F LOW N EURAL N ETWORK FOR T RAFFIC F LOW M ODELLING IN IP N ETWORKS
Anonymous authors
Paper under double-blind review
A BSTRACT
This paper presents and investigates a novel and timely application domain for deep learning: sub-second trafﬁc ﬂow modelling in IP networks.
Trafﬁc ﬂows are the most fundamental components in an IP based networking system.
The accurate modelling of the generative patterns of these ﬂows is crucial for many practical network applications.
However, the high nonlinearity and dynamics of both the trafﬁc and network conditions make this task challenging, particularly at the time granularity of sub-second.
In this paper, we cast this problem as a representation learning task to model the intricate patterns in data trafﬁc according to the IP network structure and working mechanism.
Accordingly, we propose a customized Flow Neural Network , which works in a self-supervised way to extract the domain-speciﬁc data correlations.
We report the state-of-the-art performances on both synthetic and realistic trafﬁc patterns on multiple practical network applications, which provides a good testament to the strength of our approach.
I NTRODUCTION
Deep Learning (DL) has gained substantial popularity in light of its applicability to real-world tasks across computer vision, natural language processing (Goodfellow et al., 2016), protein structure prediction (Senior et al., 2020) and challenging games such as Go (Silver et al., 2017).
Typically, the data for these learning tasks takes the form of either grids, sequences, graphs or their combinations.
The tremendous efforts on customizing neural network structures (Krizhevsky et al., 2012; Kiros et al., 2015; Hochreiter & Schmidhuber, 1997) and learning strategies (Sermanet et al., 2018; Oord et al., 2019) to explore the data-speciﬁc properties underpin the success of modern DL in these domains.
Following the same design philosophy, we wish to capitalize on these advancements to develop a customized neural network and self-supervised learning strategy to tackle the crucial and timely challenge of trafﬁc ﬂow modelling in IP networks.
1. T RAFFIC F LOW M ODELLING IN IP N ETWORKS
An IP network is a communication network that uses Internet Protocol (IP) to send and receive messages between one or more devices such as computers, mobile phones.
The messages could be general application data such as video, emails or control signals of any connected devices.
When sending the messages from a source to a destination, the source device encapsulates the bit chunks of encoded messages into a set of IP packets.
The packets then travel through communications links and routers or switches in a given routing path sequentially, thus forming the trafﬁc ﬂows in an IP network (Hunt, 1992).
As one of the most commonly used global networks, the IP network provides the majority of such data transmission services to support today’s Internet applications such as video streaming, voice-over-IP, and Internet of Things.
Therefore, a good understanding of the behaviorial patterns of the underlying trafﬁc ﬂows plays a crucial role in network planning, trafﬁc management, as well as optimizing Quality of Service (QoS, e.g., transmission rate, delay).
This challenge is termed as trafﬁc ﬂow modelling and is fundamental to IP networking research and practice.
However, the high nonlinearity, randomness and complicated self similarity (Leland et al., 1994) of these trafﬁc thwart extensive traditional analytical and learning models, particularly at ﬁne-grained time scales, such as trafﬁc ﬂow modelling at a sub-second level.
Consider the illustrative example in Fig.
1, which depicts multiple packet ﬂows with shared forwarding nodes and links in their routing paths.
The sender of each ﬂows streams data packets to the
[Figure]
receiver at a dynamic sending rate, which is determined according to many factors such as its rate demand, existing trafﬁc loads, available link bandwidth, and etc.
The packets usually experience various delays on the journey due to actions such as forwarding processing, link transmission, packet queueing.
For example, when the sum rate of Sender 2 and 3 exceeds 10 Gbps, the router R2–R will hold off and cache the arriving packets in their buffers until the links from R2 to Receiverbecome free, causing what is known as the queueing delay.
The extent of these delays depends on multiple factors, including the amount of trafﬁc going on, the capacity of the router’s output queue, link bandwidth etc.
The random establishment, interaction and termination of massive ﬂow connections give rise to network dynamics.
This illustrates the complexity of trafﬁc ﬂow modelling in
IP network even for the simple example.
This challenge is exacerbated when the trafﬁc loads are running at over 100 Gbps and in a network with signiﬁcantly larger size in practice.
1. M OTIVATING F LOW NN B ASED T RAFFIC F LOW M ODELLING
A ﬂow pattern can be deﬁned as anything that follows a trend and exhibits some kind of regularity, e.g., distribution, periodicity etc.
