A Fault Management Preventive Maintenance Approach in Mobile
Networks using Sequential Pattern Mining
Márcio Pereira
1,3 a
, David Duarte
1b
and Pedro Vieira
2,3 c
1
CELFINET, Consultoria em Telecomunicações, Lda., Lisboa, Portugal
2
Instituto Superior de Engenharia de Lisboa (ISEL), Lisboa, Portugal
3
Instituto de Telecomunicações (IT), Lisboa, Portugal
Keywords: Fault Management, Machine Learning, Preventive Maintenance, Sequential Pattern Mining.
Abstract: Mobile networks' fault management can take advantage of Machine Learning (ML) algorithms making its
maintenance more proactive and preventive. Currently, Network Operations Centers (NOCs) still operate in
reactive mode, where the troubleshoot is only performed after the problem identification. The network
evolution to a preventive maintenance enables the problem prevention or quick resolution, leading to a greater
network and services availability, a better operational efficiency and, above all, ensures customer satisfaction.
In this paper, different algorithms for Sequential Pattern Mining (SPM) and Association Rule Learning (ARL)
are explored, to identify alarm patterns in a live Long Term Evolution (LTE) network, using Fault
Management (FM) data. A comparative performance analysis between all the algorithms was carried out,
having observed, in the best case scenario, a decrease of 3.31% in the total number of alarms and 70.45% in
the number of alarms of a certain type. There was also a considerable reduction in the number of alarms per
network node in a considered area, having identified 39 nodes that no longer had any unresolved alarm.
These results demonstrate that the recognition of sequential alarm patterns allows taking the first steps in the
direction of preventive maintenance in mobile networks.
1 INTRODUCTION
Fault Management (FM) optimization in mobile
networks involves taking advantage of Machine
Learning (ML) techniques to make its maintenance
proactive and preventive.
Mobile network operations still work in reactive
mode, i.e., the diagnosis and the problem solving
starts only after a network malfunction occurs, a
service is impacted, or when a customer complains.
The engineers have access to a lot of information such
as alarms, performance measurements and more, but
they lack an effective way to quickly solve issues.
Thus, Mean Time To Repair (MTTR) is affected,
impacting network and service availability,
operational efficiency, and customer satisfaction.
Minding a solution for preventive maintenance,
operators can leverage ML to reduce operational
costs, improve network and service availability,
a
https://orcid.org/0000-0003-3354-973X
b
https://orcid.org/0000-0002-9774-4115
c
https://orcid.org/0000-0003-0279-8741
improve customer satisfaction, and reduce missed
Service Level Agreements (SLAs). In this context,
the main goal of this paper is to create a solution for
preventive maintenance of mobile networks' alarms,
using real data, that:
1. Mines alarms clusters and establishes
relationships between them, forming
association rules;
2. Continuously learns from new data, improves
over time, and builds expertise in the network
maintenance domain;
3. Defines antecedent and consequent alarms in
a sequential pattern, where they are sorted
chronologically;
4. Recognizes the most frequent patterns in order
to find the most concerning faults and identify
the main advantages that come from their
prevention.
76
Pereira, M., Duarte, D. and Vieira, P.
A Fault Management Preventive Maintenance Approach in Mobile Networks using Sequential Pattern Mining.
DOI: 10.5220/0011308100003286
In Proceedings of the 19th International Conference on Wireless Networks and Mobile Systems (WINSYS 2022), pages 76-83
ISBN: 978-989-758-592-0; ISSN: 2184-948X
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Some recent related work will be presented in the
following. In (Araújo, 2019), the alarms’ proactive
management is addressed as a way to take advantage
of ML algorithms to follow the evolution of mobile
networks and services operations. In (Mulvey, 2019)
and (Nouioua, 2021), research is carried out on the
application of ML techniques and data mining in the
fault management of telecommunications networks,
from an operational point of view. The authors
surveyed several machine learning based techniques
for fault management, including the ones used in this
work.
The paper is organized as follows: Section 2
briefly presents the study carried out on Association
Rule Learning (ARL) and Sequential Pattern Mining
(SPM) algorithms, its methodology and their
respective evaluation metrics. Section 3 presents the
experimental results of all the discussed algorithms'
implementation, with a comparison of their
efficiencies, and finally Section 4 concludes the
paper.
