Deep-Q: Traffic-driven QoS Inference using Deep Generative Network

Transcription

1 Deep-Q: Traffic-driven QoS Inference using Deep Generative Network Shihan Xiao, Dongdong He, Zhibo Gong Network Technology Lab, Huawei Technologies Co., Ltd., Beijing, China 1

2 Background What is a QoS Model? Traffic QoS Model Delay, jitter, packet loss Network

3 Background Why is it important? Online QoS Monitoring SLA guarantee & anomaly detection Delay Monitoring Path A Path B A QoS model helps reduce most of the cost! Path C Monitor Require high cost on real-time active QoS measurements!

4 Background Why is it important? Online QoS Monitoring SLA guarantee & anomaly detection Delay Monitoring Path A Path B Path C Monitor Offline Traffic Analysis Delay Inference Path A Path B Path C Traffic trace Inference + Network A QoS model can do QoS inference without QoS measurements

5 Background Why is it important? Online QoS Monitoring SLA guarantee & anomaly detection Delay Monitoring Path A Path B Path C Monitor Offline Traffic Analysis Delay Inference Path A Path B Path C Traffic trace Inference + Network Path A Path C What if Analysis Delay Prediction Predict How QoS will change if a flow switches from Path A to C?

6 Traditional Methods 1. Network simulator Traffic NS2, NS3, OMNeT++ Network Simulator Delay, jitter, packet loss Network Slow and Inaccurate 6

7 Traditional Methods 2. Mathematical modeling Traffic Simplified assumptions Queuing Theory Delay, jitter, packet loss Network Large human-analysis cost & Inaccurate 7

8 Traditional Methods 2. Mathematical modeling Traffic Simplified assumptions Queuing Theory Delay, jitter, packet loss Network Large human-analysis cost & Inaccurate A fast, accurate & low-cost QoS model is helpful! 8

9 Key Observations Observation 1: Traffic load per link is much easier to collect & wellsupported by existing tools (e.g., SNMP) than QoS values per path 9

10 Node index Key Observations Observation 1: Traffic load per link is much easier to collect & wellsupported by existing tools (e.g., SNMP) than QoS values per path Observation 2: Traffic load is the key factor of QoS changes Traffic: collected link load matrixes Delay, jitter, packet loss QoS Model Node index 10

11 Key Observations Observation 3: Different traffic loads lead to different QoS distributions 40 traffic loads (per 20 min) Testbed measurement Measured delay samples 11

12 Key Observations Target Problem: Given a set of traffic load matrixes during time T, what are the distributions of QoS values (delay, jitter, loss...) of each network path during T? Different traffic loads lead to different QoS distributions 12

13 Solution of Deep-Q Why deep learning helps? Low human-analysis cost Fast inference Data-driven VS. Human-engineered model Delay model Loss model Packets Running time of Hours! Network Simulator QoS values Auto Training Delay/Jitter/Loss Running time of Milliseconds! Traffic load matrix QoS values 13

14 Probability Key Technology: Deep Generative Network State-of-the-art DGNs in deep learning Image domain GAN(Generative Adversarial Network) & VAE(Variational Autoencoder) So what is the difference? Network domain Input: this small bird has a pink breast and crown, and black primaries and secondaries infer Input: number 2 infer Input: traffic load matrixes infer Source: ICML2016, Generative Adversarial Text to Image Synthesis (Conditional) GAN Example Source: NIPS2014, Semi-supervised Learning with Deep Generative Models (Conditional) VAE Example Deep-Q Delay (us) 14

15 Key Technology: Deep Generative Network Differences Application: text label to images Image domain (GAN & VAE) Network domain (Deep-Q) Application: traffic load matrixes to QoS values Input Output Input Output Discrete Label Image samples Traffic statistics QoS values Discrete & Low/high Dimensional Discrete & High Dimensional Continuous & High Dimensional Continuous & Low Dimensional Target: the generated image samples satisfy real image distribution and match the label class Target: the generated QoS values satisfy real QoS distribution and match the traffic statistics Deep-Q requires a high accuracy on the output distribution, but GAN & VAE do not apply! 15