The modelling of trafﬁc ﬂow patterns can be done mathematically or by the use of data-driven learning algorithms.
We argue that developing a customized FlowNN in the context of IP trafﬁc ﬂow modelling is important in two aspects: 1) improving the performances of supported network applications from the accurate modelling towards the behavioral patterns of trafﬁc ﬂows in IP network, particularly at the time scale of sub-second level; 2) providing an exciting new “playground” and neural network model for the DL community to solve real-world-motivated research challenges by deeply combining its structure and working mechanisms.
Next, we make the following two clariﬁcations.
Why not using traditional mathematical models.
The past decades have seen numerous trafﬁc models proposed to mathematically model the trafﬁc characteristics of networks (Gebali, 2015).
For example, extensive studies use the Poisson model to characterize the trafﬁc by assuming the arrival pattern between two successive packets follows Poisson process.
Considering the heavy tailed distribution and burstiness of the data-center trafﬁc, recent work in Benson et al.
(2010) models the trafﬁc arrival pattern as a log-normal process.
To capture the temporal patterns and make predictions accordingly, Seasonal Autoregressive Integrated Moving Average (SARIMA) is exploited in (Ergenc & Ertan, 2019) to model the trafﬁc time series.
These analytical models may generate outputs that are easier to interpret, but are bonded to the speciﬁc working circumstance and assumptions.
More importantly, these statistical models function at coarse time scales of hours and assume relatively smoother trafﬁc patterns.
However, as reported in many practical trafﬁc measurements in e.g. Benson et al.
; Greenberg et al.
, most ﬂows last less than 1 minute.
This implicates tasks requiring trafﬁc models at ﬁner-grained time scales are beyond the capability of these traditional models.
Fig.
2 plots the trafﬁc traces we collected from a practical backbone network–WIDE 1 , which shows the realistic trafﬁc patterns when the packet ﬂows are sampled by two different time scales.
The long time-scale plot in Fig.
2b shows clear a “tide-effect” associated with daily human activities.
By contrast, the trafﬁc traces in Fig.
2a get more noisy and difﬁcult to recognize obvious patterns when they are counted by millisecond.
[Figure]
Why not using existing neural network models.
When put in the context of data-driven learning, the trafﬁc ﬂow modelling problem can be reduced to the representation learning task.
If treating the trafﬁc ﬂows as the general spatio-temporal data, extensive existing neural networks ﬁt such task, including Convolutional Neural Net (CNN, (Mozo et al., 2018)), Graph Neural Net (GNN, (Rusek et al., 2019)), Recurrent Neural Net (RNN) as well as their variants and combinations (e.g., STHGCN (Kalander et al., 2020), STGCN (Yu et al., 2018), and Xiao et al.
(2018); Polson & Sokolov (2017);
Cui et al.
Guo et al.
; Lin et al.
The customized designs to the data-speciﬁc properties make the success of these existing models, such as the convolutional operation to capture the spatially local correlations in CNN and the aggregation operation to extract the adjacent link correlations in GNN, and so on.
As a human-engineered industrial system with clear system structure and working mechanism, the IP network creates domain-speciﬁc spatio-temporal data correlations, which are difﬁcult to capture for the incumbent spatio-temporal models if without any modiﬁcation.
One of the most important difference is that the spatial data in IP networks is not only correlated to other spatial data at the same point in time, but also able to directly inﬂuence the future realizations of correlated locations with strict order (i.e., the Spatio-Temporal Induction effect as we will disclose later).
Moreover, these existing studies only target at a coarse-grained timescale above minutes or even hours.
Models at a sub-second granularity, as FlowNN functions, require deeply combining the spatio-temporal data trends, as well as the system structural knowledge and working mechanism.
1. O UR C ONTRIBUTIONS
We claim two critical contributions: 1) we formulate the crucial trafﬁc ﬂow modelling problem in
IP networks as the representation learning task in deep learning, and develop a customized neural network– FlowNN and the associated Induction Operation to extract the domain-speciﬁc spatiotemporal data correlations in IP trafﬁc ﬂows.
To the best of our knowledge, this is the ﬁrst work to design customized neural network and learning strategy by deeply combining the IP network structure and working mechanism.
