Not All Apps Are Created Equal:

Transcription

1 Not All Apps Are Created Equal: Analysis of Spatiotemporal Heterogeneity in Nationwide Mobile Service Usage Cristina Marquez and Marco Gramaglia (Universidad Carlos III de Madrid); Marco Fiore (CNR-IEIIT); Albert Banchs (Universidad Carlos III de Madrid and Institute IMDEA Networks); Cezary Ziemlicki and Zbigniew Smoreda (Orange Labs) 1

2 INTRODUCTION Current status of mobile services: Superficial comprehension Restricted to a small set of coarse-grained datasets Properly dimension & orchestrate the mobile network Aim: characterize the usage of mobile services at a national scale given a large dataset Analysis of traffic behavior of services Across time & space Supporting data mining techniques Understanding social behaviors 2

3 DATASET Dataset collected at Orange core network 1 week from September 24, 2016 User population ~30 million individuals Distributed over > 550,000 km 2 Granularity of 5 mins Data recorded at passive probes at Gn and s5/s8 interfaces of GGSN & P-GW ~25,000 base stations (distributed over > 36,000 communes) ( ~ 16 km 2 each) We aggregated data per commune AIM: mobile service overview 3

4 DATASET: DEEP VIEW time commune service ul dl macro-category 500 distinct services service Description 1 YouTube WEB. Instagram. Web Advertising. Wikipedia 500 Shazam Extensive dataset! Selection of 20 main categories (most representative) High granularity! YouTube: YouTube WEB, YouTube Streaming HTTP, YouTube TLS, YouTube Streaming MP4,YouTube Apple 4

5 ANALYSIS 5

6 TIME SERIES ANALYSIS Focus on weekly demand for each traffic over communes: Each time series is characterized by a variety of fluctuation In all cases higher diurnal activity (activity reduced at night). Apple Store YouTube Distinctive dichotomy between weekends & weekdays Facebook SnapChat Different temporal patterns between categories & similar services 6

7 ARE THEY REALLY SIMILAR? All possible k considered! To be minimized To be maximized Downlink Uplink K-Shape Time Clustering: check goodness of fit with distinct quality indices vs the #clusters K - Davies-Bouldin (top graphs) Best option? 19 clusters - Dunn, Silhoutte (bottom graphs) NOT QUITE SIMILAR! 7

8 PEAKS DETECTED AppleStore Same macrocategory, different behavior 8

9 SERVICE USAGE GEOGRAPHY Significant peaks of activity also in space: Bytes/ subscriber Except 2 outliers It is used outdoors It is ubiquitous Twitter NetFlix Similar geographical pattern 9

10 DOES THE SPACE HAVE AN INFLUENCE IN TIME DYNAMICS? INSEE urbanization distribution 10

11 ARE TIME SERIES RELATED? Correlation of mobile services for different urbanization levels Each bar shows the average r 2 value. In all cases but TGV, the correlation is extremely high Depends on the train s schedule urbanization level has little impact on temporal dynamics of category usage. Service usage changes when people are aboard TGV. 11

12 SIMILAR USAGE IN TERMS BYTES/SUBSCRIBER? Slope of least square regression of per-subscriber time series Findings: Semi-urban & urban areas present similar individual service usage level Subscribers in rural areas consume around ½ of the mobile service data in urban areas Users on TGV generate on average twice or more volume of traffic than urban areas 12

13 CONCLUSIONS We studied temporal, spatial & hybrid dynamics of mobile services categorized granularity at a national scale finding new interesting macroscopic properties of traffic Findings: No 2 services exhibit similar time patterns Mobile services have very comparable geographical distributions The urbanization level influences how users consume mobile services, but limited on when they do so Unique time dynamics on high-speed trains 13

14 14 Cristina Marquez Cristina Márquez /Dec 13th, 2017/ Not All Apps Are Created Equal

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16 3G/4G NETWORK Data recorded at passive probes at the Gn and s5/s8 interfaces of GGSN & P-GW DPI techniques classify 88% of the mobile traffic Geo-referencing of the IP sessions by examining ULI (User Location Information) 16