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Research Challenges in Coupling Artificial Intelligence and Network Management

Document Type Active Internet-Draft (nmrg RG)
Authors Jérôme François , Alexander Clemm , Dimitri Papadimitriou , Stenio Fernandes , Stefan Schneider
Last updated 2024-03-04
Replaces draft-francois-nmrg-ai-challenges
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Intended RFC status Informational
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Document shepherd Laurent Ciavaglia
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Internet Research Task Force                                 J. François
Internet-Draft                        University of Luxembourg and Inria
Intended status: Informational                                  A. Clemm
Expires: 5 September 2024                   Futurewei Technologies, Inc.
                                                        D. Papadimitriou
                                            3NLab Belgium Reseach Center
                                                            S. Fernandes
                                                  Central Bank of Canada
                                                            S. Schneider
                                  Digital Railway (DSD) at Deutsche Bahn
                                                            4 March 2024

  Research Challenges in Coupling Artificial Intelligence and Network


   This document is intended to introduce the challenges to overcome
   when Network Management (NM) problems may require to couple with
   Artificial Intelligence (AI) solutions.  On the one hand, there are
   many difficult problems in NM that to this date have no good
   solutions, or where any solutions come with significant limitations
   and constraints.  Artificial Intelligence may help produce novel
   solutions to those problems.  On the other hand, for several reasons
   (computational costs of AI solutions, privacy of data), distribution
   of AI tasks became primordial.  It is thus also expected that network
   are operated efficiently to support those tasks.

   To identify the right set of challenges, the document defines a
   method based on the evolution and nature of NM problems.  This will
   be done in parallel with advances and the nature of existing
   solutions in AI in order to highlight where AI and NM have been
   already coupled together or could benefit from a higher integration.
   So, the method aims at evaluating the gap between NM problems and AI
   solutions.  Challenges are derived accordingly, assuming solving
   these challenges will help to reduce the gap between NM and AI.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at

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   Internet-Drafts are draft documents valid for a maximum of six months
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   This Internet-Draft will expire on 5 September 2024.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Conventions and Definitions . . . . . . . . . . . . . . . . .   5
   3.  Acronyms  . . . . . . . . . . . . . . . . . . . . . . . . . .   5
   4.  Difficult problems in network management  . . . . . . . . . .   6
   5.  High-level challenges in adopting AI in NM  . . . . . . . . .   9
   6.  AI techniques and network management  . . . . . . . . . . . .  11
     6.1.  Problem type and mapping  . . . . . . . . . . . . . . . .  11
       6.1.1.  Sub-challenge: Suitable Approach for Given Input  . .  12
       6.1.2.  Sub-challenge: Suitable Approach for Desired
               Output  . . . . . . . . . . . . . . . . . . . . . . .  13
       6.1.3.  Sub-challenge: Tailoring the AI Approach to the Given
               Problem . . . . . . . . . . . . . . . . . . . . . . .  14
     6.2.  Performance of produced models  . . . . . . . . . . . . .  15
     6.3.  Lightweight AI  . . . . . . . . . . . . . . . . . . . . .  17
     6.4.  Distributed AI  . . . . . . . . . . . . . . . . . . . . .  18
       6.4.1.  Network management for efficient distributed AI . . .  18
       6.4.2.  Distributed AI for network management . . . . . . . .  19
     6.5.  AI for planning of actions  . . . . . . . . . . . . . . .  20
   7.  Network data as input for ML algorithms . . . . . . . . . . .  22
     7.1.  Data for AI-based NM solutions  . . . . . . . . . . . . .  22
     7.2.  Data collection . . . . . . . . . . . . . . . . . . . . .  24
     7.3.  Usable data . . . . . . . . . . . . . . . . . . . . . . .  25
   8.  Acceptability of AI . . . . . . . . . . . . . . . . . . . . .  27
     8.1.  Explainability of Network-AI products . . . . . . . . . .  27
     8.2.  AI-based products and algorithms in production systems  .  28
     8.3.  AI with humans in the loop  . . . . . . . . . . . . . . .  30
   9.  Security considerations . . . . . . . . . . . . . . . . . . .  31
     9.1.  AI-based security solutions . . . . . . . . . . . . . . .  31

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     9.2.  Security of AI  . . . . . . . . . . . . . . . . . . . . .  32
     9.3.   Relevance of AI-based outputs  . . . . . . . . . . . . .  33
   10. IANA Considerations . . . . . . . . . . . . . . . . . . . . .  33
   11. References  . . . . . . . . . . . . . . . . . . . . . . . . .  33
     11.1.  Normative References . . . . . . . . . . . . . . . . . .  33
     11.2.  Informative References . . . . . . . . . . . . . . . . .  34
   Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . .  40
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  41

1.  Introduction

   The functional scope of Network Management (NM) is very large,
   ranging from monitoring to accounting, from network provisioning to
   service diagnostics, from usage accounting to security.  The taxonomy
   defined in [Hoo18] extends the traditional Fault, Configuration,
   Accounting, Performance, Security (FCAPS) domains by considering
   additional functional areas but above all by promoting additional
   views.  For instance, network management approaches can be classified
   according to the technologies, methods or paradigms they will rely
   on.  Methods include common approaches as for example mathematical
   optimization or queuing theory but also techniques which have been
   widely applied in last decades like game theory, data analysis, data
   mining and machine learning.  In management paradigms, autonomic and
   cognitive management are listed.  As highlighted by this taxonomy,
   the definition of automated and more intelligent techniques have been
   promoted to support efficient network management operations.
   Research in NM and more generally in networking has been very active
   in the area of applied ML [Bou18].

   However, for maintaining network operational in pre-defined safety
   bounds, NM still heavily relies on established procedures.  Even
   after several cycles of adding automation, those procedures are still
   mostly fixed and set offline in the sense that the exact control loop
   and all possible scenarios are defined in advance.  They are so
   mostly deterministic by nature or or at least with sufficient safety
   margin.  Obviously, there have been a lot of propositions to make
   network smarter or intelligent with the use of Machine Learning (ML)
   but without large adoption for running real networks because it
   changes the paradigms towards stochastic methods.

   ML includes regression analysis, statistical learning (SVM and
   variants), deep learning (ANN and variants), reinforcement learning,
   etc.  It is a sub-area of Artificial Intelligence (AI) that
   concentrates the focus nowadays but AI encompasses other areas
   including knowledge representation, inductive logic programming,
   inference rule engine or by extension the techniques that allow to
   observe and perform actions on a system.

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   It is thus legitimate to question if ML or AI in general could be
   helpful for NM in regards to practical deployment.  This question is
   actually tight with the problems the NM aims to address.
   Independently of NM, ML-based solutions were introduced to solve one
   type of problems in an approximate way which are very complex in
   nature, i.e. finding an optimal solution is not possible (in
   polynomial time).  This is the case for NP-hard problems.  In those
   cases, solutions typically rely on heuristics that may not yield
   optimal results, or algorithms that run into issues with scalability
   and the ability to produce timely results due to the exponential
   search space.  In NM, those problems exist, for instance allocation
   of resources in case of Service Function Chaining (SFC) or network
   slicing among others are recent examples which have gained interest
   in our community with SDN.  Many propositions consist of modeling the
   optimization problem as an MILP (Mixed-Integer Linear Programming)
   and solve it by means of heuristics to reach a satisfactory tradeoff
   between solution quality (gap to optimality) - computation time and
   model size/dimensionality.  Hence, ML is recognized to be well
   adapted to progress on this type of problem [Kaf19].

   However, all computational problems of NM are not NP-hard.  Due to
   real-time constraints, some involve very short control loops that
   require both rapid decisions and the ability to rapidly adapt to new
   situations and different contexts.  So, even in that case, time is
   critical and approximate solutions are usually more acceptable.
   Again, it is where AI can be beneficial.  Actually, expert systems
   are AI systems [Ste92] but this kind of systems are not designed to
   scale with the volume and heterogeneity of data we can collect in a
   network today for which the expert system is built thanks to numerous
   inference rules.  In contrast, ML is more efficient to automatically
   learn abstract representations of the rules, which can be eventually

   On one hand another type of common problem in NM is classification.
   For instance, classifying network flows is helpful for security
   purposes to detect attack flows, to differentiate QoS among the
   different flows (e.g. real-time streams which need to be
   prioritized), etc.  On the other hand, ML-based classification
   algorithms have been widely used in literature with high quality
   results when properly applied leading to their applications in
   commercial products.  There are many algorithms including decision
   tress, support vector machine or (deep) neural networks which have
   been to be proven efficient in many areas and notably for image and
   natural language processing.

   Finally, many problems also still rely on humans in the loop, from
   support issues such as dealing with trouble tickets to planning
   activities for the roll-out of new services.  This creates

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   operational bottlenecks and is often expensive and error prone.  This
   kind of tasks could be either automated or guided by an AI system to
   avoid inividual human bias.  It is worth noting that Ml relying
   training data generated by human can also suffer indirectly from a
   collective human bias.  Indeed, the balance between human resources
   and the complexity of problems to deal with is actually very
   imbalanced and this will continue to increase due to the size of
   networks, heterogeneity of devices, services, etc.  Hence, human-
   based procedures tend to be simple in comparison to the problems to
   solve or time-consuming.  Notable examples are in security where the
   network operator should defend against potential unknown threat.  As
   a result, services might be largely affected during hours

   Actually, all the problems aforementioned are exacerbated by the
   situation of more complex networks to operate on many dimensions
   (users, devices, services, connections, etc.).  Therefore, AI is
   expected to enable or simplify the solving of those problems in real
   networks in the near future [czb20] [Yan20] because those would
   require reaching unprecedented levels of performance in terms of
   throughput, latency, mobility, security, etc.

