Internet Research Task Force J. François
Internet-Draft University of Luxembourg and Inria
Intended status: Informational A. Clemm
Expires: 19 September 2025 Independent
D. Papadimitriou
3NLab Belgium Reseach Center
S. Fernandes
Central Bank of Canada
S. Schneider
Digital Railway (DSD) at Deutsche Bahn
18 March 2025
Research Challenges in Coupling Artificial Intelligence and Network
Management
draft-irtf-nmrg-ai-challenges-05
Abstract
This document is intended to introduce the challenges to overcome
when Network Management (NM) problems may require coupling 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
networks 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.
This document is a product of the Network Management Research Group
(NMRG) of the Internet Research Task Force (IRTF). This document
reflects the consensus of the research group. It is not a candidate
for any level of Internet Standard and is published for informational
purposes.
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Status of This Memo
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3. Difficult problems in network management . . . . . . . . . . 6
4. High-level challenges in adopting AI in NM . . . . . . . . . 9
5. AI techniques and network management . . . . . . . . . . . . 11
5.1. Problem type and mapping . . . . . . . . . . . . . . . . 11
5.1.1. Sub-challenge: Suitable Approach for Given Input . . 12
5.1.2. Sub-challenge: Suitable Approach for Desired
Output . . . . . . . . . . . . . . . . . . . . . . . 13
5.1.3. Sub-challenge: Tailoring the AI Approach to the Given
Problem . . . . . . . . . . . . . . . . . . . . . . . 14
5.2. Performance of produced models . . . . . . . . . . . . . 15
5.3. Lightweight AI . . . . . . . . . . . . . . . . . . . . . 17
5.4. Distributed AI . . . . . . . . . . . . . . . . . . . . . 19
5.4.1. Network management for efficient distributed AI . . . 19
5.4.2. Distributed AI for network management . . . . . . . . 20
5.5. AI for planning of actions . . . . . . . . . . . . . . . 21
6. Network data as input for ML algorithms . . . . . . . . . . . 23
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6.1. Data for AI-based NM solutions . . . . . . . . . . . . . 23
6.2. Data collection . . . . . . . . . . . . . . . . . . . . . 25
6.3. Usable data . . . . . . . . . . . . . . . . . . . . . . . 26
7. Acceptability of AI . . . . . . . . . . . . . . . . . . . . . 28
7.1. Explainability of Network-AI products . . . . . . . . . . 28
7.2. AI-based products and algorithms in production systems . 29
7.3. AI with humans in the loop . . . . . . . . . . . . . . . 31
8. Security considerations . . . . . . . . . . . . . . . . . . . 32
8.1. AI-based security solutions . . . . . . . . . . . . . . . 32
8.2. Security of AI . . . . . . . . . . . . . . . . . . . . . 33
8.3. Relevance of AI-based outputs . . . . . . . . . . . . . 34
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 34
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 34
10.1. Normative References . . . . . . . . . . . . . . . . . . 34
10.2. Informative References . . . . . . . . . . . . . . . . . 35
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 47
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 47
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 has 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] and allows progresses in different
domains such as network security [Ke23], Software-Defined Networks
(SDN) [Ham21], vehicular network [Tan21]. This large adoption has
led to a strong interlinkage between networks and AI, laying the
foundation of future networks such as 6G or B5G networks expected to
be AI-native [Sha25].
However, for maintaining network operational in pre-defined safety
bounds, NM still heavily relies on established procedures. Even
after several cycles of adding automation, these procedures are still
mostly fixed and set offline in the sense that the exact control
loops and all possible scenarios are defined in advance. They are so
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mostly deterministic by nature 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
(RL), Large Language Models (LLMs), 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.
It is thus legitimate to question if ML or AI in general could be
helpful for NM in regard 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. when 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: 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 modelling the
optimization problem as a MILP (Mixed-Integer Linear Programming) and
solve it by means of heuristics to reach a satisfactory trade-off
between solution quality (gap to the optimal solution) - 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 rule-based systems are not
designed to scale with the volume and heterogeneity of data we can
collect in a network today. In contrast, ML is more efficient to
automatically learn abstract representations of the rules, which can
be eventually updated.
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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 with satisfactory results when
properly applied leading to their application in commercial products.
There are many algorithms including decision trees, Support Vector
Machine (SVM) or (deep) Neural Networks (NNs) 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
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 individual human bias. It is worth noting that ML relying on
training data generated by human can also indirectly suffer from a
collective human bias. Indeed, the balance between human resources
and the complexity of problems to deal with is very imbalanced and
this will continue to increase due to the evolving 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.
