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Modeling Web Quality-of-Experience on Cellular Networks
Athula Balachandran, Vaneet Aggarwal,Emir Halepovic, Jeff Pang, Srinivasan Seshan,
Shobha Venkataraman, He Yan
AT&T Labs- Research CMU
Rise of Mobile
Cisco Visual Networking Index 2013: Global Mobile Traffic Data Update
Familiar?
Why is QoE important?Cellular
Network Factors
Quality of Experience
User (dis)satisfaction
Revenue
Why do we need a QoE model?• Service Quality Monitoring
• Trending• Alarming
• Better system designs and resource allocations schemes
Outline
Cellular Network Factors
1. What are the network factors?2. How to extract network factors?
Quality-of-Experience
3. What QoE metrics to use?4. How to extract QoE metrics?
5. Model the relationship.
Outline
Cellular Network Factors
1. What are the network factors?2. How to extract network factors?
Quality-of-Experience
3. What QoE metrics to use?4. How to extract QoE metrics?
5. Model the relationship.
Cellular Network Factors
Signal Strength
Handovers Failures
Cell load
Throughput
Outline
Cellular Network Factors1. What are the network factors?
2. How to extract network factors?
Quality-of-Experience
3. What QoE metrics to use?4. How to extract QoE metrics?
5. Model the relationship.
Collecting Network Characteristics
• Logs collected at Radio Network Controller– Intermittent: Signal strength, Throughput– Event based: Handovers, Failures
Outline
Cellular Network Factors
1. What are the network factors?2. How to extract network factors?
Quality-of-Experience
3. What QoE metrics to use?4. How to extract QoE metrics?
5. Model the relationship.
QoE Metrics
Session Length Abandonment
Partial Download Ratio (PDR)
Outline
Cellular Network Factors
1. What are the network characteristics?2. How to extract these metrics?
Quality-of-Experience
3. What metrics to use?4. How to extract these metrics?
5. Model the relationship.
Detecting Clicks
• Challenge: Classify embedded objects vs. click from network traces.
• Current Approaches: – Idle-time based– Stream Structure
• Our Approach: Text classification
Performance News, Social, Wiki
Precision is defined as the number of correct clicks identified divided by the total number of clicks identified Recall is de fined as the number of correct clicks identified divided by the total number of clicks.
Outline
Cellular Network Factors
1. What are the network characteristics?2. How to extract these metrics?
Quality-of-Experience
3. What metrics to use?4. How to extract these metrics?
5. Model the relationship.
Correlation Analysis
Signal StrengthHandovers
Failures
Cell load
Throughput
Session Length
Abandonment
Partial Download Ratio (PDR)
Correlation AnalysisCell load
Increasing Cell load leads to worse web QoE
Correlation AnalysisSignal Strength
RSSI: Received Signal Strenth
ECNO : How well a signal can be distinguished from the noise. Similar to SINR in WiFito signal to noise ratio
Web QoE is interference limited and not power limited.
Correlation AnalysisHandovers
Soft
IRATInter-frequency
IRAT handovers lead to worse QoE
Correlation AnalysisThroughput Failures
Web QoE is more latency-limited than throughput-limited
Downlink
Uplink
Correlation Analysis: Summary
• Cell load, IRAT handovers lead to worse QoE.• Improving ECNO leads to better QoE.• Higher RSSI worse QoE.• All other handovers, throughput, failures do
not have much impact on QoE.
Complex Inter-Dependencies
Signal Strength
Handovers
Failures
Cell load
Throughput
Unified Model
Machine Learning
Network Characteristics Web QoE metrics
QoE Model
Predictive Models
Model ML Algorithm Model RMSE
Estimate PDR Linear Regression 0.1709
Estimate Session length Linear Regression 1.703
Model ML Algorithm Model Accuracy
Predict Abandonment Decision Tree 69.12
Predict Partial Download Decision Tree 73.02
External Factors: Time of day
External Factors: Website
Unified Model
Machine Learning
Network Characteristics Web QoE metrics
QoE Model
External Factors
Predictive Models
Model ML Algorithm Old RMSE UpdatedRMSE
Estimate PDR Linear Regression 0.1709 0.087
Estimate Session length Linear Regression 1.703 1.401
Model ML Algorithm Old Accuracy
Updated Accuracy
Predict Abandonment Decision Tree 69.12 74.30
Predict Partial Download Decision Tree 73.02 83.95
Updated Model
Conclusions
• Web QoE $$• QoE metrics and network parameters– Session Length, Abandonment, PDR text classification– Network parameters RNC logs
• Network parameters impact web QoE– ECNO, Cell load, IRAT handovers
• Build accurate and intuitive models – Complex relationships ML algorithms– Incorporate external factors.
EXTRA SLIDES
Unified Model
Web QoE Model
Web QoE
Signal Strength Handovers
Failures
Cell load
Throughput
1) Estimate partial download ratio – Linear Regression 2) Estimate session length3) Predict partial download – Decision Tree4) Predict user abandonment
• Why is QoE important?• How to measure QoE?• How to improve QoE?• Why measure QoE?