2.  Conventions and Definitions

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "OPTIONAL" in this document are to be interpreted as described in
   BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

3.  Acronyms

   *  AI: Artificial Intelligence

   *  FL: Federated Learning

   *  GAN: Generative Adversarial Network

   *  GNN: Graph Neural Network

   *  IBN: Intent-Based Networking

   *  LSTM: Long Short-Term Memory

   *  ML: Machine Learning

   *  MILP: Mixed-Integer Linear Programming

   *  MLP: Multilayer Perceptron

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   *  NM: Network Management

   *  RL: Reinforcement Learning

   *  SFC: Service Function Chaining

   *  SVM: Support-Vector Machine

   *  VNF: Virtual Network Function

4.  Difficult problems in network management

   As mentioned in introduction, problems to be tackled in NM tend to be
   complex and exhibit characteristics that make them candidates for
   solutions that involve AI techniques:

   *  C1: A very large solution space, combinatorially exploding with
      the size of the problem domain.  This makes it impractical to
      explore and test every solution (again NP-hard problems here)

   *  C2: Uncertainty and unpredictability along multiple dimensions,
      including the context in which the solution is applied, behavior
      of users and traffic, lack of visibility into network state, and
      more.  In addition, many networks do not exist in isolation but
      are subjected to myriads of interdependencies, some outside their
      control.  Accordingly, there are many external parameters that
      affect the efficiency of the solution to a problem and that cannot
      be known in advance: user activity, interconnected networks, etc.

   *  C3: The need to provide answers (i.e. compute solutions, deliver
      verdicts, make decisions) in constrained or deterministic time.
      In many cases, context changes dynamically and decisions need to
      be made quickly to be of use.

   *  C4: Data-dependent solutions.  To solve a problem accurately, it
      can be necessary to rely on large volumes of data, having to deal
      with issues that range from data heterogeneity to incomplete data
      to general challenges of dealing with high data velocity.

   *  C5: Need to be integrated with existing automatic and human

   *  C6: Solutions MUST be cost-effective as resources (bandwidth, CPU,
      human, etc.) can be limited, notably when part of processing is
      distributed at the network edge or within the network.

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   Many decision/optimization problems are affected by multiple
   criteria.  Below is a non-exhaustive list of complex NM problems for
   which AI and/or non-AI-based approaches have been proposed:

   *  Computation of optimal paths: Packet forwarding is not always
      based on traditional routing protocols with least cost routing,
      but on computation of paths that are optimized for certain
      criteria - for example, to meet certain level objectives, to
      result in greater resilience, to balance utilization, to optimize
      energy usage, etc.  Many of those solutions can be found in SDN,
      where a controller or path computation element computes paths that
      are subsequently provisioned across the network.  However, such
      solutions generally do not scale to millions of paths (C1), and
      cannot be recomputed in sub-second time scales (C3) to take into
      account dynamically changing network conditions (C2).  To compute
      those paths, operations research techniques have been extensively
      used in literature along with AI methods as shown in [Lop20].  As
      such, this problem can be considered as close to big data problems
      with some of the different Vs: volume, velocity, variety, value…

   *  Classification of network traffic: Without loss of generality a
      common objective of network monitoring for operators is to know
      the type of traffic going through their networks (web, streaming,
      gaming, VoIP).  By nature, this task analyzes data (C4) which can
      vary over time (C2) except in very particular scenarios like
      industrial isolated networks.  However, the output of the
      classification technique is time-constrained only in specific
      cases where fast decisions MUST be made, for example to reroute
      traffic.  Simple identification based on IANA-assigned TCP/UDP
      ports numbers were sufficient in the past.  However, with
      applications using dynamic port numbers, signature techniques can
      be used to match packet payload [Sen04].  To handle applications
      now encapsulated in encrypted web or VPN traffic, machine-learning
      has been leveraged [Bri19].

   *  Network diagnostics: Disruptions of networking services can have
      many causes and thus can rely on analyzing many sources of data
      (C4).  Identifying the root cause can be of high importance when
      what is causing the disruption is not properly understood, so that
      repair actions can address the root cause versus just working
      around the symptoms.  Such repair actions may involve human
      actions (C5).  Further complicating the matter are scenarios in
      which disruptions are not “hard” but involve only a degradation of
      service level, and where disruptions are intermittent, not
      reproducible, and hard to predict.  Artificial intelligence
      techniques can offer promising solutions.

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   *  Network observability: having deeper insights of network status
      can rely on monitoring techniques to gather data from various
      sources.  A major issue is to aggregate all these data in a
      valuable format.  While they are not directly used to automate
      some actions, the aggregation of the data needs to be presented in
      an interpretable manner to human operators.  In this area,
      vizualisation techniques are helpful and also rely on AI-related
      tehcniques to provide the best outputs by reducing the number of
      dimensions (C4) and adapting the vizualisation of data to there
      potential final use in human-handled processes (C5).

   *  Intent-Based Networking (IBN): Roughly speaking, IBN refers to the
      ability to manage networks by articulating desired outcomes
      without the need to specify a course of actions to achieve those
      outcomes [RFC9315].  The ability to determine such courses of
      actions, in particular in scenarios with multiple
      interdependencies, conflicting goals, large scale, and highly
      complex and dynamic environments is a huge and largely unsolved
      challenge (C1, C2, C3).  As an illustration, a major problem with
      intent is ti interpret them correctly knowing that different
      intent formats have been proposed including natural language.
      Without good interpretation of the intent, i.e. the expected
      outcomes, the derived actions will not be adequate.  In case the
      intent is correctly interpreted, a major problem is to find
      concrete solutions to realize the intents which implicilty needs
      to optimize the actions to be taken.  Artificial Intelligence
      techniques can be of help here in multiple ways, from accurately
      classifying dynamic context to determine matching actions to
      reframing the expression of intent as a game that can be played
      (and won) using artificially intelligent techniques.

   *  VNF (Virtual Network Function) placement and SFC design: VNFs need
      to be placed on physical resources and Service Function Chains
      designed in an optimized manner to avoid use of networking
      resources and minimize energy usage (C1,C6).

   *  Smart admission control to avoid congestion and oversubscription
      of network resources: Admission control needs to be set up and
      performed in ways that ensure service levels are optimized in a
      manner that is fair and aligned with application needs, congestion
      avoided or its effects mitigated (C6).

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5.  High-level challenges in adopting AI in NM

   As shown in the previous section, AI techniques are good candidates
   for the difficult NM problems.  There have been many propositions but
   still most of them remain at the level of prototypes or have been
   only evaluated with simulation and/or emulation.  It is thus
   questionable why our community investigates much research in this
   direction but has not adopted those solutions to operate real
   networks.  There are different obstacles.

   First, AI advances have been historically driven by the image/video,
   natural language and signal processing communities as well as
   robotics for many decades.  As a result, the most impressive
   applications are in this area including recently the generalization
   of home assistants or the large progress in autonomous vehicles.
   However, the network experts have been focused on building the
   Internet, especially building protocols to make the world
   interconnected and with always better performance and services.  This
   trend continues today with the 5G networks in deployment and beyond
   5G under definition.  Hence, AI was not the primary focus even if
   increased network automation calls for AI and ML solutions.  However,
   AI is now considered as a core enabler for the future 6G networks
   which are sometimes qualified as AI-native networks.

   While we can see major contributions in AI-based solutions for
   networking over more than two decades, only a fraction of the
   community was concerned by AI at that time.  Progress as a whole,
   from a community perspective, was so limited and compensated by
   relying on the development of AI in the communities as mentioned
   earlier.  Even if our problems share some commonalities, for example
   on the volume of data to analyze, there are many differences: data
   types are completely different, networks are by nature heavily
   distributed, etc.  If problems are different, they SHOULD require
   distinct solutions.  In a nutshell, network-tailored AI was
   overlooked and leads to a first set of challenges described in
   Section 6.

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   Second, many AI techniques require enough representative data to be
   applied independently if the algorithms are supervised or
   unsupervised.  NM has produced a lot of methods and technologies to
   acquire data.  However, in most cases, the goal was not to support AI
   techniques and lead so to a mismatch.  For example, (deep) learning
   techniques mostly rely on having vectors of (real) numbers as input
   which fits some metrics (packet/byte counts, latency, delays, etc)
   but needs some adjustment for categorical (IP addresses, port
   numbers, etc) or topological features.  Conversions are usually
   applied using common techniques like one-hot encoding or by coarse-
   grained representations [Sco11].  However, more advanced techniques
   have been recently proposed to embed representation of network
   entities rather than pure encoding [Rin17][Evr19][Sol20].

