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

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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

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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?

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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

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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

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Liu et al. MobiCom’08

Manweiler et al.

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Shafiq et al. SIGMETRICS’

11

second minute hour

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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

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Evidence of performance predictability

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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

0.001

Why predictable? Scheduling at base stations

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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

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time windowhistory window

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No offline training, predicting in real-time

• Available after the first history window

Comparing against two adaption solutions• AD1: adapt to the current time window• AD2: adapt to the averaged history window

Running regression trees over traces

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Prediction accuracy for lossA false positive occurs if a loss is predicted but actually not

FP: PROTEUS 1%, AD1 3-20%, AD2 >80%, ∀ linear 3-5%; FN: PROTEUS 1-3%, AD1 5-25%, AD2 20-80%, ∀ linear 3-20%

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Collecting throughput, loss, and OWD predictions from AD1, AD2, 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

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Equivalent mobile video conferencing

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Per-frame adaptation

Encoding/decoding 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

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Replaying packet trace in encoding

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1. Compute <source rate, FEC>

2. Encode <frame> adaptively

3. Refill <random> with <frame>

PROTEUSAD1/AD2

<frame><frame><frame>

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Decoding replayed packet traces

PROTEUS 36dB

AD1/AD2 23dB TCP 20dB

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Additional FEC overhead: PROTEUS 5kbps, AD1/AD2 20kbps

FEC overhead due to over-protection

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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

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