Internet DRAFT - draft-francois-nmrg-ai-challenges
draft-francois-nmrg-ai-challenges
Internet Research Task Force J. François
Internet-Draft University of Luxembourg and Inria
Intended status: Informational A. Clemm
Expires: 14 September 2023 Futurewei Technologies, Inc.
D. Papadimitriou
3NLab Belgium Reseach Center
S. Fernandes
Central Bank of Canada
S. Schneider
Digital Railway (DSD) at Deutsche Bahn
13 March 2023
Research Challenges in Coupling Artificial Intelligence and Network
Management
draft-francois-nmrg-ai-challenges-02
Abstract
This document is intended to introduce the challenges to overcome
when network management problems may require to couple with AI
solutions. On the one hand, there are many difficult problems in
Network Management 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 SHOULD be
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
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Conventions and Definitions . . . . . . . . . . . . . . . . . 5
3. Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . 5
4. Difficult problems in network management . . . . . . . . . . 5
5. High-level challenges in adopting AI in NM . . . . . . . . . 8
6. AI techniques and network management . . . . . . . . . . . . 10
6.1. Problem type and mapping . . . . . . . . . . . . . . . . 10
6.1.1. Sub-challenge: Suitable Approach for Given Input . . 11
6.1.2. Sub-challenge: Suitable Approach for Desired
Output . . . . . . . . . . . . . . . . . . . . . . . 11
6.1.3. Sub-challenge: Tailoring the AI Approach to the Given
Problem . . . . . . . . . . . . . . . . . . . . . . . 12
6.2. Performance of produced models . . . . . . . . . . . . . 13
6.3. Lightweight AI . . . . . . . . . . . . . . . . . . . . . 15
6.4. Distributed AI . . . . . . . . . . . . . . . . . . . . . 17
6.4.1. Network management for efficient distributed AI . . . 17
6.4.2. Distributed AI for network management . . . . . . . . 18
6.5. AI for planning of actions . . . . . . . . . . . . . . . 19
7. Network data as input for ML algorithms . . . . . . . . . . . 21
7.1. Data for AI-based NM solutions . . . . . . . . . . . . . 21
7.2. Data collection . . . . . . . . . . . . . . . . . . . . . 22
7.3. Usable data . . . . . . . . . . . . . . . . . . . . . . . 24
8. Acceptability of AI . . . . . . . . . . . . . . . . . . . . . 25
8.1. Explainability of Network-AI products . . . . . . . . . . 25
8.2. AI-based products and algorithms in production systems . 27
8.3. AI with humans in the loop . . . . . . . . . . . . . . . 28
9. Security Considerations . . . . . . . . . . . . . . . . . . . 29
10. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 29
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11. References . . . . . . . . . . . . . . . . . . . . . . . . . 29
11.1. Normative References . . . . . . . . . . . . . . . . . . 29
11.2. Informative References . . . . . . . . . . . . . . . . . 30
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 35
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 36
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 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 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 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
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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 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 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
updated.
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
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 human bias. Indeed, the balance between human resources and
the complexity of problems to deal with is actually very imbalanced
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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",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"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 MLP:
Multilayer Perceptron NM: Network Management RL: Reinforcement
Learning
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, combinzatorially exploding with
the size of the problem domain. This makes it impractical to
explore and test every solution (again NP-hard problems here)
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* 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
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 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...
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* 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.
* 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). 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 placement and SFC design: Virtual Network Functions 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).
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* 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).
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.
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
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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.
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
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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).
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.
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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
topology.
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.
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.
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Again, there are rough guidelines for identifying a group of suitable
AI approaches. For example, if a single value is required (e.g., the
amount of resources to allocate to a service instance), then typical
supervised regression approaches SHOULD be used. If classification
(e.g., of malware or another security issue [Abd10]) instead of a
value is desired, supervised classification methods SHOULD be used.
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.
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
management.
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
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
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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.
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? and (3) How to ensure/
improve the quality of produced results?
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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 application. 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.
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Another concrete example is traffic predictors which aim at
forecasting traffic demands. They only produce an input 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).
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
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created models can be deployed, configured, modified and tuned in
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.
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
of programs to be executed. For example, BPF (Berkeley Packet
Filter) 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].
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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 so 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
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.
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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
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
distances.
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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.
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
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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 Alpha Go.
Instead of using AI to merely solve classification problems, Alpha Go
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
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 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
section.
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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
engineering).
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,
(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
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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.
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 BigData
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
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
identification.
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-
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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
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.
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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
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.
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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.
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 all. The same applies for protocol. 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 all Machine Learning (ML) applications is that
they are black boxes. 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.
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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].
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
applciations.)
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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, it is not feasible in production networks. 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).
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
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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, ...). 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,
e.g., for data collection, processing, predictions, validation, etc.
Again, ensuring interoperability between these frameworks can be
tedious.
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
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
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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.
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
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
TODO Security
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,
<https://www.rfc-editor.org/info/rfc2119>.
[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>.
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[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
[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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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 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.
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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
Futurewei Technologies, Inc.
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
Stefan Schneider
Digital Railway (DSD) at Deutsche Bahn
Germany
Email: stefanbschneider@outlook.com
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