   An additional challenge concerns the fact that AI techniques that
   involve analysis of networking data can also lead to the extraction
   of sensitive and personally-identifiable information, raising
   potential privacy concerns and concerns regarding the potential for
   abuse.  For example, AI techniques used to analyze encrypted network
   traffic with the legitimate goal to protect the network from
   intrusions and illegitimate attack traffic could be used to infer
   information about network usage and interactions of network users.
   Intelligent data analysis and the need to maintain privacy are in
   many ways that are contradictory in nature, resulting in an arms
   race.  Similarly, training ML solutions on real network data is in
   many cases preferable over using less-realisitic synthetic data sets.
   However, network data may contain private or sensitive data, the
   sharing of which may be problematic from a privacy standpoint and
   even result in legal exposure.  The challenge concerns thus how to
   allow AI techniques to perform legitimate network management
   functions and provide network owners with operational insights into
   what is going on in their networks, while prohibiting their potential
   for abuse for other (illegitimate) purposes.  Challenges related to
   network data as input to ML algorithms is detailed in Section 7.

   Finally, networks are already operated thanks to (semi-)automated
   procedures involving a large number of resources which are
   synchronized with management or orchestration tools.  Adding AI
   supposes it would be seamlessly integrated within pre-existing
   processes.  Although the goal of these procedures might be solely to
   provide relevant information to operators through alerts or
   dashboards in case of monitoring applications, many other
   applications rely on those procedures to trigger actions on the
   different resources, which can be local or remote.  The use of AI or
   any other approaches to derive NM actions adds further constraint on
   them, especially regarding time constraints and synchronization to
   maintain a coherence over a distributed system.

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   A related challenge concerns the fact that to be deployed, a solution
   needs to not only provide a technical solution but to also be
   acceptable to users - in this case, network administrators and
   operators.  One challenge with automated solutions concerns that
   users want to feel “in control” and able to understand what is going
   on, even more so if ultimately those users are the ones who are held
   accountable for whether or not the network is running smoothly.
   Those same concerns extend to artificially intelligent systems for
   obvious reasons.  To mitigate those concerns, aspects such as the
   ability to explain actions that are taken - or about to be taken - by
   AI systems become important.

   Beyond reasons of making users more comfortable, there are
   potentially also legal or regulatory ramifications to ensure that
   actions taken are properly understood.  For example,agencies such as
   the FCC may impose fines on network operators when services such as
   E911 experience outages, as there is a public interest in ensuring
   highest availability for such services.  In investigating causes for
   such outages, the underlying behavior of systems has to be properly
   understood, and even more so the reasons for actions that fall under
   the realm of network operations.  All these aspects about integration
   and acceptability of the integration of AI in NM processes is
   detailed in Section 8.

6.  AI techniques and network management

6.1.  Problem type and mapping

   In the last few years, an increasing number of different AI
   techniques have been proposed and applied successfully to a growing
   variety of different problems in different domains, including network
   management [Mus18], [Xie18].  Some of the more recently proposed AI
   approaches are clearly advancements of older approaches, which they
   supersede.  Many other AI approaches are not predecessors or
   successors but simply complementary because they are useful for
   different problems or optimize different metrics.  In fact, different
   AI approaches are useful for different kinds of problem inputs (e.g.,
   tabular data vs. text vs. images vs. time series) and also for
   different kinds of desired outputs (e.g., a predicted value, a
   classification, or an action).  Similarly, there may be trade-offs
   between multiple approaches that take the same kind of inputs and
   desired outputs (e.g., in terms of desired objective, computation
   complexity, constraints).

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   Overall, it is a key challenge of using AI for network management to
   properly understand and map which kind of problems with which inputs,
   outputs, and objectives are best solved with which kind of AI (or
   non-AI) approaches.  Given the wealth of existing and newly released
   AI approaches, this is far from a trivial task.

6.1.1.  Sub-challenge: Suitable Approach for Given Input

   Different problems in network management come with widely different
   problem parameters.  For example, security-related problems may have
   large amounts of text or encrypted data as input, whereas forecasting
   problems have historical time series data as input.  They also vary
   in the amount of available data.

   Both the type and amount of data influences which AI techniques could
   be useful.  On one hand, in scenarios with little data, classical
   machine learning techniques (e.g., SVM, tree-based approaches, etc.)
   are often sufficient and even superior to neural networks.  On the
   other hand, neural networks have the advantage of learning complex
   models from large amounts of data without requiring feature
   engineering.  Here, different neural network architectures are useful
   for different kinds of problems.  The traditional and simplest
   architecture are (fully connected) multi-layer perceptrons (MLPs),
   which are useful for structured, tabular data.  For images, videos,
   or other high-dimensional data with correlation between “close”
   features, convolutional neural networks (CNNs) are useful.  Recurrent
   neural networks (RNNs), especially LSTMs, and attention-based neural
   networks (transformers) are great for sequential data like time
   series or text.  Finally, Graph Neural Networks (GNNs) can
   incorporate and consider the graph-structured input, which is very
   useful in network management, e.g., to represent the network

   The aforementioned rough guidelines can help identify a suitable AI
   approach and neural network architecture.  Still, best results are
   often only achieved with sophisticated combinations of different
   approaches.  For example, multiple elements can be combined into one
   architecture, e.g., with both CNNs and LSTMs, and multiple separate
   AI approaches can be used as an ensemble to combine their strengths.
   Here, simplifying the mapping from problem type and input to suitable
   AI approaches and architectures is clearly an open challenge.  Future
   work SHOULD address this challenge by providing both clearer
   guidelines and striving for more general AI approaches that can
   easily be applied to a large variety of different problem inputs.

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6.1.2.  Sub-challenge: Suitable Approach for Desired Output

   Similar to the challenge of identifying suitable AI approaches for a
   given problem input, the desired output for a given problem also
   affects which AI approach SHOULD be chosen.  Here, the format of the
   desired output (single value, class, action, etc.), the frequency of
   these outputs and their meaning SHOULD be considered.

   Again, there are rough guidelines for identifying a group of suitable
   AI approaches.  For example, if a single numerical value is required
   (e.g., the amount of resources to allocate to a service instance),
   then typical supervised regression approaches SHOULD be considered as
   a first candidate option.  If classification (e.g., of malware or
   another security issue [Abd10]) instead of predicting a value is
   desired, supervised classification methods SHOULD be used when
   labeled training data is available.  There are also cases where a
   single class of training data is available, as for example in the
   context of anomaly detection where the model is fitted to normal
   data.  In that case, one-class supervised techniques SHOULD be
   considered as a good candidates.  Alternatively, unsupervised machine
   learning can help to cluster given data into separate groups, which
   can be useful to analyze networking data, e.g., for better
   understanding different types of traffic or user segments.
   Furthermore, the quality of the data directly impacts on the
   robustness of a ML model with the risk of biased models due to over-
   fitting.  As highlighted with these few examples, finding a suitable
   approach to a problem depends on many factors including the type of
   problem to handle but also other contextual elements such as the
   availability and the quality of data.

   In addition to these classical supervised and unsupervised methods,
   reinforcement learning approaches allow active, sequential decisions
   rather than simple predictions or classifications.  This is often
   useful in network management, e.g., to actively control service
   scaling and placement as well as flow scheduling and routing.
   Reinforcement learning agents autonomously select suitable actions in
   a given environment and are especially useful for self-learning
   network management.  In addition to model-free reinforcement
   learning, model-based planning approaches (e.g., Monte Carlo Tree
   Search (MCTS)) also allow choosing suitable actions in a given
   environment but require full knowledge of the environment dynamics.
   In contrast, model-free reinforcement learning is ideal for scenarios
   with unknown environment dynamics, which is often the case in network

   Similar to the previous sub-challenge, these are just rough
   guidelines that can help to select a suitable group of AI approaches.
   Identifying the most suitable approach within the group, e.g., the

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   best out of the many existing reinforcement learning approaches, is
   still challenging.  And, as before, different approaches could be
   combined to enable even more effective network management (e.g.,
   heuristics + RL, LSTMs + RL, …).  Here, further research MAY simplify
   the mapping from desired problem output to choosing or designing a
   suitable AI approach.

6.1.3.  Sub-challenge: Tailoring the AI Approach to the Given Problem

   After addressing the two aforementioned sub-challenges, one may have
   selected a useful kind of AI approach for the given input and output
   of a network management problem.  For example, one may select
   regression and supervised learning to forecast upcoming network
   traffic.  Or select reinforcement learning to continuously control
   network and service coordination (scaling, placement, etc.).
   However, even within each of these fields (regression, reinforcement
   learning, etc.), there are many possible algorithms and
   hyperparameters to consider.  Selecting a suitable algorithm and
   parametrizing it with the right hyperparameters is crucial to tailor
   the AI approach to the given network management problem.

   For example, there are many different regression techniques
   (classical linear, polynomial regression, lasso/ridge regression,
   SVR, regression trees, neural networks, etc.), each with different
   benefits and drawbacks and each with its own set of hyperparameters.
   Choosing a suitable technique depends on the amount and structure of
   the input data as well as on the desired output.  It also depends on
   the available amount of compute resources and compute time until a
   prediction is required.  If resources and time are not a limiting
   factor, many hyperparameters can be tuned automatically.  In
   practice, however, the design space of choosing algorithms and
   hyperparameters is often so large that it cannot be effectively tuned
   automatically but also requires some initial expertise in selecting
   suitable AI algorithms and hyperparameters.

