PROTEUS: Network Performance Forecast for Real- Time, Interactive Mobile Applications Qiang Xu* Sanjeev Mehrotra# Z. Morley Mao* Jin Li# *University of Michigan #Microsoft Research

Real-time, interactive apps enrich mobile user experience Qiang Xu2 VoIP Video conferencing Online gaming Head Up Display (HUD) MobiSys’13

Bad condition Performance adaptation Forward error correction (FEC), de- jitter buffer, source coding rate Unpredictable condition Performance degradation Sensitivity to network performance Qiang Xu3MobiSys’13 Which category is cellular network performance?

Determining the predictability of cellular network performance in short term What performance metrics? What time granularity? How predictable? Leveraging network performance predictability in real- time, interactive applications How to efficiently predict? How to support applications? How much benefit? What problems does PROTEUS address? Qiang Xu4MobiSys’13

Hidden factors, e.g., on devices, in networks Using regression trees Treating hidden factors together as a blackbox Cost of learning predictability Passive monitoring, no active probing Application behavior is stable in short term Awareness to predictability Implementing PROTEUS library connecting regression trees and applications Challenges & solutions Qiang Xu5MobiSys’13

Predictability of cellular network performance Resource allocation at different network aggregations levels, e.g., base station, RNC, GGSN The predictability at time granularity of seconds is best suitable for real-time interactive applications A chunk for adaptive bitrate streaming is multi-second Qiang Xu6 Liu et al. MobiCom’08 Manweiler et al. MobiSys’11 Shafiq et al. SIGMETRICS’11 secondminutehourMobiSys’13

400+ one-hour packet traces Protocol: UDP TCP has congestion control and retransmission Device: Android, iPhone, USB dongle Windows Phone doesn’t have a packet sniffer Location: Ann Arbor (MI), Chicago (IL) Carrier: AT&T, Sprint, T-Mobile Verifying performance predictability Qiang Xu7MobiSys’13

Evidence of performance predictability Qiang XuMobiSys’138 The current throughput is highly correlated with the one 1s ago, but unlikely with 20s ago

Proportional fair scheduling: X vs. Y X: device with the best network condition Y: fairness among devices A device can occupy the same channel for ~1s The time slot for channel resource allocation is ~1.67ms The aggressiveness factor to favor the current device is Why predictable? Scheduling at base stations Qiang Xu9MobiSys’13

Using regression trees for prediction Exponential backoff to favor recent performance Short time window, e.g., 0.5s, for real-time requirement Small history window, e.g., 20s, for efficiency Qiang Xu10 time window history window MobiSys’13

No offline training, predicting in real-time Available after the first history window Comparing against two adaption solutions AD 1 : adapt to the current time window AD 2 : adapt to the averaged history window Running regression trees over traces Qiang Xu11MobiSys’13

Prediction accuracy for loss A false positive occurs if a loss is predicted but actually not FP: PROTEUS 1%, AD %, AD 2 >80%, ∀ linear 3-5%; FN: PROTEUS 1- 3%, AD %, AD %, ∀ linear 3-20% Qiang Xu12MobiSys’13

Collecting throughput, loss, and OWD predictions from AD 1, AD 2, and PROTEUS How to guarantee reproducible cellular network performance? Adjusting source rate, redundancy (FEC), and de-jitter buffer size Standard approach using the H.264 reference software No such open-source encoding/decoding suite for mobile Evaluating PROTEUS in video conferencing Qiang Xu13MobiSys’13

Equivalent mobile video conferencing Qiang Xu14 Per-frame adaptation Encoding/decodin g suite Reproducible network conditions Modifying the H.264 reference software Running the modified H.264 reference suite on a laptop Replaying the 400+ packet traces with adaptively encoded content MobiSys’13

Replaying packet trace in encoding Qiang Xu15 1. Compute 1. Compute 2. Encode adaptively 3. Refill with 3. Refill with PROTEUS AD 1 /AD 2 <frame> <frame> <frame> MobiSys’13

Decoding replayed packet traces PROTEUS 36dB AD 1 /AD 2 23dBTCP 20dB Qiang Xu16MobiSys’13

Additional FEC overhead: PROTEUS 5kbps, AD 1 /AD 2 20kbps FEC overhead due to over-protection Qiang Xu17MobiSys’13

Identified the predictability of cellular network performance in short term (e.g., 0.5s) Prediction accuracy: loss 98%, delay 97%, throughput 10±10kbps Designed PROTEUS to provide applications with performance forecast Evaluated the benefit to video conferencing Video conferencing: PSNR 15dB higher, almost identical to the hypothetical optimal Concluding PROTEUS Qiang Xu18MobiSys’13

Qiang Xu19MobiSys’13