2 METHODOLOGY
2.1 Fault Management Data
The used dataset contains several alarm logs from a
live LTE network. These logs contain the timestamp,
severity, name, type, technology, detailed
information, and the network node associated with
each alarm.
As described in (Huawei, 2015), the alarms can be
classified, as to their severity, as follows:
1. Critical: A critical alarm affects system services.
As soon as it is triggered, immediate actions must
be taken, even when the fault occurs outside
working hours;
2. Major: A major alarm affects the quality of
service and requires immediate action, only if
occurs during working hours;
3. Minor: A minor alarm usually does not affect the
quality of service. It should be treated as soon as
possible or monitored to avoid potential severe
failures;
4. Warning: A warning alarm indicates a possible
error that could affect the quality of service.
Requires different actions depending on specific
errors.
In the sequence, alarms are further categorized by
their type, depending on their operational area:
Power; Environment; Signaling; Trunk; Hardware;
Software; System; Communication; Service quality;
Unexpected operation; Operations and Maintenance
Center (OMC); Integrity; Operation; Physical
resource; Security; Time domain; Running;
Processing.
2.2 Association Rule Learning
Association Rule Learning (ARL) allows extracting
correlations or strong associations, hidden between
sets of items present in transactions in a certain
dataset. The association rules problem defines a
transaction as a set of items where each item can have
different attributes. The used dataset contains several
transactions. An association rule is an implication,
written as 𝐴→𝐶, where A and C are sets of items
called antecedent and consequent, respectively.
Generally, there are two metrics for evaluating an
association rule, support and confidence, presented in
equations (1) and (2), respectively. As datasets
usually store large amounts of information but only
the most frequent transactions are interesting,
minimum values are defined for these evaluation
metrics, which help to filter out less frequent rules.
Support: Relative frequency or occurrence
probability of a transaction. It can take values
between 0 and 1.
Support(AC) =
Occurrences of AC
T
o
t
a
l n
u
m
be
r
o
f tr
a
n
sac
ti
o
n
s
(1)
Confidence: Probability of a transaction
containing the consequent, knowing that it also
contains the antecedent. Confidence is
maximum if the consequent and the antecedent
always occur together. It is not symmetric, i.e.,
the confidence of AC is different from the
confidence of CA.
Confidence(AC) =
Support(AC)
Support(A)
(2)
In addition to these two metrics, others can be
used to better classify the association rules,
considering other properties that both support and
confidence cannot quantify. In the scope of this work,
three more evaluation metrics were used: lift,
leverage and conviction, and set in equations (3), (4)
and (5), respectively. These metrics were not used to
filter rules, but to better evaluate them.
Lift: Quantifies how frequent the simultaneous
occurrence of A and C is compared to what
A Fault Management Preventive Maintenance Approach in Mobile Networks using Sequential Pattern Mining
77
would be expected if they were statistically
independent. If A and C are independent, the
lift will be equal to 1.
Lift(AC) =
Confidence(AC)
Support(C)
(3)
Leverage: Difference between the frequency
of A and C occurring together and the
frequency that would be expected if A and C
were independent. A value of 0 indicates
independence between the two itemsets.
Leverage(AC) = Support(A
C) – Support(A) × Support(C)
(4)
Conviction: A high value means that C is
strongly dependent on A. For example, if the
confidence is equal to 1, the denominator will
be 0, so the conviction will be
. Like lift, if
the items are independent, the conviction is
equal to 1.
Conviction(AC) =
  ()
 ()
(5)
Under this work, three ARL algorithms were
implemented: Apriori, Equivalence CLAss
Transformation (ECLAT) and Frequent Pattern
(FP)Growth, and extracted from (Agrawal, 1994),
(Zaki, 2000) and (Han, 2004), respectively.
2.2.1 Apriori
This algorithm has to access the dataset several times
to obtain the frequent itemsets. In the first access, the
support is counted for each item individually (level 1
itemsets). Then, with a minimum defined support
value, S, there are excluded rare items, i.e., those
whose support is lower than S.
In later accesses, higher-level itemsets containing
rare items are no longer considered, because if an
item’s support, S
i
, is lower than S, then all subsets that
contain it will have a support equal or lower than S
i
and, thus, lower than S (Apriori property).
