1. A Spectral Approach for Large Scale Data Traffic Load: Analysis and Application HONGZHI SHI, YONG LI (TSINGHUA UNIVERSITY) DI WU (HUNAN UNIVERSITY) YING ZHANG (HP LABS) IEEE SECON 2016 1

2. Background  Tremendous Growth of Mobile data. 2 Mobile dataHuman behavior

3. Motivation  Find patterns of data traffic.  Develop better algorithm.  Understand human behavior. 3

4. Contributions  Faster Algorithm  Provide Insights  Application of our model 4

5. Take a quick look at our work… 5 5 Findings four main clusters of the traffic patterns Application Generate a traffic model Data 3G/LTE data traffic 9,213 base stations one month Tools a spectral model clustering method

6. Data 6 6 Findings four main clusters of the traffic patterns Application Generate a traffic model Data 3G/LTE data traffic 9,213 base stations one month Tools a spectral model clustering method

7. Data: basic information 7  The 3G/LTE traffic load of each BS in Shanghai with the time granularity of 10 minutes for the whole 31 days.  Place: Shanghai; Number of base stations: 9213  Time granularity :10 minutes; Duration: the whole 31 days From August 1 to August 31, 2014 9600 BSs 150,000 users Large-scaleLong duration Fine-grained Start and end time accurate to second

8. Different area 8 Different time

9. Problem: Does the traffic pattern exist among thousands of cellular towers? 9 DifferentSimilar

10. Tools 10 Findings four main clusters of the traffic patterns Application Generate a traffic model Data 3G/LTE data traffic 9,213 base stations one month Tools a spectral model clustering method

11. How many traffic patterns do we have ? What do they look like ? 11

12. Periodicity in different scales 12

13. Discrete Fourier Transform (DFT) 13 Periodicity Week Day Half Day Day Week

14. Reconstruct the traffic time series 14 Three spectrum components: week day Half day We can use them as features to cluster. DFT

15. Clustering Algorithm 15  Process includes: DFT Normalization K-means ……  Input: Cell towers number S, Clustering number M, Traffic data in 28 days: Di = Xi[k], for i = 1, 2, 3…S  Output: Labels Li, for i = 1; 2; 3…S

16. How to define the labels ?  Location Longitude and Latitude of BS.  Points of Interests (POI) POI is a specific point location of a certain function such as restaurant and shopping mall. 16

17. Advantages of the Algorithm (Compared with clustering directly by the time domain traffic profile)  Faster: only three features.  Robust: spectrums we pick do not change.  Tolerance: less outliers. 17

18. Findings 18 Findings four main clusters of the traffic patterns Application Generate a traffic model Data 3G/LTE data traffic 9,213 base stations one month Tools a spectral model clustering method

19. Results: Frequency domain 19 Five types of base stations

20. Results: Time domain 20 Figure: Reconstructed time-domain traffic using the three principal frequency components. High accuracy: energy lost less than 6%

21. 21 Observation  Resident: Mostly the surrounding areas of the city.  Office: Business district, etc.  Transport: Subway stations, bus stations, overpass  Entertainment: Shopping malls, entertainment plaza

22. Patterns: time domain & frequency domain 22

23. Application 23 Findings four main clusters of the traffic patterns Application Generate a traffic model Data 3G/LTE data traffic 9,213 base stations one month Tools a spectral model clustering method

24. Autoregressive Integrated Moving Average model (ARIMA) 24  We use the ARIMA model to generate the traffic of each type in the fourth week by the traffic in the former three weeks.

25. Our traffic model is more accuracy. 25

26. Let’s review our work… 26 Findings four main clusters of the traffic patterns Application Generate a traffic model Data 3G/LTE data traffic 9,213 base stations one month Tools a spectral model clustering method

27. Summarization 27  A powerful spectral approach  Five patterns of traffic data load  Application: traffic model

28. Thanks! 28