The Induction Operation also makes it the ﬁrst time for a data-driven learning model able to infer the data features at a millisecond granularity, which is usually treated as the ‘ noise ’ region by existing coarse-grained models; 2) we report the state-of-the-art performance over the baselines in different type of practical network applications, which provides a good testament of our model.
S PATIO -T EMPORAL I NDUCTION
By stacking the networking feature timeseries sampled at all the nodes that a ﬂow passes through, the IP trafﬁc ﬂow data can be organized in the form of high-dimensional tensor time series, as shown in Fig. 3a.
The feature (denoted as x tf,n ) could be the average ﬂow rate (i.e., the amount of packet bits received in each unit measurement time) at each node or the average per-hop packet delay etc.
The routing path herein constitutes the most signiﬁcant attribute for the generative process of each
IP trafﬁc ﬂow.
This creates many peculiar properties in such ﬂow data.
For example, for a ﬂow with a routing path [1 → 4 → 12] in Fig.
3a, the current data at node 4 was originated from the history data at its predecessor node 1, but delayed by at least 2 the link delay ∆ t .
These data will also ﬂow to its successor node after certain delays.
This shows that the ﬂow state at a node is physically 2 Packet processing and queueing will also impose extra delay. [Figure]
𝑓
[Figure]
driven 3 by the past ﬂow state at its predecessor node.
Therefore, such time-resolved ﬂow data not only tells us who is related to whom but also when and in which order relations occur.
This forms the S-shaped data correlation pattern, as exempliﬁed in Fig. 3a.
An accurate modelling of the ﬂow data requires attention to such domain-speciﬁc data properties, which are absent in the majority of existing learning models if not all of them.
Fig.
3b plots a sample of ﬂow traces at two neighboring path nodes from the WIDE dataset.
We can observe that an exceeding of data rate at node 1 over node 2 for some duration (e.g., T 1 ) will always induce a subsequent duration T 2 when the data rate at node 2 is greater than that at node 1.
Moreover, the cumulative amount of data bits the two nodes forward in such two durations are almost same, as indicated by the rate difference between the two nodes at the bottom of Fig.
3b.
This illustrates that there is a stronger correlation among the data in these two durations, and the future realizations at T 2 are subject to the constraint of the states at T 1 by local ﬂow conservationbetween the two nodes.
In analogy to the concept of Electromagnetic Induction 5 in Physics, in what follows, we introduce the Spatio-Temporal Induction (STI) effect in IP trafﬁc ﬂows.
Accordingly, a learning model is proposed to model the network trafﬁc ﬂows at a ﬁne-grained timescale.
Deﬁnition 1.
Spatio-Temporal Induction is the production of the temporal evolutions of a ﬂow from the history spatial patterns at its correlated locations.
The STI builds a physical relationship between the spatial and temporal patterns of IP ﬂow data.
This provides a more accurate interpretation to the underlying data generating process than the trending information manifested by the data itself.
Such induction effect is created by the IP network structure and working mechanism, which is preserved when the ﬂow data is sampled at any timescale in practice.
Next, we develop an Induction Operation to concretely capture the S-shaped data correlation in IP ﬂow data and propose what we call FlowNN to generate the desired trafﬁc model for IP networks.
F LOW N EURAL N ETWORK
As aforementioned, after the packet ﬂows leave the source devices, they may experience delays from many factors (e.g., link transmission, packet processing, queueing etc) during the journey throughout the routing path.
Accordingly, each node in the path will record a transformed time-series view of the same source ﬂow due to the transmission and forwarding actions from the network.
This 𝑥 𝑓,1𝑡 𝑥 𝑓,2𝑡 ⋮𝑥 𝑓,𝑛𝑡 𝑡=0,1,⋯𝑓=1,2,⋯,𝑁 𝑏
[Figure]
Node
[Figure]
⋮
Node 𝑡 𝑛
Path-level Coding ⋮
Induction layer
Induction layer
Node-level Induction ⋮ ⋮ ⋮
Input
I n iti a l e m b e dd
i
ng l a y e r
Flow ⋮ 𝑡 𝑁 𝑏 ⋮
R ea dou t L a y e r
Support downstream applications
[Figure]
implicates the existence of similarity among these time series since they are from the same source ﬂow.
Such path-wide similarity reﬂects the shared forwarding policies all path nodes follow, as well as the original behaviors of the source ﬂows at the sending side, such as the dynamics of user demands.
On the other hand, the ﬂow patterns recorded at each node may end up being different from the patterns at the sending side, depending on the experienced networking actions.