Actually, all aforementioned problems 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. Acronyms
* AI: Artificial Intelligence
* CNN: Convolutional Neural Network
* FL: Federated Learning
* GAN: Generative Adversarial Network
* GNN: Graph Neural Network
* IBN: Intent-Based Networking
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* LLM: Large Language Model
* LSTM: Long Short-Term Memory
* MAE: Mean Absolute Error
* ML: Machine Learning
* MILP: Mixed-Integer Linear Programming
* MLP: Multilayer Perceptron
* MSE: Mean Squared Error
* NM: Network Management
* RL: Reinforcement Learning
* SDN: Software-Defined Networks
* SFC: Service Function Chaining
* SVM: Support-Vector Machine
* VNF: Virtual Network Function
3. 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 good candidates
for AI-based solutions:
* C1: A very large solution space, combinatorically exploding with
the size of the problem domain. This makes it impractical to
explore and test every solution (again NP-hard problems).
* C2: Uncertainty and unpredictability along multiple dimensions,
including the context in which the solution is applied, behaviour
of users and traffic, lack of visibility into network state, etc.
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.
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* C3: The need to provide answers (i.e. compute solutions, deliver
verdicts, make decisions) in constrained or deterministic time.
In many cases, the context changes dynamically and decisions need
to be made quickly to be useful.
* C4: Data-dependent solutions. To solve a problem accurately, it
can be necessary to rely on large volumes of data and 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
processes.
* 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.
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 [Lop20]. Mobility and
dynamicity are two conditions that make the problem of computing
optimal paths even harder. Hence, adaptive routing based on RL
has been proposed as it allows an agent to promptly react to
topological changes [Sin22]. 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). Such a task analyses 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
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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, various ML-based approaches have
been thus adopted [Bri19][Naj24][Wan24][Ake24] including LLMs
[Gin24].
* Network diagnostics: Disruptions of networking services can have
many causes and thus can rely on analysing many sources of data
(C4). Identifying the root causes is of high importance, so that
repair actions can address them 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 complete 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. Especially, anticipation of faults is of
paramount of importance and will lead to the development of
predictive maintenance in future networks[Mut24].
* 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 [Zha21]. When it is 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,
visualisation techniques are helpful and also rely on AI
techniques to provide the best outputs by reducing the number of
dimensions (C4) and adapting the visualisation of data for human-
handled processes (C5) [Ami24].
* 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 [RFC9315]. The ability to
determine such courses of actions, in particular 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 to 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 implicitly needs
to optimize the actions to be taken. Artificial Intelligence
techniques can be of help here in multiple ways, from accurately
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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. As an
example, LossLeaP even goes further by trying to predict the gap
between a targeted objective and the predicted impact of the
intent realization [Col22].
* VNF (Virtual Network Function) placement and SFC (Service Function
Chain) design: VNFs need to be placed on physical resources and
SFCs designed in an optimized manner to minimize the use of
networking resources and energy (C1,C6). As it is known to be a
NP hard problem, many heuristic- or machine learning based
approaches have been proposed. The VNF paradigm actually emerges
alongside 5G networking and orchestration methods [Att23].
* Smart admission control to avoid congestion and oversubscription
of network resources: Admission control needs to be set up to
ensure service levels are optimized in a manner that is fair and
aligned with application needs, congestion avoided, or its effects
mitigated (C6). This field of research has notably been extended
to the context of network slicing [Vin21][Sul23].
4. 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 widely 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, chatbots or the large progress in autonomous
vehicles. However, the network experts have been focused on building
the Internet, in particular designing 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 [Sha25].
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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 analyse, there are differences: data types
are completely different, networks are by nature heavily distributed,
etc. If problems are different, they should require distinct
solutions or at least in-depth adaptation. In a nutshell, network-
tailored AI was overlooked and leads to a first set of challenges
described in Section 5.
Second, many AI techniques require data representative enough. 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 can be proposed to embed representation of network
entities rather than pure encoding as illustrated in
[Rin17][Evr19][Sol20].
Besides, 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. Actually, this is a common and
known problem that applies to many application domains [Liu22]. For
example, AI techniques used to analyse 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 [Hoa21]. Intelligent
data analysis and the need to maintain privacy are in many ways
contradictory in nature, resulting in an arms race. Similarly,
training ML solutions on real network data is often preferable over
using less-realistic synthetic data sets [Liu22b]. 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 6.