   This sub-challenge holds for all fields of AI: Supervised learning
   (regression and classification), self-supervised learning,
   unsupervised learning, and reinforcement learning, each are broad and
   rapidly growing fields.  Selecting suitable algorithms and
   hyperparameters to tailor AI approaches to the network management
   problem is both an opportunity and a challenge.  Here, future work
   should further explore these trade-offs and provide clearer
   guidelines on how to navigate these trade-offs for different network
   management tasks.

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6.2.  Performance of produced models

   From a general point of view, any AI technique will produce results
   with a certain level of quality.  This leads to two inherent
   questions: (1) what is the definition of the performance in a context
   of a NM application? (2) How to measure it? (3) How to ensure the
   quality of produced results by AI is aligned with NM objectives?  How
   to maintain or improve the quality of produced results?

   Many metrics have been already defined to evaluate the performance of
   an AI-based techniques in regards to its NM-level objectives.  For
   example, QoS metrics (throughput, latency) can serve to measure the
   performance of a routing algorithm along with the computational
   complexity (memory consumption, size of routing tables).  The
   question is to model and measure these two antagonist types of
   metrics.  Number of true/false positives/negatives are the most basic
   metrics for network attack detection functions.  Although the first
   two questions are thus already answered even if improvement can be
   done, question (3) refers to the integration of metrics into AI
   algorithms.  Its objective is to obtain the best results which need
   to be quantified with these metrics.  Depending on the type of
   algorithm, these metrics are either evaluated in an online manner
   with a feedback loop (for example with reinforcement learning) or in
   batch to optimize a model based on a particular context (for example
   described by a dataset for machine learning).

   The problem is two-fold.  First, the performance can be measured
   through multiple metrics of different types (numerical or ordinal for
   example) and some can be constrained by fixed boundaries (like a
   maximum latency), making their joint use challenging when creating an
   AI model to resolve a NM problem.  Second, the scale metrics differ
   from each other in terms of importance or impact and can eventually
   vary on their domains.  It can be hard to precisely assess what is a
   good or bad value (as it might depend on multiple other ones) and it
   is even more difficult to integrate in an AI technique, especially
   for learning algorithms to adjust their models based on the
   performance.  Indeed, learning algorithms run through multiple
   iterations and rely on internal metrics (MAE or (R)MSE for neural
   network, gini index or entropy for decision trees, distance to an
   hyperplane for SVMs, etc) which are not strongly correlated to the
   final metrics of the NM application.  AI-internal metrics such as the
   loss do not match well the metrics related to the final NM objectives
   as the significance and impact of the AI errors cannot be easily
   translated into the NM domain.

   For instance, a decision tree algorithm for classification purposes
   aims at being able to create branches with a maximum of data from the
   same classes and so avoid mixing classes.  It is done thanks to a

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   criterion like the entropy index but this kind of index does not
   assume any difference between mixing class A and B or A and C.
   Assuming now that from an operational point of view, if A and B are
   mixed in the predictions is not critical, the algorithm should have
   preferred to mix and A and B rather than A and C even if in the first
   case it will produce more errors.  Therefore, the internal
   functioning of the AI algorithms should be refined, here by defining
   a particular criterion to replace the entropy as a quality measure
   when separating two branches.  It assumes that the final NM
   objectives are integrated at this stage.

   Another concrete example is traffic predictors which aim at
   forecasting traffic demands.  They only produce an output that is not
   necessarily simple to be interpreted and used by, e.g., capacity
   allocation strategies/policies.  A traditional traffic prediction
   that tries to minimize (perfectly symmetric) MAE/MSE treats positive
   and negative errors in identical ways, hence is agnostic of the
   diverse meaning (and costs) of under- and over-provisioning.  And,
   such a prediction does not provide any information on, e.g., how to
   dimension resources/capacity to accommodate the future demand
   avoiding all underprovisioning (which entails service disruption)
   while minimizing overprovisioning (i.e., wasting resources).  In
   other words, it forces the operator to guess the overprovisioning by
   taking (non-informed) safety margins.  A more sensible approach here
   is instead forecasting directly the needed capacity, rather than the
   traffic [Beg19].

   While the one above is just an example, the high-level challenge is
   devising forecasting models that minimize the correct objective/loss
   function for the specific NM task at hand (instead of generic MAE/
   MSE).  In this way, the prediction phase becomes an integral part of
   the NM, and not just a (limited and hard-to-use) input to it.  In ML
   terms, this maps to solving the loss-metric mismatch in the context
   of anticipatory NM [Hua19].

   Another issue for statistical learning (from examples/observations)
   is mainly about extracting an estimator from a finite set of input-
   output samples drawn from an unknown probability distribution that
   should be descriptive enough for unseen/new input data.  In this
   context online monitoring and error control of the quality/properties
   of these point estimators (bias, variance, mean squared error, etc.)
   is critical for dynamic/uncertain network environments.  Similar
   reasoning/challenge applies for interval estimates, i.e., confidence
   intervals (frequentist) and credible intervals (Bayesian).

   Finally, question (4) refers to the ability of an AI solution to
   remain efficient and to eventually improve over time.  This requires
   dynamic methods capable to adapt to a changing environment.  As

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   already highlighted, the models can be dynamically adjusted based on
   the errors they produced.  In the context of ML, the models can be
   also updated based on new data, either through a complete re-learning
   phase, fine-tuning or transfer-learning approaches.  This assumes to
   collect and ingest continuously new data.  This leads to set of
   related challenges related to select or discard some data over a time
   horizon and to label data real-time.  There are ML techniques which
   helps in that direction such as LSTMs which integrates forget gates
   in their architectures.

6.3.  Lightweight AI

   Network management and operations often need to be performed under
   strict time constraints, i.e. at line rate, in particular in the
   context of autonomic or self-driven networks.  Locating NM functions
   as close as possible where forwarding is achieved is thus an
   interesting option to avoid additional delays when these operations
   are performed remotely, for example in a centralized controller.
   Besides, forwarding devices may offer available resources to
   supplement or replace edge resources.  In case of AI coupled with
   network management, AI tasks can be offloaded in network devices, or
   more generally embedded within the network.  Obviously, time-critical
   tasks are the best candidates to be offloaded within the network.
   Costly learning tasks should be processed in high-end servers but
   created models can be deployed, configured, modified and tuned in

   Recent advances in network programmability ease the programming of
   specific tasks at data-plane level.  P4 [Bos14] is widely used today
   for many tasks including firewalling [Dat18] or bandwidth management
   [Che19].  P4 is prone to be agnostic to a specific hardware.
   Switches actually have particular architectures and the RMT
   (Reconfigurable Match Table) [Bos13] model is generally accepted to
   be generic enough to represent limited but essential switch
   architecture components and functionalities.  P4 is inspired by this
   architecture.  The RMT model allows reconfiguring match-action tables
   where actions can be usual ones (rewrite some headers, forward,
   drop...).  Actions are thus applied on the packets when they are
   forwarded.  Actions can also be more complex programs with some
   safeguards: no loop, resistivity… The impact on the program
   development is huge.  For example, real number operations are not
   available by default while they are primordial in many AI algorithms.

   In a nutshell, the first challenge to overcome of embedding AI in a
   network is the capacity of the hardware to support AI operations
   (architectural limitation).  Considering software equipment such as a
   virtual switch simplifies the problem but does not totally resolve it
   as, even in that case, strong line-rate requirement limits the type

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   of programs to be executed.  For example, BPF (Berkeley Packet
   Filter) [Mcc93] programs provides a higher control on packet
   processing in OVS [Cha18] but still have some limitations, as the
   execution time of these programs are bounded by nature to ensure
   their termination, an essential requirement assuming the run-to-
   completion model which permits high throughput.

   The second challenge (resource limitation) of network-embedded AI in
   the network is to allocate enough resources for AI tasks with a
   limited impact on other tasks of network devices such as forwarding,
   monitoring, filtering… Approximation and/or optimization of AI tasks
   are potential directions to help in this area.  For instance, many
   network monitoring proposals rely on sketches and with a proposed
   well-tuned implementation for data-plane [Liu16][Yan18].  However, no
   general optimized AI-programmable abstraction exists to fit all cases
   and proposals are mostly use-case centric.  Research direction in NM
   regarding this issue can benefit from propositions in the field of
   embedded systems that face the same issues.  Binarization of neural
   networks is one example [Lia18].  Besides, distributed processing is
   a common technique to distribute the load of a single task between
   multiple entities.  AI task decomposition between network elements,
   edge servers or controllers has been also proposed [Gup18].

6.4.  Distributed AI

   Distributed AI assumes different related tasks and components to be
   distributed across computational resources which are possibly
   heterogeneous.  For example, with advances in transfer and Federated
   Learning (FL), models can be learned, partially shared and combined
   or data can be also shared to either improve a local or global model.
   By nature, a network and a networked infrastructure is distributed
   and is thus well adapted to any distributed applications.  This is
   exacerbated with the deployment of fog infrastructure mixing network
   and computational resources.  Hence, network management can directly
   benefit to the distributed network structure to solve its own
   particular problems but any other type of AI-based distributed
   applications also assumes communication technologies to enable
   interactions between the different entities.  This leads to the two
   sub-challenges described hereafter.