With all these in mind, we attend to such similarities and differences by two key blocks– Path-level Coding and Node-level Induction , and build the framework of FlowNN as Fig. 4.
Path-level Coding:
This block constructs an encoding network to extract the patterns that can represent the state of the whole path.
Such path-level patterns capture the similar part of behaviors within the time series received from all path nodes and will be the base to derive the evolved patterns at each path node.
Technically, we tested both the very recent Siamese net (Maron et al., 2020; Ye et al., 2019) and GNN based encoders (Rusek et al., 2019) to extract the path-level patterns.
Surprisingly, they are not outperforming a direct encoding by the Gated Recurrent Unit (GRU) with all raw node data as inputs.
This stands to the reason that the raw data from the correlated locations conveys the direct guidance for the near future realization at a node.
The operations in above models, e.g., SumPooling or node aggregation, actually destroy such data correlations and introduce more ‘noise’.
More details are provided in later experiments.
Consequently, in the following, we directly apply a GRU to encode all the raw inputs.
Node-level Induction: This block encodes the individual patterns at different nodes conditioned on the path-level patterns from the source ﬂows.
Looking into how ﬂows are induced in Fig.
3b, we can observe an explicit data correlations subject to local ﬂow conservation among the correlated data sequences.
Next, we propose the Induction Operation and the associated Contrastive Induction
Learning to extract such data correlations.
As illustrated in Fig.
3b, the induction process operates on any two consecutive durations like Tand T 2 , which can be identiﬁed through the rate differences between two neighboring nodes.
The induction is performed separately for each path node, as shown in Fig.
For ease of exposition, we denote the data sequences at all path nodes in T 1 and the data sequence at the induced node in T 2 as
S and T , respectively.
Then, the Induction Operation performs the conditional induction function f
( T |S ) to capture the correlations between source sequence S and target sequence T .
The function f ( T |S ) can be approximated by a specialized Contrastive Induction Learning in a self-supervised manner as follows.
Contrastive Induction Learning: As discussed in Path-level Coding , we ﬁrst take the outputs of path-level feature patterns, { h t S } t ∈ T 1 , as the initial coding states of source sequence S .
A recurrent encoder (e.g., GRU 6 (Chung et al., 2014)) is then applied to generate the context c of S with { h t S } t ∈ T 1 as inputs.
Conditioned on the context c , a GRU decoder can be then used to produce the state codes, { ˆ h t T } t ∈ T 2 , for the target sequence T .
With the state codes of both S and T at hand, the key of the induction function f ( T |S ) is to force the learned state codes of S and T to express the data correlations subject to local ﬂow conservation.
Inspired by Contrastive Predictive Coding (CPC) (Oord et al., 2019), we learn the mappings from S to T in a way that maximally preserves the mutual information of the source and target sequences deﬁned as follows:
[Equation]
where p ( · ) is the probability function.
In analogy to CPC, we rely on a contrastive loss function to train the model.
As the practice of contrastive learning (Chen et al., 2020; Oord et al., 2019), we ﬁrst construct a training batch of
N b sequence samples, Φ = {T (cid:63) 1 , T 2 , · · · , T N b } .
T (cid:63) 1 is the positive sample from the conditional distribution p
( T | c ) for the target sequence T and other N b − 1 sequences are the negative samples from the original distribution p ( T ) .
Accordingly, we deﬁne the following contrastive induction loss
L I to optimize the mutual information in Equation 1 as follows:
[Equation]
where f ( · ) is a score function proportional to the ratio p ( T | c )
/p ( T ) .
As the way done in CPC, this can be technically implemented by using a simple log-bilinear model:
[Equation]
where trans [ h t T ] is the transpose of the state coding h t T from the initial embedding layer in Fig.
The induction loss L I deﬁnes a categorical cross-entropy of classifying the positive sample correctly.
Optimizing L I will result in high similarity between the true embedding codes h t T (cid:63) 1 and the induced state codes ˆ h t T (cid:63) 1 .
The above induction pattern is learned by directly comparing the target coding with the codings of randomly sampled negative samples in latent space in a self-supervised manner.
Such process requires no direct labels in the output space, which makes it easier to generalize the induced knowledge to diverse applications in IP networks.
E XPERIMENTS
We validate the efﬁciencies of FlowNN on two crucial networking application tasks in IP networks– ﬂow rate prediction and end-to-end delay inference.