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Finally, networks are already operated thanks to (semi-)automated
procedures involving many resources which are synchronized with
management or orchestration tools. Adding AI supposes so a seamless
integration 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, this can be defined 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 [Liu21] and synchronization to
maintain a coherence over a distributed system.
A related challenge concerns the fact that to be deployed, a solution
needs to provide a technical solution but also be acceptable to users
- in this case, network administrators and operators. 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 held accountable for whether or not the network is running
smoothly. 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 [Sen24].
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. In investigating causes for such outages,
the underlying behaviour of the 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 7.
5. AI techniques and network management
5.1. Problem type and mapping
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 most 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
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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).
Overall, it is a key challenge of using AI technique 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.
5.1.1. 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 textual 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 the selection of an
appropriate AI technique. On one hand, in scenarios with small
dimensional data, classical machine learning techniques (e.g., SVM,
tree-based approaches, etc.) are often sufficient and even superior
to NNs [Gre19]. On the other hand, NNs 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 the Long Short-Term
Memory (LSTM) architecture, and attention-based neural networks
(transformers) are great for sequential data like time series or
text. This evolution leads to the era of LLMs also impacting
research in networking [Hua25][Wu24].
It is worth noting that Graph Neural Networks (GNNs) can incorporate
and consider the graph-structured input, which is very useful in
network management [Jie22], e.g., to represent the network topology.
The aforementioned rough guidelines can help identify a suitable AI
approach or NN architecture. Still, best results are often achieved
with a sophisticated combination of different approaches. For
example, multiple elements can be combined into one architecture
[Ham23], e.g., with both CNNs and LSTMs, and multiple separate AI
approaches can be used as an ensemble to combine their strengths
[Das23]. Here, simplifying the mapping from problem type and input
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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.
5.1.2. 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 a supervised regression approaches should be considered as a
first candidate option as it is lightweight for this type of task.
In case of classification (e.g., of malware or another security issue
[Abd10]) instead of predicting a value is desired, supervised methods
can be used if labelled 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 can be
considered as a good candidate. Alternatively, unsupervised machine
learning can help to cluster given data into separate groups, which
can be useful to analyse networking data, e.g., for better
understanding different types of traffic or user segments.
Furthermore, the quality of the data [Liu22b] 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. To help in building AI-based
solutions, pipeline generators have merged with automated
capabilities, paving the way to the field of AutoML [Urb23].
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In addition to these classical supervised and unsupervised methods,
Reinforcement Learning (RL) approaches allows active, sequential
decisions rather than simple predictions or classification. This is
often useful in network management, e.g., to actively control service
scaling and placement [Sah23]. RL agents autonomously select
suitable actions in a given environment and are especially useful for
self-learning network management. In addition to model-free RL,
model-based planning approaches (e.g., Monte Carlo Tree Search) also
allow choosing suitable actions but require full knowledge of the
environment dynamics. In contrast, model-free RL is ideal for
scenarios with unknown environment dynamics, which is often the case
in network management.
Like 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 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, etc.). Here, further research can simplify the
mapping from desired problem output to choosing or designing a
suitable AI approach.
5.1.3. 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 hyper-
parameters to consider. Selecting a suitable algorithm and
parametrizing it with the right hyper-parameters 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,
support vector regression, regression trees, neural networks, etc.),
each with different benefits and drawbacks and each with its own set
of hyper-parameters. 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 hyper-parameters can be tuned
automatically.
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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 hyper-
parameters 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. As already mentioned, the AutoML field of research provides
solutions to better customize ML algorithms and pipeline. However,
such kind of optimization should be optimized according to domain-
specific metrics rather than pure-AI metrics only. For instance, the
integration of network-specific knowledge can be done through human
feedback [Arz21].
5.2. 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? (4)
How to maintain or improve the quality of produced results?
Many metrics have been already defined to evaluate the performance of
an AI-based technique according 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 of the metrics
differs from each other in terms of importance or impact and can
eventually varies on their domains. It can be hard to precisely
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assess what a good or bad value is (as it might depend on multiple
other ones) and it is even more difficult to integrate this in an AI
technique, especially for learning algorithms to adjust their models
based on performance. Indeed, many learning algorithms run through
multiple iterations and rely on internal metrics, Mean Absolute Error
(MAE) or Mean Squared Error (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 operational metrics of
the NM application. AI-internal metrics such as the loss do not
match well the metrics related to the final NM objectives, thus 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
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 under-provisioning (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].
