experience, it could be possible to evaluate the hypothesis of the aid of other AI systems to support human supervision, which carry out extemporaneous (therefore unpredictable) redundancy checks in environments separated by AI system to be governed in a sort of "logic" of "assisted governance". The autonomy of the AI system also depends on its ability to interact: that is, some context in the network management could be used as first field for the application of this approach.

This concept of governance then extends to the autonomous interaction of the AI system with other systems networks and devices; in this way it could be possible in the near future that network systems could be equipped with the capacity to autonomously expand their interactions within a predefined "evolutionary perimeter" during the design phase.

The AI, equipped with its own start-up of a minimum set of connections to protect potential vulnerabilities, could independently activate further systemic interactions to increase its evolutionary degree. The improvement in efficiency would correspond to a reduction in risks related to vulnerability. This will improve the capacity to include the AI in the Telco's context to manage better the network and the final services to the end-users.

In particular, we believe that an assessment lists should be enriched by items to detect the correct application of "safe interruptibility" and "distributional shift", or other applicable procedures to avoid danger in case of outage of communication networks.

Further fine tuning should be applied on supervised machine learning algorithms and AI's essentially learn what they are taught. Therefore, for them to be fair and nondiscriminatory, a proper choice of the training set is crucial to avoid imbalances and inequalities when classifying (i.e. with the term "imbalance" we mean that the classification results are not distributed evenly across all the classes forming the domain of the classification problem).

Since miss-classification might lead to uneven decisions and bias reinforcement, an approach to mitigate the risk of miss-classification could consist in the assessment of the training sets in search of imbalances not necessarily intended.

Moreover, we generally agree on the principle that it is needed to guarantee not only the concept of "privacy by design" but also the concept of "ethics by design"; in this way, we require ethical principles (and privacy) to be embedded in AI products and/or services, right at the beginning of the design process.

In fact, ethics must be embedded into the design and development process from the very beginning of the AI creation and those ethics must be aligned with the values and ethical principles of a society or of the community it affects. For this reason, it could be necessary to use the same approach in Europe to guarantee the process. Each company has to share a common vision and follow an internal procedure to guarantee principles for trust and transparency. By adopting and practicing this approach, it could be more clear and transparent about how to use the AI solutions in different contexts.

AI packed Approach to 5G Business Case

Artificial Intelligence requires an ongoing and interdisciplinary effort to cover all the effects in the different ecosystems where the new approach will be introduced. Guidelines should give clear and unequivocal criteria to support a communications network operator so that to make choices when network resources are insufficient in order to manage one or more mission critical services that transit networks, simultaneously: the consequences of wrong decisions made by an AI are well diverse depending on the field they are made. Wrong decisions, in a medical and/or transport contexts, are clearly much worse than others. Therefore, we think that the regulation of the safety topic should be domain specific.

In the same way, it is important to realise "Trustworthy AI" according to a shared framework compatible with the general architecture described in [2]. We look at the opportunities of enhancing our tools for the management of the network and to improve our OSS (Operations Support Systems) services -or in other sectors-so that AI can be "trained" to help improve diagnoses -or other outcomes-to improve the quality of the final services in different contexts (such as smart mobility, smart cities or industry 4.0).

The OSS/BSS (Operational and Business Support Systems) service provides Operating and Business supports functions in order to manage the network infrastructure (OSS) as well products, customers and customer life cycle (i.e.: Orders, Upgrades, Complains, Usage & Invoicing, Leave) of the services provided by the operators (BSS).

The need for highly automated processes is particularly stressed from a telco's point of view, due to frequency/network/IT (Information Technology) resource sharing in deployment and provisioning processes, having in mind the maximisation of the customer experience, services and network resources.

As the network infrastructure moves towards virtualized infrastructures, OSS and BSS systems evolve the virtualization path based on IaaS (Infrastructure-as-a-Service), PaaS (Platform-as-a-Service) or SaaS (Software-as-a-Service), by using a common commercial hardware and storage and by providing separate virtualized infrastructure, virtualized platform and virtualized software to a set of tenants using the shared physical infrastructure. Specific application environments can be then made available for vertical implementations, including Artificial Intelligence and Machine Learning functionalities in order to provide enhanced analytics needed for a quick service time to market. In the following we discuss a few examples of telco's contexts in which we think that the introduction of AI will be an important challenge to improve the Telco's role:

1. Network Management: More and more data are generated by devices across the network and used to manage itself; other network sources can provide insight into how networks are performing. The way how that data is collected and analyzed to provide meaningful and actionable information is the "key" to make a difference in network operations. The AI tools can help Telcos to monitor and evaluate network performance and these tools are comprehensive enough to deliver a complete and holistic picture of the entire network (from the user terminal to the cloud through the edge). 2. Product Management: A Telco organization is typically responsible for managing the Product/Service Catalogue through the assembly and update of products utilizing available components. The definition of a "product" is an item that satisfies a market's want or need. The introduction of the AI approach will improve the Telco's ability to create and maintain products that can be sold to customers in the target market. It could be possible via using the AI approach to model the structure of a product, creating and managing this in a "catalog" of products or a number of components associated together to create new opportunities to reduce the time to market. According to this, some of the components within a product will be enabled by shared/common/reusable resources (e.g., network exchange) that may be managed by different parts of the organization, improving the global efficiency of a Telco organization. 3. Customer Contact Management: Another context in which the introduction of an AI approach is important, is the "customer contact management" dealing with the retention and loyalty aspects; the Customer Relationship Management (CRM) suite allows a telco operator to create, update and view the customer's information (i.e.: names, addresses, phone numbers, organizational hierarchy), as well as to record and view all customer interactions across different communication channels and department. The AI solution will offer better capabilities to highlight customers as risk of switching to an alternative carrier and provide comparisons with other operator's service packages, allowing customer care agents to provide advantages for the business intelligence platforms. 4. Customer Order Management: Starting from the telecoms experience, the introduction of AI solutions will help in the design, price and the lifecycle of the proposed services. Besides, the AI will offer support to negotiation and closure of a formal contract with the customers to deal with the creation of the customer's contract and of any associated service level agreements, including approval of custom language, customer contract sign-off, appropriate counter signature and contract expiration taking into account all the "smart" elements coming from an analysis based upon AI tools. In particular, Customer Order Management application handles order requests to suspend, resume, change ownership, amend, add, change and discontinue existing ordered products. By using AI it will be easier to deal with repackaging of the purchased offers into alternate product offering (may require sales/contract negotiation).

In the telco contexts in which we think that the introduction of AI will be an important challenge to improve the Telco's role, as discussed, the AI is going to be part of any application and network controller. In 5G scenarios we can assume that AI solutions can be part of business proposition of specific use case. In particular, proposition depends upon specific use case; when assuming a Network Slice for a Smart City scenario the proposition could include specific AI services for sensor to be deployed on edge or in the scenario of broadband network slice AI-based monitoring within vEPC (virtualised Evolved Packet Core) solution.

The 5G ESSENCE Solution

The 5G ESSENCE project Errore. L'origine riferimento non è stata trovata. addresses the paradigms of Edge Cloud computing and Small Cell as-a-Service (SCaaS) by fueling the drivers and removing the barriers in the Small Cell (SC) market, forecasted to grow at an impressive pace up to 2020 and beyond and to play a "key role" in the 5G ecosystem. The project defines the baseline system architecture and interfaces for the provisioning of a cloud-integrated multi-tenant Small Cell network and a programmable radio resources management (RRM) controller.

The introduction of AI solutions will improve the capability of the system to development of the centralized software-defined radio access network (SD-RAN) controller to program the radio resources usage in a unified way for all CESCs (Cloud-Enabled Small Cells) and to distribute service management in a multi-tier architecture.

As already introduced, the 5G ESSENCE offers an innovative architecture, capable of providing Small Cell coverage to multiple operators "As-a-Service", enriched with a two-tier architecture: a first distributed tier for providing low latency services and a second centralized tier for providing high processing power for compute-intensive network applications. To that end, 5G ESSENCE envisages to virtualize and to partition Small Cell capacity while, at the same time, it aims to support enhanced edge cloud services by enriching the network infrastructure with an edge cloud.

The introduction of the approach based on AI techniques in a project like the 5G ESSENCE will improve the capabilities of the solution not only to cope with the complex network based on the combination of MEC (Mobile Edge Computing) and NFV (Network Function Virtualisation) concepts with Small Cell virtualization in 5G networks, but also to cope better with the complex ecosystem required for supporting multi-tenancy and for increasing the network capacity and the available computational resources at the edge.

The proposed solution allows multiple network operators (tenants) to provide services to their users through a set of Cloud Enabled Small Cells deployed, owned and managed by a third party (i.e., the CESC provider). Fig. 1 shows a high level view of the envisaged architecture.

Fig. 1. 5G ESSENCE architecture

In the 5G ESSENCE architecture Errore. L'origine riferimento non è stata trovata. the NFVI (Network Function Virtualization Infrastructure) spans across the Main Data Centre-DC (i.e., the core cloud) and Light DCs (i.e., edge clouds), and this requires dealing with wide distribution and heterogeneity. high characterizing the system calls for an efficient, distributed and adaptable monitoring system. The monitoring data is provided to an analytics engine to extract insights for the support of orchestration decisions and to enable efficient resource allocation across the overall infrastructure. An end-to-end orchestration of infrastructure and services is crucial for reliable deployment and execution of services. The 5G ESSENCE orchestration platform also manages the lifecycle of services and it fully supports an ETSIbased view of the MANO (MANagement and Orchestration) [START_REF] Etsi | Network Functions Virtualisation (NFV); Management and Orchestration 001 V1[END_REF]. For all these reasons this context could be a good reference point to start in the evaluation of the AI introduction, to improve the global systems using an approach like the Machine Learning as a Service (MLaaS) or the ML Market Places; it could be possible to install specific algorithm at edge level or in the distributed sensors or to use specific APIs, as suggested by some already available solutions described in [6] or in [7].

Conclusion

The integration of such AI capabilities demonstrates how the new integrated approach contributes to 5G architectural concepts such as, for example, the improvement of the realization and management of the network slicing concept or the other aspects correlated to the BSS/OSS context, in particular enriching the 5G services catalogues with different AI-based functions.

Acknowledgements. This work has been partly funded by the EC in the context of the 5G ESSENCE project (Grant Agreement No.761592).