Datasets.
We conduct experiments on two publicly available ﬂow datasets–WIDE and NumFabric 7 .
WIDE are the realistic trafﬁc traces collected from real-world network environment, and NumFabric are the synthetic trafﬁc widely applied in recent networking researches (Nagaraj et al., 2016;
Lei et al., 2019).
The network topology includes 4 core nodes, 8 leaf nodes and 8 × 16 hosts organized in accordance with Fat-Tree network architecture (Al-Fares et al., 2008).
We sampled the ﬂow rates of 50 ﬂows as well as the associated aggregated ﬂow rates at each nodes at the time scale of 1 ms for total length 18432 ms .
Each ﬂow traverses a routing path with 5 nodes.
We found that the ﬂow tensor time series of such length are enough to learn a stable ﬂow pattern at a sub-second granularity.
Baselines.
We compare the proposed FlowNN with the following baselines: 1) Seasonal AutoRegression Integrated Moving Average (SARIMA) (Hyndman & Khandakar, 2008; Wang et al., 2006);
2) GRU (Chung et al., 2014); 3) multiple GRUs (multiGRU); 4) STHGCN (Kalander et al., 2020), and 5) STGCN (Yu et al., 2018).
Particularly, GRU encodes and predicts each time-series sequence stand-alone without any reference to the information from other spatial nodes.
In contrast, multiGRU uses separate GRUs to encode each time-series sequence but predicts with Fullyconnected Neural Network (FNN) by jointly concatenating the codings of sequences from all spatial nodes.
STHGCN is the recent work for networking trafﬁc analysis, which uses graph convolutional
[Figure]
network (Kipf & Welling, 2016) and GRU to predict the spatial states of all nodes at each time.
Finally, STGCN is a widely applied model for transportation trafﬁc analysis, which is build upon the spatio-temporal graph convolution.
Implementation Details.
We use Adam (Kingma & Ba, 2014) with a learning rate of 1 e − 4 .
All hidden dimensions are set to 128 and the layer size for GRUs is 3.
We train on sampled time series window of length 256.
A batch of N b = 10 is used to draw the samples for the contrastive induction loss in Equation 2.
The initial embedding layer in FlowNN is performed for each node data by separated FNNs.
We ﬁrst pre-train the FlowNN with the contrastive induction loss until convergence and then ﬁne-tune the model with MSE loss (Equation 4) for different application tasks.
Evaluation metrics.
The following metrics are adopted for evaluating quality of prediction ˆ y with the ground truth y .
[Equation]
squared
[Equation] [Equation]
Relative absolute error:
[Equation]
Correlation coefﬁcient:
[Equation]
(6) where ¯ y and ˆ¯ y are the mean values of y and ˆ y
. 4. P ATH -W IDE V IEWS M ATTER FOR IP T RAFFIC F LOW M ODELLING !
We ﬁrst use the one-step-ahead prediction task to demonstrate that path-wide views of FlowNN are more informative than the widely used single-node view or graph-level views in the literature.
Fig.
5a shows the validation MSE loss across training epochs for the one-step-ahead prediction task.
Naive solution directly uses the previous observation as the next-step prediction.
The results of Naive and SARIMA provide a good testament to the prediction difﬁculty for traditional nonDL solutions in a millisecond-granularity networking prediction task.
We can see that the loss of
SARIMA is still as high as 80% of the Naive solution.
However, the losses of all DL based solutions are less than 20% of Naive .
This shows the powerful advantages of the deep learning principle in difﬁcult tasks.
Table 1 shows the test performances of different methods.
Speciﬁcally, GRU holds a single node view and learns the time series of each node separately and the achieved loss is only 22.5% of SARIMA.
By contrast, the other DL solutions harvest the information from all path nodes to predict (i.e., the path-wide views) and the loss are remarkably reduced further against GRU.
As discussed in Path-level Coding , STGCN and STHGCN inherit the graph aggregation operation,
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[Figure]
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[Figure]
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[Figure]
T e s t C
o
rr
FlowNN
STGCNSTHGCNmultiGRUGRU (c) Test Corr.
their performances are better than GRU, but are inferior to multiGRU.
Such ﬁndings discourage the spatial aggregation operation in GCN as the data correlation exploited by GCN deviates a lot from the truly S-shaped correlation in IP ﬂow data.