6.4.1.  Network management for efficient distributed AI

   Distributed AI relies on exchanging information between different
   entities and comes with various requirements in terms of volume,
   frequency, security, etc.  This can be mapped to network requirements
   such as latency, bandwidth or confidentiality.  Therefore, the
   network needs to provide adequate resources to support the proper
   execution of the AI distributed application.  While this is true for

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   any distributed application, the nature of the problem that is
   intended to be solved by an AI application and how this would be
   solved can be considered.  For example, with FL, local models can be
   shared to create a global model.  In case of failure of network links
   or in case of too high latency, some local models might not be
   appropriately integrated into the global model with a possible impact
   on AI performance.  Depending on the nature of the latter, it might
   be better to guarantee high performance communications with a few
   number of nodes or to ensure connectivity between all of them even
   with lower network performance.  Coupling is thus necessary between
   the network management plane and the distributed AI applications
   which leads to a set of questions to be addressed about interfaces,
   data and information models or protocols.  While the network can be
   adapted or eventually adapt itself to the AI distributed
   applications, AI applications could also adapt themselves to the
   underlying network conditions.  It paves the way to research on
   methods to support AI application aware-network management or
   network-aware AI applications or a mix of both.

6.4.2.  Distributed AI for network management

   For network management applications relying on distributed AI,
   challenges from Section 6.4.1 are still valid.  Furthermore, network
   management problems also consider network-specific elements like
   traffic to be analyzed or configuration to be set on distributed
   network equipments.  Co-locating AI processing and these elements
   (fully or partially) may help to increase performance.  For example,
   precalculation on traffic data can be offloaded on network routers
   before being further processed in high-end servers in a data-center.
   Besides, as data is forwarded through multiple routers, decomposition
   of AI processes along the forward path is possible [Jos22].  In
   general, distributed AI-based network management decisions could be
   made at different nodes in the network based on locally available
   information [Sch21].  Hence, deployment of AI-based solutions for
   network management can also take into account various network
   attributes like network topology, routing policies or network device
   capability.  In that case, management of computational and network
   resources is even more coupled than in Section 6.4.1 since the
   network is both part of the AI pipeline resources and the managed
   object through AI.

   A primary application for distributed AI is for management problems
   that have a local scope.  One example concerns problems that can be
   addressed at the edge, involving tasks and control loops that monitor
   and apply local optimizations to the edge in isolation from
   activities conducted by other instances across the network.  However,
   distributed AI can involve techniques in which multiple entities
   collaborate to solve a global problem.  Such solutions lend

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   themselves to problems in which centralized solutions are faced with
   certain foundational challenges such as security, privacy, and trust:
   The need to maintain complete state in a centralized solution may not
   be practical in some cases due to concerns such as privacy and trust
   among multiple subdomains which may not want to share all of their
   data even if they would be willing to collaborate on a problem).
   Other foundational challenges concern issue related to timeliness, in
   which distributed solutions may have inherent advanges over
   centralized solutions as they avoid issues related to delays caused
   by the need to communicate updates globally and across long

6.5.  AI for planning of actions

   Many tasks in network management revolve around the planning of
   actions with the purpose of optimizing a network and facilitating the
   delivery of communication services.  For example, Paths need to be
   planned and set up in ways that minimize wasted network resources (to
   optimize cost) while facilitating high network utilization (avoiding
   bottlenecks and the formation of congestion hotspots) and ensuring
   resiliency (by making sure that backup paths are not congruent with
   primary paths).  Other examples were mentioned in Section 4.

   The need for planning only increases with the rise of centralized
   control planes.  The promise of central control is that decisions can
   be optimized when made with complete knowledge of relevant context,
   as opposed to distributed control that needs to rely on local
   decisions being made with incomplete knowledge while incurring higher
   overhead to replicate relevant state across multiple systems.
   However, as the scale of networks and interconnected systems
   continues to grow, so does the size of the planning task.  Many
   problems are NP-hard.  As a result, solutions typically need to rely
   on heuristics and algorithms that often result in suboptimal outcomes
   and that are challenging to deploy in a scalable manner.

   The emergence of Intent-Based Networking emphasizes the need for
   automated planning even further.  The concept underlying “intent” is
   that it should allow users (network operators, not end users of
   communication services) to articulate desired outcomes without the
   need to specify how to achieve those outcomes.  An Intent-Based
   System is responsible for translating the intent into courses of
   action that achieve the desired outcomes and that continue to
   maintain the outcomes over time.  How the necessary courses of action
   are derived and what planning needs to take place is left open but
   where the real challenge lies.  Solutions that rely on clever
   algorithms devised by human developers face the same challenges as
   any other network management tasks.

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   These properties (problems with a clearly defined need, whose
   solution is faced with exploding search spaces and that today rely on
   algorithms and heuristics that in many cases result only suboptimal
   outcomes and significant limitations in scale) make automated
   planning of actions an ideal candidate for the application of AI-
   based solutions.

   AI applications in network management in the past have been largely
   focusing on classification problems.  Examples include analysis by
   Intrusion Protection Systems of traffic flow patterns to detect
   suspicious traffic, classification of encrypted traffic for improved
   QoS treatment based on suspected application type, and prediction of
   performance parameters based on observations.  In addition, AI has
   been used for troubleshooting and diagnostics, as well as for
   automated help and customer support systems.  However, AI-based
   solutions for the automated planning of actions, including the
   automated identification of courses of action, have to this point not
   been explored much.

   A much-publicized leap in AI has been the development of AlphaGo
   [Sil16].  Instead of using AI to merely solve classification
   problems, AlphaGo has been successful in automatically deriving
   winning strategy for board games, specifically the game of Go which
   features a prohibitively large search space that was long thought to
   put the ability to play Go at a world class level beyond the reach of
   problems that AI could solve.  Among the remarkable aspects of Alpha
   Go is that it is able to identify winning strategies completely on
   its own, without needing those strategies to be taught or learned by
   observations assuming the system is aware of rules.

   The challenge for AI in network management is hence, where is the
   equivalent of an Alpha Go that can be applied to network management
   (and networking) problems?  Specifically, better solutions are needed
   for solutions that automatically derive plans and courses of actions
   for network optimization and similar NP-hard problems, such as
   provided today with only limited effectiveness by controllers and
   management applications.

   Also, the evaluation of AI algorithms to derive courses of actions is
   more complex than more common regression or classification tasks.
   Actions need to be applied in order to observe the results it leads
   to.  However, contrary to game playing, solutions need to be applied
   in the real world, where actions have real effects and consequences.
   Different orientations can be envisioned.  First, incremental
   application of AI decisions with small steps can allow us to
   carefully observe and detect unexpected effects.  This can be
   complemented with roll-back techniques.  Second, formal verification
   techniques can be leveraged to verify decisions made by AI are

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   maintained within safety bounds.  Third, sandbox environments can be
   used but they SHOULD be representative enough of the real world.
   After progress in simulation and emulation, recent research advances
   lead to the definition of digital twins
   [I-D.irtf-nmrg-network-digital-twin-arch] which implies a tight
   coupling between a real system and its digital twin to ensure a
   parallel but synchronized execution.  Alternatively, transfer
   learning techniques in another promising area to be able to
   capitalize on ML models applicable on a real word system in a more
   generic sandbox environment.  It is actually also an open problem to
   make the use of AI more acceptable as highlighted in the dedicated

7.  Network data as input for ML algorithms

   Many applications of AI takes as input data.  The quality of the
   outputs of ML-based techniques are highly dependent on the quality
   and quantity of data used for learning but also on other parameters.
   For example, as modern network infrastructures move towards higher
   speed and scale, they aim to support increasingly more demanding
   services with strict performance guarantees.  These often require
   resource reconfigurations at run time, in response to emerging
   network events, so that they can ensure reliable delivery at the
   expected performance level.  Timely observation and detection of
   events is also of paramount importance for security purposes, and can
   allow faster execution of remedy actions thus leading to reduced
   service downtime.

   Thus, the challenge of data management is multifaceted as detailed in
   next subsections.

7.1.  Data for AI-based NM solutions

   Assuming a network management application, the first problem to
   address is to define the data to be collected which will be
   appropriate to obtain accurate results.  This data selection can
   require defining problem-specific data or features (feature

   Firstly, NM has already produced a lot of methods and technologies to
   acquire data.  However, in most cases, the goal was not to support AI
   problems and lead to a mismatch.  Indeed, machine learning algorithms
   only work as desired when data to be analyzed respects properties.
   Many methods rely on vector-based distances which so supposes that
   the data encoded into the vector respects the underlying distance
   semantic.  Taking the first n bytes of a packet as vectors and
   computing distances accordingly is possible but does not embed the
   semantic of the information carried out in the headers.  For example,

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   (deep) learning techniques mostly rely on vectors of (real) numbers
   as input which fits some metrics (packet/byte counts, latency,
   delays, etc) but needs some adjustment for categorical (IP addresses,
   port numbers, etc) or topological features.  Conversions are usually
   applied using common techniques like one-hot encoding or by coarse-
   grained representations [Sco11].  However, more advanced techniques
   have been recently proposed to embed representation of network
   entities rather than pure encoding [Rin17][Evr19][Sol20].  Data to
   handle can be in a schema-free or eventually text-based format.  One
   example could be the automated annotation of management intents
   provided in an unstructured textual format (policies descriptions,
   specifications,) to extract from them management entities and
   operations.  For that purpose, suitable annotation models need to be
   built using existing NER (Named Entity Recognition) techniques
   usually applied for NLP.  However, this SHALL be carefully crafted or
   specialized for network management (intent) language which indirectly
   bounces back to the challenges of AI techniques for NM specified

   Secondly, similar to the problem of mapping AI algorithms with NM
   problems in section Section 6.1, data to be collected also depends on
   the NM problem to be solved.  The mapping between the data sources
   and the problem is not straightforward as all dependencies or
   correlations are not known and some might be expected to be
   discovered by the AI algorithms themselves.  In addition, the types
   of data to collect can vary over time to maintain the performance of
   an AI-based application or to adapt it to a new context when learned
   models are updated dynamically.  The problems of collecting relevant
   data and updating models SHOULD thus be handled conjointly.