This process is repeated until there are no more
frequent itemsets to evaluate. The final list of frequent
items is the junction of all the lists created for each
level, including the support values calculated for each
frequent itemset.
2.2.2 ECLAT
ECLAT is an improved version of the Apriori
algorithm. While Apriori uses a horizontal dataset
representation, ECLAT transforms it into its vertical
representation where, instead of indicating the
itemsets that belong to each transaction, it lists the
transactions in which item occurs.
Transaction lists for higher level itemsets are
created recursively, calculating the intersection of the
transaction lists (from the previous level) of each
item. If the intersection is null, the itemset is removed
from the list. This process is over when, for a certain
level, all intersections are null.
If the minimum support is set to the same value,
the final list of frequent itemsets will be identical to
that of the Apriori algorithm. However, ECLAT takes
up less memory throughout its process, manages to be
faster by using a vertical approach in which its
calculations are done in parallel and ends up
performing fewer accesses to the dataset, because it is
possible to calculate the support values for any level.
2.2.3 FPGrowth
The FPGrowth algorithm implementation considers
the Frequent Pattern (FP)-tree, a tree that contains the
prefixes of the transactions. Each tree path represents
a set of transactions that share the same prefix, where
each node corresponds to a single item. Furthermore,
all nodes referring to the same item are linked
together, so that all transactions that contain a certain
item can be easily found and accounted for when
traversing this tree.
The main operation that the FPGrowth algorithm
has to perform is to build the FP-tree of a projected
dataset, i.e., a dataset with the transactions that
contain a certain item, with that item removed. This
projected dataset is processed recursively, not
forgetting that the frequent itemsets found share the
same prefix – the item that was removed.
After building the FP-trees for all the necessary
dataset projections, the process of eliminating some
of the nodes associated with rare items is carried out
in order to simplify the tree and speed up the process.
Thanks to an efficient implementation of FP-trees,
the FPGrowth algorithm largely outperforms the
previously presented algorithms (Apriori and
ECLAT), both in terms of execution time and the
memory required, since the storage of the dataset
using a tree representation is more compact than the
full list of transactions.
2.3 Sequential Pattern Mining
Sequential Pattern Mining (SPM), unlike ARL, takes
into consideration the items’ order in each sequence,
allowing to discover frequent patterns in a dataset,
which may prove useful or interesting to be explored.
WINSYS 2022 - 19th International Conference on Wireless Networks and Mobile Systems
78
The objective of SPM algorithms is to find all
patterns (sub-sequences) that have a support higher
than or equal to the minimum support value defined
by the user. Therefore, the patterns they each find are
the same for all the algorithms. What differentiates
them is solely their efficiency in recognizing those
patterns.
Three SPM algorithms were also implemented:
PrefixSpan, Sequential PAttern Discovery using
Equivalence classes (SPADE) and Sequential
PAttern Mining (SPAM), and extracted from (Pei,
2004), (Zaki, 2001) and (Ayres, 2002), respectively.
2.3.1 PrefixSpan
PrefixSpan is a pattern-growth algorithm, based on
FPGrowth. It is the only one of the three studied
algorithms that does not consider all possible
combinations for the patterns that can be found.
Recursively accesses the dataset to concatenate new
items until the complete pattern is formed, therefore,
it only considers the patterns that exist in the dataset.
These successive accesses can, however, be time
consuming, so the concept of “projected dataset” was
introduced, to reduce its size, optimizing the access.
In terms of memory, creating multiple dataset
projections can take up a lot of data storage space.
2.3.2 SPADE
Inspired by ECLAT, this algorithm uses a vertical
representation of the dataset, created during the first
access, indicating in which itemsets and in which
sequence each of the items is found.
The vertical representation has two interesting
properties for recognizing sequential patterns. The
first property is that the list created for any sequence
allows directly calculating its support. The second
property is that the list of any sequence can be
obtained, without directly accessing the original
dataset, by joining the various lists of the sub-
sequences that compose it.
By taking advantage of these properties,
algorithms such as SPADE and even SPAM perform
their discovery for sequential patterns without
repeatedly accessing the dataset and, therefore,
without keeping many patterns in memory.