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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
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. This assumes to collect and
ingest continuously new data. However, as highlighted in [Sha21b],
this type of ML, qualified as online or incremental, raises several
challenges when applied to traffic analysis. For example, there is a
set of related challenges related to select or discard some data over
a time horizon and to label data real-time. Other challenges are
more generic to this ML research area such as class imbalance or
concept drift.
5.3. 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
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switches.
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. Iy is
based on the RMT (Reconfigurable Match Table) architectural model
[Bos13] that is generally accepted to be generic enough to represent
limited but essential switch architecture components and
functionalities. 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, etc. The impact on the program
development is huge. For example, real number operations are not
available by default while they are widely used 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
of programs to be executed. For example, BPF (Berkeley Packet
Filter) [Mcc93] programs provide 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.
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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 well-tuned
implementations for data-plane [Liu16][Yan18]. However, no general
optimized AI-programmable abstraction exists to fit all cases and
proposals are mostly use-case centric. There have been many
propositions to develop specific P4 programs for many NM tasks,
including involving ML. For each, this requires a specific
adaptation [Hau23] with a few attempts to propose generic programs to
be reusable or composed as a kind of libraries such as [Zha24]
leveraging quantization, [Jos21] relying on pre-computed lookup
tables of real-value functions or [Swa23] proposing function
templates for common operations. 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].
5.4. Distributed AI
Distributed AI assumes different related tasks and components to be
distributed across computational and possibly heterogeneous
resources. 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 system is distributed and is
thus well adapted to any distributed application. 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.
5.4.1. 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
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 [Lin21]. For example, in the context of
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optimizing FL [Che22], 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 numbers 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 [Raj24]. It
paves the way to research on methods to support AI-aware NM or
network-aware AI applications or a mix of both.
5.4.2. Distributed AI for network management
For network management applications relying on distributed AI,
challenges from Section 5.4.1 are still valid. Furthermore, network
management problems also consider network-specific elements like
traffic to be analysed or configuration to be set on distributed
network equipment. Co-locating AI processing and these elements
(fully or partially) may help to increase performance. For example,
pre-calculation 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 consider 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 5.4.1 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
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
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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 advantages over
centralized solutions as they avoid issues related to delays caused
by the need to communicate updates globally and across long
distances.
5.5. 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, communication 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 3.
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 [Ahm21].
The emergence of Intent-Based Networking (IBN) emphasizes the need
for automated planning even further. IBN 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 [RFC9315]. 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 [Abd24].
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.
Although AI-based solutions for the automated planning of actions,
including the automated identification of courses of action, have to
this point not been explored as much as classification [Sal20].
problems, the quest for autonomous networks in the last decade and
the advent of 5G and B5G have led to a quick increase in proposed
solutions, as for example within the context of Zero Touch Management
[Cor22].
Also, the evaluation of AI algorithms to derive courses of actions is
complex. 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, verification
techniques can be leveraged to verify decisions made by AI are
maintained within safety bounds [Xin24]. 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 which implies a
tight coupling between a real system and its digital twin to ensure a
parallel but synchronized execution [Wu21]. Alternatively, transfer
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learning techniques in another promising area to be able to
capitalize on ML models applicable on a real word system, for
instance to learn an intrusion detector which can be instantiated in
multiple environment [Sha24][Ans22]. Generally, it is also an open
problem to make the use of AI more acceptable as highlighted in the
dedicated section.
6. Network data as input for ML algorithms
Many applications of AI take data as input. The quality of the
outputs of ML-based techniques are highly dependent on the quality
and quantity of data used for learning but also during the inference.
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.
6.1. 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
engineering). Furthermore, machine learning algorithms only work as
desired when data to be analysed respects given 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. A solution
to circumvent the problem of network traffic representation is to
transform traffic data into a specific format that can be easily
handled by NN architectures, for instance by creating images analysed
by convolutional neural networks [Sha21]. Data can also be easily
represented as graphs because topologies or communication graphs
would require less adaptation to be given as inputs to GNNs [Jie22].
As an example, graph-based representations are considered as
practically efficient due to their intrinsic structural similarities
with network data as illustrated in [Bar23] for attack detection.
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Since data to handle can also 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 earlier. Today, with the progress on
LLMs, different propositions have emerged as for example in
[Mek24][Fua24][Dze23].
Secondly, similar to the problem of mapping AI algorithms with NM
problems in section Section 5.1, 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 in advance and some might be be discovered
by the AI algorithms themselves [For23]. 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.
Thirdly, the behaviour 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 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 may have in
networks. For instance, the effect of a “flash crowd” detected in an
external source may require adapting the network service
configuration to support is related workload. 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 close the loop, so the AI engine can receive feedback
from the network in order to improve its own behaviour.