16 Deep-Q Solution 1. Handle the continuous high-dimensional input Extract traffic features from a sequence of high-dimensional traffic load matrixes LSTM (Long Short Term Memory) module: a state-of-the-art deep learning method to learn features from a data sequence Micro-load matrixes during time t M t M t M n t M t LSTM Cell Hidden State LSTM Cell Hidden State LSTM Cell Hidden State LSTM Cell Traffic features 16

17 Cumulative Probability Deep-Q Solution 2. Handle the continuous low-dimensional output Challenge: high accuracy is required for QoS distribution inference Solution: a new metric Cinfer loss to accurately quantify the QoS distribution error X: Inferred QoS distribution Y: Target QoS distribution CDF (Cumulative Distribution Function) CDF curve of X CDF curve of Y Height Difference Delay (ms) 17

18 Deep-Q Solution Deep-Q: A stable & accurate inference engine Built upon VAE (Stable) and augmented with Cinfer Loss (Accurate) A simple example of learning ability: Target distribution Inferred distribution VAE: Stable but Inaccurate GAN: More accurate but unstable Deep-Q: Stable & Accurate L2 Loss of VAE KL Loss of GAN Cinfer Loss of Deep-Q 18

19 Cumulative Probability Cumulative Probability Deep-Q Solution Cinfer-Loss computation for training The exact computation is NP-hard The approximation must be fully differentiable to compute gradients for training Step 1: Discretization From integral to a discrete sum of bins Discretization Delay (ms) Delay (ms) 19

20 Cumulative Probability Deep-Q Solution Cinfer-Loss computation for training The exact computation is NP-hard The approximation must be fully differentiable to compute gradients for training Step 2: Bin Height Computation required to be differentiable An intuitive method: Calculate the located bin index of each sample & Count the sample number per bin Ceil function is non-differentiable & difficult to approximate! Delay (ms) 20

21 Cumulative Probability Deep-Q Solution Cinfer-Loss computation for training The exact computation is NP-hard The approximation must be fully differentiable to compute gradients for training Step 2: Bin Height Computation required to be differentiable A differentiable method with some math tricks (borrowed from deep learning) Step 1): Use Sign function Step 2): Approximate Sign function with tanh Delay (ms) Approximation error< 10 5 in experiments 21

22 Deep-Q Solution Put it all together Sampling from N(0,1) Network QoS (delay,jitter, loss) VAE Encoder Z VAE Decoder Network QoS (delay,jitter, loss) X Traffic load matrix along time X Space-time Traffic Features Inference phase of Deep-Q Training phase of Deep-Q Automatic feature engineering & QoS modeling:end-to-end training using Cinfer Loss Collect traffic data Underlay network 22

23 Experiment Setup Testbed Topology Experiment topology of data center network Experiment topology of overlay IP network NEU200 Probe NEU200 Probe CPE CPE NEU200 Probe NEU200 Probe Traffic traces: WIDE backbone network [1] Training set: 24 hours of traffic traces on April 12, 2017 Test set: 24 hours of traffic traces on April 13, 2017 r0 AS-1 r1 Internet r2 AS-2 r3 Neural network: TensorFlow implementation with 2 hidden layers r4 [1] Traffic traces are public available at 23

24 Experiment Results Delay Inference in Datacenter Topology Traffic Real delay Distribution error of inference Mean error of inference 90-percentile error of inference 99-percentile error of inference Queuing theory Deep learning 1. Deep learning methods achieve on average 3x higher accuracy over Queuing theory 2. Deep-Q achieves the lowest errors and most stable performance over all cases 24

25 Experiment Results Packet Loss Inference in Overlay IP Topology Queuing theory Deep learning 1. Deep learning methods achieve on average 3x higher accuracy over Queuing theory 2. Deep-Q achieves the lowest errors and most stable performance over all cases Deep-Q inference speed < 10ms for network scale < 200 nodes 25

26 Conclusion Deep-Q: an accurate, fast and low-cost QoS inference engine Automation: LSTM module for auto traffic feature extraction High stability: an extended VAE inference structure with the encoder and decoder High accuracy: a new metric Cinfer loss to accurately quantify the QoS distribution error Future vision: Learn device-level QoS models (routers/switches) scalable network-level QoS models Learn high-level application QoE from traffic traces 26

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