2.3.3 SPAM
Similar to the algorithm presented above, SPAM
manages to be even more efficient by optimizing the
structure of the pattern list. This algorithm encodes
these lists as binary vectors, which cuts the memory
necessary to store the same information. In addition,
it speeds up the mathematical operations that need to
be performed. It can still be improved with the use of
compression techniques that reduce the number of
used bits.
This algorithm has been shown to be faster than
SPADE and PrefixSpan, especially for relatively
large datasets. In terms of memory, SPADE still
manages to be the more efficient of the two.
3 EXPERIMENTAL RESULTS
In order to discover the association rules and
sequential patterns within the network, real FM data
were used to test, analyse, and compare the presented
algorithms.
To evaluate every association rule, the minimum
support value was set to “2 occurrences / Total
number of transactions”, i.e., it only takes a repetition
for an itemset to be considered frequent. A minimum
confidence of 50% was also imposed, which indicates
that, at least half of the transactions that contain the
antecedent also contain the consequent (group of
alarms).
Before analysing the results and to assess the
efficiency of each algorithm, the execution times and
the used memory were quantified. These results were
obtained from tests performed on a machine with the
following specifications: Intel® Core™ i5-7300HQ
CPU @ 2.50GHz (4 CPUs), 8GB RAM, Windows 10
Education 64bits.
3.1 Association Rule Learning
In the implementation of the algorithms Apriori and
FPGrowth, the MLxtend library (Raschka, 2018) was
used. For ECLAT, it was used the PyFIM library
(Borgelt, 2012). The tests were carried out for a time
window from 1 to 30 minutes, and with one minute
granularity. Comparative graphs for the different time
windows are shown in Figure 1 and Figure 2, for the
execution time and memory usage, respectively.
Figure 1: Execution time of each ARL algorithm, for
several time windows.
A Fault Management Preventive Maintenance Approach in Mobile Networks using Sequential Pattern Mining
79
As in Figure 1, the execution time of all
algorithms decreases as the time window increases.
Although, with this growth, there are transactions
with more sets of items, the number of transactions
decreases and, also, there are fewer evaluation
metrics to calculate, which can justify the time
reduction. Comparing the executions time of each
algorithm, it is possible to verify that the fastest is
FPGrowth, followed by ECLAT and, finally, Apriori.
Figure 2: Memory usage of each ARL algorithm, for several
time windows.
Considering Figure 2, despite constant
fluctuations, it is possible to verify that the used
memory increases as the time window increases,
which is justified by the growth of the number of
items in each transaction. In general, it is possible to
conclude that the most efficient in terms of memory
occupation is also FPGrowth. ECLAT manages to be
very optimized for small transactions, but after a
certain point, it uses even more memory than Apriori.
The list of rules, identical to all algorithms, was
sorted by the Lift metric in descendent order, since
those with the higher value are the ones with a greater
dependence between the rule’s antecedent and
consequent. The top 6 rules are shown in Table 1. The
confidence and conviction metrics were omitted as
they were the same for all evaluated relations and
time windows, having the values 1 and ,
respectively.
Despite the support values being low, the
algorithms demonstrate conviction that these
associations are quite strong, i.e., when a certain
antecedent alarm occurs, the consequent alarm will
occur in the following.
Note that, for a given time window and network
node, there is always a pair of symmetric rules, i.e.,
there is always a second rule where the antecedent is
the consequent, and the consequent is the antecedent
of the first rule. Therefore, at this stage, it is not
possible to be sure about the order of occurrence of
the alarms, and it is necessary to move forward
resorting to Sequential Pattern Mining (SPM).
Table 1: The top 6 association rules, and respective evaluation metrics calculated by the three algorithms, with greater Lift.
Minute(s) Network node Antecedent(s) Consequent(s) Support Lift Leverage
15
21
BX94BL
RF Unit ALD
Current Out of Range
ALD Maintenance Link
Failure
0.00134
0.00157
741.5
634.0
0.00134
0.00157
15
21
BX94BL
ALD Maintenance
Link Failure
RF Unit ALD Current Out of
Range
0.00134
0.00157
741.5
634.0
0.00134
0.00157
1
2
VA08OL X2 Interface Fault
Inter-System Cabinet
Configuration Conflict
0.00151
0.00158
662.0
632.0
0.00150
0.00157
1
2
VA08OL
Inter-System Cabinet
Configuration
Conflict
X2 Interface Fault
0.00151
0.00158
662.0
632.0
0.00150
0.00157
2 BX16QL Certificate Invalid
External Clock Reference
Problem
0.00202 494.0 0.00202
2 BX16QL
External Clock
Reference Problem
Certificate Invalid 0.00202 494.0 0.00202
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3.2 Sequential Pattern Mining
In the implementation of the algorithms, the extensive
framework for data mining SPMF (Fournier-Viger,
2016) was used.