   Secondly, the behavior of any network is not just derived from the
   events that can be directly observed, such as network traffic
   overload, but also from events occurring outside the environment of
   the network.  The information provided by the detectors of such kinds
   of events, e.g. a natural incident (earthquake, storm), can be used
   to determine the adaptation of the network to avoid potential
   problems derived from such events.  Those can be provided by big data
   sources as well as sensors of many kinds.  The AI challenge related
   to this task is to process large amounts of data and associate it
   with the effects that those events have on the network.  It is hard
   to determine the static and dynamic relation between the data
   provided by external sources and the specific implications it has in
   networks.  For instance, the effect of a “flash crowd” detected in an
   external source depends on the relation of a particular network to
   such an event.  This can be addressed by AI and its particular
   application to network management.  The objective is to complement a
   control-loop, as shown in [Mar18], by including the specific AI
   engines into the decision components as well as the processes that

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   close the loop, so the AI engine can receive feedback from the
   network in order to improve its own behavior.  Similar challenges are
   addressed in other domains, image processing and computer vision, by
   using artifacts for anticipating movements in object location and

7.2.  Data collection

   Once defined, the second problem to address is the collection of
   data.  Monitoring frameworks have been developed for many years such
   as IPFIX [RFC7011] and more recently with SDN-based monitoring
   solutions [Yu14][Ngu20].  However, going towards more AI for actions
   in network management supposes also to retrieve more than traffic
   related information.  Actually, configuration information such as
   topologies, routing tables or security policies have been proven to
   be relevant in specific scenarios.  As a result, many different
   technologies can be used to retrieve meaningful data.  To support
   improved QoE, monitoring of the application layer is helpful but far
   from being easy with the heterogeneity of end-user applications and
   the wide use of encrypted channels.  Monitoring techniques need to be
   reinvented through the definition of new techniques to extract
   knowledge from raw measurement [Bri19] or by involving end-users with
   crowd-sourcing [Hir15] and distributed monitoring.  Also, the data-
   mesh concept proposes to classify data into three categories: source-
   aligned, aggregate and consumer-aligned.  Source-aligned data are
   those related to the same operational domain and it is important to
   correlate or aggregate them with higher planes: management-, control-
   and forwarding plane.  An issue is the difference, not only in the
   nature of data, but in their volumes and their variety.  Some may
   change rapidly over time (for example network traffic) while other
   may be quite stable (device state).

   The collecting process requirements depend on the kind of processing.
   We can distinguish two major classes: batch/offline vs real-time/
   online processing.  In particular, real-time monitoring tools are key
   in enabling dynamic resource management functions to operate on short
   reconfiguration cycles.  However, maintaining an accurate view of the
   network state requires a vast amount of information to be collected
   and processed.  While efficient mechanisms that extract raw
   measurement data at line rate have been recently developed, the
   processing of collected data is still a costly operation.  This
   involves potentially sampling, evaluating and aggregating a vast
   amount of state information as a response to a diverse set of
   monitoring queries, before generating accurate reports.  One
   difficult problem resides also in the availability of data as real-
   time data from different sources to be aggregated may not arrive at
   the same time requiring so some buffering techniques.  Machine
   learning methods, e.g. based on regression, can be used to

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   intelligently filter the raw measurements and thus reduce the volume
   of data to process.  For example, in [Tan20] the authors proposed an
   approach in which the classifiers derived for this purpose (according
   to measurements on traffic properties) can achieve a threefold
   improvement in the query processing capability.  A residual question
   is the storage of raw measurements.  In fact, predicting the lifetime
   of data is challenging because their analysis may not be planned and
   triggered by a particular event (for example, an anomaly or attack).
   As a result, the provisioning of storage capacity can be hard.

   In parallel to the continuously increasing dynamicity of networks and
   complexity of traffic, there is a trend towards more user traffic
   processing customization [RFC8986][Li19].  As a result, fine grained
   information about network element states is expected and new
   propositions have emerged to collect on-path data or in-band network
   telemetry information [Tan20b].  These new approaches have been
   designed by introducing much flexibility and customization and could
   be helpful to be used in conjunction with AI applications.  However,
   the seamless coupling of telemetry processes with packet forwarding
   requires careful definition of solutions to limit the overhead and
   the impact of the throughput while providing the necessary level of
   details.  This shares commonalities with the lightweight AI

7.3.  Usable data

   Although all agree on the necessity to have more shared datasets, it
   is quite uncommon in practice.  Data contains private or sensitive
   information and may not be shared because of the criticality of data
   (which can be used by ill-intentioned adversaries) or due to laws or
   regulations, even within the same company.  To solve this issue,
   anonymization techniques [Dij19] can be enhanced to optimize the
   trade-off between valuable data vs sensitive information (potential)
   leakage or reconstruction.  Whatever the final user of data,
   regulations and laws impose rules on data management with potentially
   costly impact if they are not respected voluntarily or not.  Defining
   a new monitoring framework should always consider security and
   privacy aspects, for example to let any user/customer or access/
   remove its own data with General Data Protection Regulation (GDPR) in
   EU.  The challenge resides here in the capacity of qualifying what is
   critical or private information and the capacity for an adversary to
   reconstruct it from other sources of data.  Hence AI/ML based
   solutions will require more data but also more administrative, legal
   and ethical procedures.  Those can last long and so slow down the
   deployment of a new solution.  In addition, this requires interaction
   with experts from different domains (e.g.  AI engineer and a lawyer).
   The integration of these non-technical constraints should be
   considered when defining new data to be collected or a new technique

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   to collect data.  However, knowing the final use of data is most of
   the time necessary for ethical and legal assessment which assumes
   that those considerations SHOULD be integrated from the early design
   of new AI-based solutions.

   For supervised or semi-supervised training, having a labeled dataset
   is a prerequisite.  It constitutes a major challenge as well.  One
   one hand, collectors are able to retrieve data.  On the other hand,
   those network data are typically unlabeled.  This limits application
   of ML to unsupervised learning tasks (learning from data).  Because
   manual labeling is a tedious task. one option is to leverage AI to
   guide humans.  This may also support a better generalization of a
   learned model.  Indeed, an underlying challenge is the genericity or
   coverage of the datasets.  Labels encode values of an objective
   function, the challenge posed by the design of such tools is
   tremendous since for involving a M:N relationship: 1 data type may be
   associated to M objective function values and N data types may be
   associated to 1 objective function.  As a result, most datasets used
   for research encodes a single label for a particular application like
   attack label for datasets to be used in the context of intrusion
   detection or application type for network traffic used for
   classification where the value of a single dataset could be
   capitalized in several applications.

   Again, researchers need empirical (or at least realistic) datasets to
   validate their solutions.  Unfortunately, as highlighted above,
   having such data from real deployments for various reasons (business
   secrets, privacy concerns, concerns that vulnerabilities are revealed
   by accident, raw unlabeled data, etc.) is tough.  Even if such a
   dataset is available it might not be enough to convincingly validate
   a new algorithm.  Instead of falling back to artificial testbed
   experiments or simulation, it would be useful to have the capability
   to generate datasets with characteristics that are not 100% identical
   but similar to the characteristics of one or more real datasets.
   Such synthetic networks can be used to validate new management
   algorithms, intrusion detection systems, etc.  The usage of AI (for
   example GANs) in this area [Hui22] is not yet widespread and there
   are still many concerns that deter researchers, e.g. the fear of
   leaking sensitive information from the original dataset into the
   synthetic dataset.

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8.  Acceptability of AI

   Networks are critical infrastructures.  On one hand, they SHOULD be
   operated without interruption and must be interoperable.  Networks,
   except in a lab, are not isolated which slow down innovation in
   general.  For example, changing Internet routing protocols SHOULD be
   accepted by multiples entities such as operators of interconnected
   networks.  The same applies for protocols.  Even if there have been
   several versions of major protocols in use like TCP or DNS, there are
   still some security issues which cannot be patched with 100%
   guarantee.  On the other hand, results provided by AI solutions are
   uncertain by nature.  The same technique applied in different
   environments can produce different results.  AI techniques need some
   effort (time and human) to be properly configured or to be
   stabilized.  For instance, reinforcement learning needs several
   iterations before being able to produce acceptable results.  These
   properties of AI techniques are thus a bit antagonist with the
   criticality of network infrastructures.  With that in mind,
   acceptability of AI by network operators is clearly an obstacle for
   its larger adoption.

8.1.  Explainability of Network-AI products

   A common issue across many Machine Learning (ML) applications is
   their lack to provide human-understandable reasoning processes.  This
   means that, after training, the knowledge acquired by ML models is
   unintelligible to humans.  As a result, offering hard guarantees on
   performance is a very challenging issue.  In addition, complex ML
   models like neural networks -that often have more than hundreds of
   thousands of parameters- are very hard to debug or troubleshoot in
   case of failure.