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6.2. 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 [Mac22] 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
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
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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
challenge.
6.3. 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 and 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)
for example to ensure by-design privacy in traffic analysis
techniques [Wan22]. The integration of these non-technical
constraints should be considered when defining new data to be
collected or a new technique 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.
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For supervised or semi-supervised training, having a labelled dataset
is a prerequisite. It constitutes a major challenge as well. While
probes exist to collect real network data, those data are typically
unlabelled. This limits application of ML to unsupervised learning
tasks (learning from data). Because manual labelling 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. More
generally, in the context of intrusion detection, using raw data
rather than pre-processed data as it is common in open dataset has
been demonstrated to be inefficient [Dub24].
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 unlabelled 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 like 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, Generative
Adversarial Networks (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. Furthermore, a major underlying
challenge is to generate data realistic enough. This requires
formalizing and integrating network-specific constraints, such as
protocol specification, when generating data [Jia24].
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7. 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 need to 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, RL 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.
7.1. Explainability of Network-AI products
A common issue across many 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 behaviour 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].
This set of techniques provides insights and, in some cases,
guarantees on the performance and behaviour of ML-based solutions.
Understanding such techniques, researching and applying them to
network AI is critical for the success of the field.
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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
analysing their neural activations on different inputs. It is worth
noting 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. However, 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 applications.)
7.2. 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). 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 hold,
and additional safe-guarding mechanisms should be considered in order
to keep outputs of ML algorithms (such as decisions) within
acceptable boundaries.
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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 the learned
models to 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. This well-known problem is due to the
dependency between training and testing data when benchmarking
models.
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
traffic patterns will help improving the generalization of an AI-
based traffic analyser. Another approach is to leverage AI designs
or architectures that facilitate generalization. One example is GNNs
[gnn1][gnn2]. They are a 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 data.
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, etc.). Deploying the same AI solutions in
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,
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e.g., for data collection, processing, predictions, validation, etc.
Again, ensuring interoperability between these frameworks can be
tedious.
This shares similarities with problems described in Section 5.4 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-
premises or at different cloud providers) [Mor18].
7.3. 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 [Wu22], which could be useful for
different aspects of network management.
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
process.
When using RL, e.g. to control service scaling and placement or route
traffic flows, theagents 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 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 RL agent
based on human feedback rather than a pre-defined reward function
[Lee21]. Again, while there are first promising approaches, more
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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.
8. 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, 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 serve as the common basis to be refined according when
a particular NM application is developed.
8.1. 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 tend to obfuscate how predictions are made and decisions
taken. Explainability of AI is thus highly important and is
addressed globally in section Section 7.1.
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
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 configuration. 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
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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 5.1.3 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 analysed 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 Section 6 of this document fully addresses the concerns
related to data in regard to NM problems.
8.2. 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 poison
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 labelled. 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
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
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assessed, solutions need 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.
8.3. Relevance of AI-based outputs
Security breaches can be created by an AI-driven application.
Generally, any system that will be used to guide or advice on actions
to be perform on network raise the same issue. For example, if an AI
algorithm decides 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.
9. IANA Considerations
This document has no IANA actions.
10. References
10.1. Normative References
[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,
<https://www.rfc-editor.org/info/rfc7011>.
[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,
<https://www.rfc-editor.org/info/rfc8986>.
[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, <https://www.rfc-editor.org/info/rfc9315>.
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[Hau23] Hauser, F., Häberle, M., Merling, D., Lindner, S.,
Gurevich, V., Zeiger, F., Frank, R., and M. Menth, "A
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[Hoo18] Hooft, J. V. D., Claeys, M., Bouten, N., Wauters, T.,
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Acknowledgments
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
This document was 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. This research is
funded in part, by the Luxembourg National Research Fund (FNR), grant
reference C23/IS/18088425/COCTEL. 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
Luxembourg
Email: jerome.francois@uni.lu
Alexander Clemm
Independent
United States of America
Email: ludwig@clemm.org
Dimitri Papadimitriou
3NLab Belgium Reseach Center
Leuven
Belgium
Email: papadimitriou.dimitri.be@gmail.com
Stenio Fernandes
Central Bank of Canada
Canada
Email: stenio.fernandes@ieee.org
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Stefan Schneider
Digital Railway (DSD) at Deutsche Bahn
Germany
Email: stefanbschneider@outlook.com
François, et al. Expires 19 September 2025 [Page 48]