Modeling Web Quality-of-Experience on Cellular Networks

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1 Modeling Web Quality-of-Experience on Cellular Networks Athula Balachandran, Vaneet Aggarwal, Emir Halepovic, Jeffrey Pang, Srinivasan Seshan, Shobha Venkataraman, He Yan Carnegie Mellon University, AT&T Labs Research MobiCom 2014 Presented by Nawanol Theera-Ampornpunt 11 February, 2015

2 Motivation Cellular network characteristics affect users Quality-of- Experience (QoE) Signal strength Handovers Load of cell tower Network operators want to optimize network for QoE They cannot directly measure QoE Need to rely on model of relationship between network characteristics and QoE Goal: Model QoE metrics from network traces Application: web browsing 2 / 25

3 Uses of the Model Determine when network degradation actually affects user experience Give operators information about trade-offs among potential solutions Troubleshooting a problem Tweaking a network element Expanding the network 3 / 25

4 Previous Work Relies on client-side or server-side instrumentation Discovers how web QoE is affected by website designs web browsers network protocols This paper takes a cellular operator view of web QoE No detailed client-side or server-side logs Estimate QoE metrics using only network measurements 4 / 25

5 Contributions Develops a technique to reconstruct mobile web sessions and user clicks from HTTP traces Quantifies individual impact of network characteristics on mobile web QoE Develops machine-learning models for predicting web QoE from radio network characteristics 5 / 25

6 Data Sources Radio statistics RSSI (received signal strength indicator) Handovers End-to-end throughput Latency HTTP flows HTTP headers TCP flow duration, flags Anonymized device identifier Location: a major metropolitan area in western U.S. Duration: one month in 2012 All data sets are anonymized 6 / 25

7 Websites Analyzed Analysis focuses on three leading mobile websites in top 100 News Social Wiki HTTP trace contains (only visits to 3 websites above) 2 million web sessions 70 million HTTP requests 1 million unique devices Radio trace contains complete information about 100,000 of the HTTP sessions 7 / 25

8 QoE Metrics User engagement identified as key measure Session length Number of pages a user clicks through Abandonment rate Percentage of users who leave the website after visiting the landing page Both require identification of user clicks 8 / 25

9 Detecting Clicks - Baseline Common approach: use idle time between requests Requests for embedded objects are generated by browser Requests generated by clicks require user intervention Gives poor accuracy (~20% error) 9 / 25

10 Detecting Clicks - Approach Most embedded objects are hosted by third party Advertising agency Content Distribution Networks (CDNs) Analytics services Classify requests based on URLs Models trained separately for each website 10 / 25

11 Detecting Clicks Steps Group sessions based on Referrer header and IMEI hash 2. Extract features bag of words from domain name bag of words from URN type of content Example: Domain = <blog, xyz, com> URN =<my, blog, abc.html> Type = html 11 / 25

12 Detecting Clicks Steps Label data points Only include requests in the first 10 seconds of each session First request from a click Other requests for embedded objects 4. Running classification algorithm Naïve Bayes performs best 12 / 25

13 Detecting Clicks Results Feature Simple = Domain only Feature Diverse = All features Stream Structure = Previous work 13 / 25

14 QoE Metrics Revisited Session length and abandonment rate are also influenced by user interest Many web sessions are one click Not helpful in distinguishing satisfied and dissatisfied users 14 / 25

15 QoE Metrics Alternative Partial download ratio proposed as alternative Fraction of HTTP objects not completely downloaded Correlates well with session length 15 / 25

16 Network Factors Load Higher network load results in worse QoE QoE can be improved by Adding more cells Distributing users across cells to balance load 16 / 25

17 Network Factors RSSI Higher signal strength does not correlate with QoE 17 / 25

18 Network Factors ECNO Higher signal energy to inference (ECNO) correlates with better QoE ECNO is a better indicator of channel quality than RSSI RSSI includes power of noise and interference QoE is interference and noise limited, not power (i.e., coverage) limited 18 / 25

19 Network Factors Handovers Inter-radio-access-technology (IRAT) handovers have strongest impact on QoE Impacts of other handovers and failure events on QoE are negligible 19 / 25

20 Network Factors Data Rate Higher radio data rate does not lead to better QoE It has been shown that web browsing traffic is more latencylimited than throughput-limited 20 / 25

21 Modeling Web QoE Goal: Predict web QoE metrics based on network factors alone 1. Partial download ratio 2. Session length 3. Whether session includes partially downloaded pages 4. Whether user will abandon a session Different algorithms evaluated using 10-fold crossvalidation Linear regression works best for metrics 1 and 2 Decision tree works best for metrics 3 and 4 21 / 25

22 Results Partial Download Ratio Accuracy measured in root mean squared error (RMSE) Baseline: Always predict the mean Linear regression produces 20% lower RMSE than baseline 22 / 25

23 Results Session Length Linear regression produces 10% lower RMSE than baseline Session length is affected more by external factor (e.g., user interest) than partial download ratio 23 / 25

24 Results Binary Predictions Partial Whether session includes partially downloaded pages Abandonment Whether user will abandon a session Baseline: Always predict the majority class 24 / 25

25 Insights Linear regression coefficients for predicting partial download ratio Coefficients are relatively constant across datasets Similar conclusions for session length Inspecting individual decision trees confirms impact of network factors analyzed earlier 25 / 25

26 Questions?

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