   While this is a common issue for all applications of AI, many areas
   work well with uncertainty and the black-box behavior of AI-based
   solutions.  For instance, users accept an inherent error in
   recommender systems or computer vision solutions.

   The networking field has already produced a set of well-established
   network management algorithms and methods, with clear performance
   guarantees and troubleshooting mechanisms [Rex06][Kr14].  As such,
   improving debugging, troubleshooting and guarantees on AI-based
   solutions for networking is a must.

   AI researchers and practitioners are devoting large research efforts
   to improve this aspect of ML models, which is commonly known as
   explainability [XAI].

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   This set of techniques provides insights and, in some cases,
   guarantees on the performance and behavior of ML-based solutions.
   Understanding such techniques, researching and applying them to
   network AI is critical for the success of the field.

   There exist several ML-based methods that are human-understandable,
   although not widely used today.  For instance, [Mar20] shows a method
   for building anticipation models (prediction) that provide
   explanations while determining some actions for tuning some
   parameters of the network.  There are other challenges that SHOULD be
   addressed, such as providing explanations for other ML methods that
   are quite extended.  For instance, xNN/SVM models can be accompanied
   by Digital Twins of the network that are reversely explored to
   explain some output from the ML model (e.g., xNN/SVM).  In this
   context, there already exist several methods [Zil20][Puj21] that
   produce human-readable interpretations of trained NN models, by
   analyzing their neural activations on different inputs.  (As an
   aside, it should be noted that Digital Twins are not considered per
   se an AI approach; they merely serve to provide a digital
   representation of a network that can serve as its proxy and offer a
   layer of indirection between management applications and actual
   network resources.  That being said, it is conceivable that AI-based
   management applications can be combined and operate in conjunction
   with Digital Twin technology, for example to use a Digital Twins as
   an experimentation sandbox or staging ground for AI-driven

8.2.  AI-based products and algorithms in production systems

   AI-based network management and optimization algorithms are first
   trained, then the resulting model is used to produce relevant
   inferences in operation, either in management or optimization
   scenarios.  A relevant question for the success of AI-based solutions
   is: where does this training occur?

   Traditionally, AI-based models have been trained in the same scenario
   where they operate[Val17][Xu18], this is the customer network.
   However this presents critical drawbacks.  First, training an AI
   model for management and operation typically requires generating
   network configurations and scenarios that can break the network.
   This is because training requires seeing a broad spectrum of
   scenarios.  Thus, training in production networks is very
   challenging.  Second, customer networks may not be equipped with the
   monitoring infrastructure required to collect the data used in the
   training process (e.g., performance metrics).  Actually, performing
   learning directly into a production network is possible assuming
   imperfect models and the need of several step of refinement before it
   gets stable.  For non-critical management task, such assumption can

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   hold and additional safe-guarding mechanisms SHOULD be considered in
   order to keep outputs of ML algorithms (such as decisions) within
   acceptable boundaries.

   A more sensible approach is to train the AI-based product in a lab,
   for instance in the vendor’s premises.  In the lab, AI models can be
   trained in a controlled testbed, with any configuration, even ones
   that break the network.  However, the main challenge here arises from
   the fundamental differences between the lab’s network and the
   customer networks.  For instance, the topology of the lab’s network
   might be smaller, etc.  As a result, there is a need for models that
   are able to generalize.  In this context, generalization means that
   models should be able to operate in other scenarios not seen during
   training, with different topologies, routing configurations,
   scheduling policies, etc.

   In order to address this generalization problem, multiple
   complementary approaches are possible: One approach is training on
   diverse data that represents large parts of the expected problem
   space.  For example, training with various different traffic patterns
   will help improve generalization to unseen but comparable traffic
   patterns.  Another approach is to leverage AI designs or
   architectures that facilitate generalization.  One example are Graph
   Neural Networks (GNN) [gnn1][gnn2].  GNNs are a rather novel type of
   neural network able to operate and generalize over graphs.  Indeed,
   networks are fundamentally represented as graphs: topology, routing,
   etc.  With GNNs, vendors can train the AI model in a lab with a
   certain topology and then directly use the resulting model in
   different customer networks, even with different network topologies.
   Finally, another approach is Transfer Learning [tl1].  With this
   technique, the knowledge gained in the lab’s training is used to
   operate in the customer network.  Transfer Learning still requires
   that some data from the customer is used to re-train and fine-tune
   the model (e.g., accurate performance measurements).  This means
   that, for each customer network, re-training is required.  This may
   be problematic since it requires added cost and access to customer

   In addition to the challenge of generalizing from training to
   production environment, there are also challenges in terms of
   interoperability between different AI approaches and different
   deployment environments.  As mentioned above, AI approaches may be
   deployed in diverse environments, e.g., for training and production,
   but also for local development, for testing, and for validation or in
   different part of the production systems.  These environments may
   differ in available compute resources, network topology, operating
   systems, cloud providers, etc. (single node machine, single cluster,
   many distributed clusters, ...).  Deploying the same AI solutions in

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   these different environments can lead to various challenges in terms
   of interoperability.  Common AI frameworks support scaling across
   networks of different size.  Yet, many frameworks are often combined,
   e.g., for data collection, processing, predictions, validation, etc.
   Again, ensuring interoperability between these frameworks can be

   This shares some with problems described in Section 6.4 and
   particularly emphasizes the need for network environments to provide
   interfaces and descriptions suitable for AI solutions to be properly
   instantiated and configured.

   One approach to address these interoperability challenges is through
   meta-frameworks that interface with most available AI frameworks.
   These meta-frameworks provide a higher level of abstraction and often
   allow seamless deployment across different environments (e.g., on-
   premise or at different cloud providers) [Mor18].

8.3.  AI with humans in the loop

   Depending on the network management task, AI can automate and replace
   manual human control or it can complement human experts and keep them
   in the loop.  Keeping humans in the loop will be an important step of
   building trust in AI approaches and help ensure the desired outcomes.
   There are various ways of keeping humans in the loop in the different
   fields of AI, which could be useful for different aspects of network

   In classification tasks (e.g., detecting security breaches, malware
   or detecting anomalies), trained AI models provide a confidence score
   in addition to the predicted class.  If the confidence is high, the
   prediction is used directly.  If the confidence is too low, a human
   expert may jump in and make the decision - thereby also providing
   valuable training data to improve the AI model.  Such approaches are
   already being used in industry, e.g., to automatically label datasets
   (AWS SageMake).  Similar approaches could also be used for other
   supervised learning tasks, e.g., regression.  Still, it is an open
   challenge to keep humans in the loop in all phases of the learning

   Another field of AI is reinforcement learning, which is useful for
   taking continuous control decisions in network management, e.g.,
   controlling service scaling and placement as well as flow scheduling
   and routing over time.  Reinforcement learning agents typically
   interact with the environment (i.e., the simulated or real network)
   completely autonomously without human feedback.  However, there is a
   growing number of approaches to put human experts back into the loop.
   One approach is offline reinforcement learning, where the training

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   data does not come from the reinforcement learning agent’s own
   exploration but from pre-recorded traces of human experts (e.g.,
   placement decisions that were made by humans before).  Another
   approach is to reward the reinforcement learning agent based on human
   feedback rather than a pre-defined reward function [Lee21].  Again,
   while there are first promising approaches, more work is required in
   this area.  Overall, it is an open challenge to both leverage the
   benefits of AI but keep human experts in the loop where it is useful.

9.  Security considerations

   This document introduces the challenges of coupling AI and NM.  Since
   the aim of this document is not to address a particular NM problem by
   defining a solution and because many possible ones can be developed
   further to address the proposed challenge, it is not possible at this
   stage to define security concerns specific to a solution.  However,
   examples of applications mentioned and cited in the different
   sections may face their own security concerns.  In this section, our
   objective is to highlight high-level security considerations to be
   considered when coupling AI and NM.  Those concerns serves as the
   common basis to be refined according when a particular NM application
   is developped.

9.1.  AI-based security solutions

   The first security consideration refers to the use of AI for NM
   problem related to security of the managed networked systems.  There
   are multiple scenarios where AI can be leveraged: to perform traffic
   filtering, to detect anomalies or to decide on target moving defence
   strategies.  In these cases, the performance of the AI algorithms
   impacts on the security performance (e.g. detection or mitigation
   effectiveness) like any other non-AI system.  However, AI methods
   generally tends to obfuscate how predictions are made and decisions
   taken.  Explainability of AI is thus highly important.  This is why
   this concern is addressed globally in this document in section
   Section 8.1 as it is a general problem that can impact on any other
   type of NM applications.