Finally, the proposed FlowNN outperforms all the baselines.
Fig.
5b compares the predictions by different models against the ground truth.
We can observe that
FlowNN and multiGRU capture the trend of ground truth accurately.
Beneﬁting from the Induction
Operation , FlowNN avoids over-reliance on the far distant history as the vanilla GRU and its variants function and shows the fastest response to the dynamic changes among the trafﬁc ﬂows.
4. W IDE APPLICABILITY ON DIFFERENT APPLICATION TASKS
Multi-step-ahead predictions : We ﬁrst exam the effectiveness of FlowNN on the multi-step-ahead prediction task.
In this task, we ﬁrst pretrain FlowNN based on the contrastive induction loss in
Equation 2 until convergence.
Following the practice of the recent work for the short-term (secondlevel granularity) trafﬁc forecasting in (Mozo et al., 2018), we then take the output of pre-trained
FlowNN and ﬁnetune a FNN based Readout layer in Fig.
4 to infer the mean of multi-step-ahead predictions.
The mean realizations of the multi-step ahead are crucial to many short-term networking planning and control tasks in IP networks.
Fig.
6 compares the test performances of different DL models on the NumFabric dataset.
The test results shows that FlowNN outperforms all these recent baselines on all metrics.
End-to-end delay inference : In today’s IP network, it is important to guarantee that the provided
QoS meets the subscribed service agreement, such as end-to-end transmission delay less than 50ms for online gaming.
However, it is difﬁcult to build an accurate delay model for human experts (Xiao et al., 2018;
Rusek et al., 2019) in practical networks since many factors may inﬂuence the delay, including the dynamic trafﬁc demands, packet processing and queueing etc.
In this task, we apply the learned trafﬁc ﬂow model from FlowNN to perform the trafﬁc-driven delay inference.
Speciﬁcally, we apply the same FlowNN model pretrained as in multi-step-ahead prediction task, and ﬁnetune a new FNN based Readout layer to infer the next-step delay.
Table 2 shows the test performances of different models.
Although pretrained on the dataset of trafﬁc ﬂow rates, FlowNN still achieves the best results on the task of inferring the data with different physical meaning.
This shows the robustness of FlowNN across different tasks.
[Table] [Table]
G ENERALITY T EST ON O UT -O F -D ISTRIBUTION D ATASET
In this subsection
[Table]
we test the model generality when a FlowNN model, pretrained with the contrastive induction loss on one dataset, say NumFabric
[Table]
is used to work in an environment that is different from the trained one.
This is performed by testing with the one-step-ahead prediction task on Out-Of-Distribution dataset (i.e., cross dataset test).
Speciﬁcally, we take the FlowNN model pretrained on the NumFabric dataset and ﬁnetune the FNN based Readout layer to test its prediction performances on the WIDE dataset.
As comparison, we also ﬁnetune the Readout layers of multiGRU and STHGCN already trained on NumFabric to test their performances on WIDE.
From the test results shown in Table 3, we can observe that FlowNN achieves the best performances except that RAE is slightly higher.
Moreover, the performances of FlowNN achieved in cross dataset test is even better than the results of other baselines achieved in same dataset test (except multiGRU).
This shows the good generality and robustness of FlowNN .
Discussions : We observe that multiGRU, to certain extent, also works well in above experiments, although is inferior to FlowNN .
This can be interpreted by the shortcut learning, as disclosed in (Geirhos et al., 2020).
As illustrated by the data correlation in Section 2, the near future realization of a node is highly correlated to the path-wide values at last step.
This makes a shortcut for multiGRU that a prediction by directly weighting the last-step path-wide value will capture such kind of data correlation, although this can not further extract the effect of local ﬂow conservation along the routing path as FlowNN functions.
C ONCLUSION
In this paper, we formulated the crucial trafﬁc ﬂow modelling problem in IP networks, and develop a customized neural network– FlowNN and the associated Induction Operation to extract the domain-speciﬁc spatio-temporal data correlations in IP trafﬁc ﬂows.
This study makes the pioneering work to design customized neural network and learning strategy by deeply combining the IP network structure and working mechanism.
We reported the state-of-the-art performances for multiple practical networking application tasks, which demonstrates the strength of our approach.
As a new ‘playground’ for both networking and deep learning communities, the research of network intelligence is still in its infancy.
We hope this work will inspire more innovations in this ﬁeld in future.