In the previous section, it was noticed that there
was no need to test several time windows (1 to 30
minutes, every minute) in the definition of
transactions, as the results were similar for all of
them. Therefore, for these algorithms, transactions
were defined based on a single dynamic window.
Additionally, there is a timeout interval in which if no
alarm happens during that interval, the next alarm that
occurs will belong to the next transaction.
The timeout value was defined considering the
predominant association rules in the previous section,
namely those that contained alarms associated with
the Antenna Line Device (ALD). Since the objective
is to have this pair of alarms always in the same
transaction, the timeout was fixed at 11 minutes,
because this is the maximum time interval registered
between the two alarms.
The execution times and memory usage of each
algorithm were measured, for the dynamic window,
and are presented in Table 2.
Observing the average value of three tests
executed in a row, the fastest algorithm is the SPAM,
followed by PrefixSpan, and finally SPADE. In terms
of used memory, the order is reversed, with SPADE
being the most efficient, followed by PrefixSpan and,
finally, SPAM.
Table 2: Execution times and memory usages of each
algorithm, for the dynamic time window.
Algorithm Execution time
Maximum
memory
usage
PrefixSpan 14 minutes 43 seconds 179.07 MB
SPADE 15 minutes 9 seconds 178.06 MB
SPAM 13 minutes 50 seconds 180.18 MB
The list of patterns, identical to all algorithms,
was sorted by the Lift metric in descendent order, as
those with this highest value are those with a greater
dependency among all the alarms that make up the
pattern. The top 5 patterns are shown in Table 3.
As already noticed, the same alarms that had
already appeared in the association rules of the
previous section continue to be present. However, in
this analysis, there are no longer symmetrical rules,
only the patterns, which, by themselves, already
indicate the real order in which the alarms occur.
All listed patterns have been analysed using the
alarm’s vendor documentation. Furthermore,
assuming that when the antecedent is solved, the
consequent no longer happens, and that the time
interval between them is sufficient to report and solve
the failure, the consequent will be removed from the
original dataset, and the maximum reduction in the
number of alarms will be calculated.
The next sections will present some use cases
considering the patterns found in Table 3.
Table 3: The top 5 sequential patterns, and respective evaluation metrics calculated for the three algorithms, with greater Lift.
Network node Pattern Support Confidence Lift Leverage Conviction
VA08OL
{Inter-System Cabinet
Configuration Conflict, X2
Interface Fault}
0.00259 1.0 386.5 0.00258
VA83WL
{RF Unit ALD Current Out of
Range, ALD Maintenance Link
Failure}
0.00274 1.0 364.33 0.00274
BX16QL
{Certificate Invalid, External Clock
Reference Problem}
0.00197 0.66 226.0 0.00196 2.9912
AB42AL
{Inter-System Cabinet
Configuration Conflict, X2
Interface Fault}
0.00476 1.0 210.0 0.00474
TX49BL
{ALD Maintenance Link Failure,
External Clock Reference Problem}
0.00477 1.0 209.66 0.00475
A Fault Management Preventive Maintenance Approach in Mobile Networks using Sequential Pattern Mining
81
3.2.1 RF Unit ALD Current out of Range
ALD Maintenance Link Failure
In the context of preventive maintenance, it is
necessary to understand the benefits of being able to
recognize the patterns that happened in the past, in
order to predict and prevent what will happen in the
future. In this case, by preventing the antecedent
alarm, RF Unit ALD Current Out of Range, 715
“ALD Maintenance Link Failure” alarms are avoided.
This mainly translates into the reduction of the
number of alarms as follows:
“Communication” type alarms: 1860 1145
(-38.44%);
“VX73KL” node alarms: 40 22 (-45%);
“CS12KL” node alarms: 87 51 (-41.38%);
“VX37KL” node alarms: 179 107 (-
40.22%);
“CR29VL” node alarms: 64 44 (-31.25%).