   Assuming a ML trained model, there is always an uncertainty regarding
   reachable performance on the wild once the solution is deployed as it
   can suffer from a poor generalization due to different reasons.
   There are two major problems which are well known in the ML field:
   overfitting or under-fitting.  In the first case, the learned model
   is too specific to the training data while in the second case the
   model does not infer any valuable knowledge from data.  To avoid
   these issues, hyper-parameter fine-tuning is necessary.  For example,
   the number of iterations is an essential hyper-parameter to be
   adjusted to learn a neural network model.  If it is too low, the

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   learning does not converge to a representative model of the training
   data (underfitting).  With a high value, there is a risk that the
   model is too close to the training data (overfitting).  In general,
   finding the right hyper-parameters is helpful to find a good ML
   algorithm configurations.  There are different techniques ranging
   from grid-search to bayesian optimization falling into the AI area of
   Hyper-Parameter Optimization (HPO) [Bis23].  This consideration goes
   beyond the sole problem of hyper-parameters settings but as a full
   analysis pipeline also assumes the ML algorithm to be selected or the
   data pre-processing to be configured.  This reflects challenges from
   the AI research area covered by AutoMl technique.  In this document,
   this is also referred in section Section 6.1.3 when considered in the
   context of NM.  As highlighted in the aforementioned section, some
   expertise or area-specific knowledge can help guiding automated
   configuration processes.

   Besides, machine learning assumes to have representative training
   data.  The quality of dataset for learning is a vast problem.
   Additionally, the representation of data needs to be addressed
   carefully to be properly analyzed by AI models, for example with pre-
   processing techniques to normalize data, balance classes or encode
   categorical features depending on the type of algorithms.  Actually,
   Section 7 of this document fully addresses the concerns related to
   data in regards to NM problems.

9.2.  Security of AI

   Although ensuring a good performance of AI algorithms is already
   challenging, assuming an attacker aiming at compromising it
   emphasizes the problem.  Adversarial AI and notably adversarial ML
   have attracted a lot of attention over the last year.  Adversarial AI
   and ML relates to both attack and defences.  While this is out of
   scope of the document, evaluating threats against an ML system before
   deploying it is an important aspect.  This supposes to assess what
   types of information the attacker can access (training data, trained
   model, algorithm configuration...) and the performable malicious
   actions (inject false training data, test the system, poison a model,
   etc.) to evaluate the magnitude of the impact of possible attacks.

   For illustration purposes, we refer hereafter to some examples.  In
   the case of an intrusion detector, an attacker may try to compromise
   training data by providing adversarial samples to ensure that the
   detector will miss-classify the future attacks [Jmi22].  In a white-
   box approach where the model is known from the attacker, the attack
   can be carefully crafted to avoid being properly labeled.  For
   instance, packet sizes and timings can be easily modified to bypass
   ML-based traffic classification system [Nas21].  In a black-box
   model, the attacker ignores the functioning or training data of the

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   ML systems but can try to infer some information.  For example, the
   attacker can try to reconstruct sensitive information which have been
   used for training.  This type of attack qualified as model inversion
   [Fre15] raises concern regarding privacy.

   All these threats are exacerbated in the context of solutions relying
   on distributed AI involving multiple entities that are not
   necessarily controlled by the same authority.  Once the threats are
   assessed, solutions needs to be developed and deployed which can be
   either proactive by providing some guarantees regarding the involved
   entities using authentication or trust mechanisms but also reactive
   by validating data processing through voting mechanisms or knowledge
   proofs.  Other solutions include defensive techniques to rate limit
   or filter queries to a particular deployed model.  All these examples
   are for illustration purposes and are not exhaustive.

9.3.   Relevance of AI-based outputs

   Security breaches can be created by an AI-driven application.
   Actually,any system that will be use to guide or advice on actions to
   be perform on network raise the same issue.  For example, if an AI
   algorithm decide to change the filtering tables in a network it may
   compromise access control policies.  Irrelevant results could be also
   produced.  In the area of QoS, an AI system could allocate a
   bandwidth to a flow higher to the real link capacity.  As shown from
   these two examples, an AI can produce decisions or values which are
   out of bounds of normal operations.  To avoid such issues, safeguards
   can be added to discard or correct irrelevant outputs.  Detecting
   such type of outputs can be also challenging in complex and
   distributed systems such as a network.  Formal verification methods
   or testing techniques are helpful in that context.

10.  IANA Considerations

   This document has no IANA actions.

11.  References

11.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,

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   [RFC7011]  Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
              "Specification of the IP Flow Information Export (IPFIX)
              Protocol for the Exchange of Flow Information", STD 77,
              RFC 7011, DOI 10.17487/RFC7011, September 2013,

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <>.

   [RFC8986]  Filsfils, C., Ed., Camarillo, P., Ed., Leddy, J., Voyer,
              D., Matsushima, S., and Z. Li, "Segment Routing over IPv6
              (SRv6) Network Programming", RFC 8986,
              DOI 10.17487/RFC8986, February 2021,

   [RFC9315]  Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
              Tantsura, "Intent-Based Networking - Concepts and
              Definitions", RFC 9315, DOI 10.17487/RFC9315, October
              2022, <>.

11.2.  Informative References

   [Abd10]    Jalil, K. A., Kamarudin, M. H., and M. N. Masrek, "A
              Diagnosis Expert System for Network Traffic Management",
              2010.  IEEE international conference on networking and
              information technology

   [Beg19]    Bega, D., Gramaglia, M., Fiore, M., Banchs, A., and X.
              Costa-Perez, "DeepCog: Cognitive Network Management in
              Sliced 5G Networks with Deep Learning", 2019.  IEEE

   [Bis23]    Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J.,
              Coors, S., Thomas, J., Ullmann, T., Becker, M.,
              Boulesteix, A.-L., Deng, D., and M. Lindauer,
              "Hyperparameter optimization: Foundations, algorithms,
              best practices, and open challenges", 2023.  Wiley
              Interdisciplinary Reviews: Data Mining and Knowledge
              Discovery, 13(2)

   [Bos13]    Bosshart, P., Gibb, G., Kim, H.-S., Varghese, G., McKeown,
              N., Izzard, M., Mujica, F., and M. Horowitz, "Forwarding
              metamorphosis: Fast programmable match-action processing
              in hardware for SDN", 2013.  ACM SIGCOMM

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   [Bos14]    Bosshart, P., Daly, D., Gibb, G., Izzard-, M., McKeown,
              N., Rexford, J., Schlesinger, C., Talayco, D., Vahdat, A.,
              Varghese, G., and D. Walker, "P4: programming protocol-
              independent packet processors", 2014.  SIGCOMM Comput.
              Commun.  Rev. 44

   [Bou18]    Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S.,
              Shahriar, N., Estrada-Solano, F., and O. M. Caicedo, "A
              comprehensive survey on machine learning for networking:
              evolution, applications and research opportunities", 2018.
              Journal of Internet Services and Applications 9, 16

   [Bri19]    Brissaud, P.-O., François, J., Chrisment, I., Cholez, T.,
              and O. Bettan, "Transparent and Service-Agnostic
              Monitoring of Encrypted Web Traffic", 2019.  IEEE
              Transactions on Network and Service Management, 16 (3)

   [Cha18]    Chaignon, P., Lazri, K., François, J., Delmas, T., and O.
              Festor, "Oko: Extending Open vSwitch with Stateful
              Filters", 2018.  ACM Symposium on SDN Research (SOSR)

   [Che19]    Chen, Y., Yen, L., Wang, W., Chuang, C., Liu, Y., and C.
              Tseng, "P4-Enabled Bandwidth Management", 2019.  Asia-
              Pacific Network Operations and Management Symposium

   [czb20]    Clemm, A., Zhani, M. F., and R. Boutaba, "Network
              Management 2030: Operations and Control of Network 2030
              Services", 2020.  Springer Journal of Network and Systems
              Management (JNSM)

   [Dat18]    Datta, R., Choi, S., Chowdhary, A., and Y. Park,,
              "P4Guard: Designing P4 Based Firewall", 2018.  IEEE
              Military Communications Conference (MILCOM)

   [Dij19]    Dijkhuizen, N. V., Ham, J. V. D., and X. Li, "A Survey of
              Network Traffic Anonymisation Techniques and
              Implementations", 2014.  ACM Comput.  Surv. 51, 3, Article

   [Evr19]    Evrard, L., François, J., Colin, J.-N., and F. Beck,
              "port2dist: Semantic Port Distances for Network
              Analytics", 2019.  IFIP/IEEE Symposium on Integrated
              Network and Service Management (IM)

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   [Fre15]    Fredrikson, M., Jha, M. S., and T. Ristenpart, "Model
              Inversion Attacks that Exploit Confidence Information and
              Basic Countermeasures", 2015.  ACM SIGSAC Conference on
              Computer and Communications Security

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   This document is the result of a collective work.  Authors of this
   document are the main contributors and the editors but contributions
   have been also received from the following people we acknowledge:
   Laurent Ciavaglia, Felipe Alencar Lopes, Abdelkader Lahamdi, Albert
   Cabellos, José Suárez-Varela, Marinos Charalambides, Ramin Sadre,
   Pedro Martinez-Julia and Flavio Esposito

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Internet-Draft     Coupling AI and network management         March 2024

   This document is also partially supported by project AI@EDGE, funded
   from the European Union's Horizon 2020 H2020-ICT-52 call for
   projects, under grant agreement no. 101015922.

   The views expressed in this document do not necessarily reflect those
   of the Bank of Canada's Governing Council.

Authors' Addresses

   Jérôme François
   University of Luxembourg and Inria
   6 Rue Richard Coudenhove-Kalergi
   L- Luxembourg

   Alexander Clemm
   Futurewei Technologies, Inc.
   United States of America

   Dimitri Papadimitriou
   3NLab Belgium Reseach Center

   Stenio Fernandes
   Central Bank of Canada

   Stefan Schneider
   Digital Railway (DSD) at Deutsche Bahn

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