3.2.2 Inter-System Cabinet Configuration
Conflict X2 Interface Fault
By solving the errors and configuration conflicts in
the cabinet, 6327 “X2 Interface Fault” alarms can be
prevented from happening. This means a reduction
of:
Total number of alarms: 191053 184726 (-
3.31%);
“Signaling” type alarms: 8981 2654 (-
70.45%);
“LO01AL” node alarms: 37 18 (-51.35%);
“VX83EL” node alarms: 74 37 (-50%);
“VX49KL” node alarms: 53 27 (-49.06%).
3.2.3 Certificate Invalid External Clock
Reference Problem
By properly validating the certificate, it can prevent a
lot of External Clock Reference Problem” alarms
from happening. As this pattern shares the same
consequent as the next, the calculation and analysis of
this impact is made for the next pattern.
These two patterns are between “Major” alarms,
while the first two were between aMinor
antecedent and a “Major” consequent. This allows to
deduce, after analysing the 4 cases, that the sequence
of alarms always follows an increasing order in terms
of severity. Of course, there are alarms caused by
other ones with the same severity, but a less severe
alarm than the previous one is unlikely to occur (for
example, Major Minor).
3.2.4 ALD Maintenance Link Failure
External Clock Reference Problem
This pattern is the second (from the ones presented)
in which the “External Clock Reference Problem”
alarm is found as a consequence. This suggests that
the problems in the external clock reference can be
indirectly caused by various sources on the network,
which leads to a bad configuration of the clock or the
malfunction of some hardware element, essential for
proper clock synchronization.
Resuming the correct external reference clock
synchronization, it prevents the occurrence of 1414
“External Clock Reference Problem” alarms. This
mainly translates into the reduction of:
“Hardware” type alarms: 7969 6555 (-
17.74%);
There are 39 nodes that no longer have any
unresolved alarm (-100%);
“VX30EG” node alarms: 18 1 (-94.44%);
“CR06VU” node alarms: 22 4 (-81.82%);
“VX88EU” node alarms: 50 12 (-76%).
Hence, it is concluded that, with sequential
pattern mining, it is possible to predict which will be
the most likely alarm to be the consequence of a given
antecedent alarm. Within the scope of preventive
maintenance, if the problem resolution is quick and
effective, it is possible to completely prevent the
consequent alarm from occurring, resulting in a
decrease in the number of triggered alarms and
failures caused by them.
4 CONCLUSIONS
This work aimed to develop and test a solution for
proactive and preventive maintenance in LTE mobile
networks, by using fault management data. Two types
of machine learning techniques for handling this data
were explored: ARL and SPM.
For the ARL algorithms, FPGrowth presented the
best performance in terms of execution time and used
resources. ECLAT was the most efficient for short
transactions, being surpassed by Apriori for larger
transactions. In all of them there was a symmetry in
the association rules, having therefore evolved to the
SPM algorithms, where the order of alarms’
occurrence is strongly important.
For the SPM algorithms, SPAM was the most
efficient in terms of execution time, but the worst in
terms of resource utilization. SPADE was the most
efficient in terms of resource utilization but the
slowest of all. On the other hand, PrefixSpan offered
WINSYS 2022 - 19th International Conference on Wireless Networks and Mobile Systems
82
a good compromise among all. In all the performed
tests, it was possible to conclude that the prevention
of consequent alarms results in a decrease in their
absolute number in the network’s nodes where they
occurred.
In the best case scenario, there was a decrease of
3.31% in all analysed nodes, and 70.45% in terms of
alarms of the same type. It was also noticed that 39
network nodes no longer had any unresolved alarm.
These results demonstrate that sequential pattern
mining drives the preventive maintenance of alarms
in a LTE mobile network, reinforcing the preventive
maintenance’ importance for Mobile Network
Operators (MNOs).
ACKNOWLEDGEMENTS
This work was carried out in the scope of the
international project Cognitive and Automated
Network Operations for Present and Beyond
(CANOPY) AI2021-061, under the CELTIC-NEXT
Core Group and the EUREKA Clusters program. The
authors would like to thank the COMPETE/FEDER
program for funding the national component of the
project (14/SI/2021), as well as the Instituto de
Telecomunicações (IT) and CELFINET for its
support.
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