Throughput prediction based on mobile device context

in Cellular Network Yihua (Ethan) Guo

University of Michigan

AIMS-5 2013

Background • Prevalence of cellular networks

– Mobile Traffic is expected to grow rapidly in the near future [Cisco VNI White Paper]

– 4G LTE network with much higher bandwidth (100 Mbps downlink and 50 Mbps uplink) and lower RTT (<5ms user-plane latency) [3GPP TR 25.913]

– Several measurement tools targeting at cellular network performance

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Challenges • How can mobile devices better utilize the

cellular network resources?

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Bartendr ARO IMP SALSA DWRA Our Approach

Layer A A/T A A T A/T

Scheduling?

Use context Location RSSI

RRC state

net type

net type, RSSI

RTT RSSI, RRC state

Efficient context?

Different network?

Throughput prediction?

T: transport layer, A: application layer

Challenges • How can we better predict performance?

– It’s dynamic, yet depending on the context

– Data analysis: correlating performance (e.g. TCP throughput) with device context

– Accuracy and overhead of prediction

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Utilizing the Mobile Device Context

• Radio Access – Network type, signal strength, cell ID, RRC/DRX state, etc.

• Sensors – Acceleration, GPS coordinates, etc.

• Other – Device type, screen on/off, time of day, etc.

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Measurement Settings

• Methodology – Mobile Device: Android (with access to a nation-wide ISP)

– TCP connection with continuous randomized data transfer in 2-5 minutes. Phone is kept stationary during the data transfer.

– Skip the first 10 seconds without sampling

– Throughput is sampled every 500 ms, device context is collected at the same time, packet traces are collected from both device and server

– Downlink: server -> device, Uplink: device -> server

– Different areas/network types/devices are considered

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HSDPA Downlink

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r = 0.6141

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HSDPA Uplink

r = -0.0098

LTE Downlink

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r = 0.8475

LTE Downlink

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r = 0.4814

LTE Uplink

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r = 0.6738

TCP Slow Start (LTE Downlink)

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TCP Slow Start (HSDPA Downlink)

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Implications

• Findings – HSDPA/LTE Downlink, LTE Uplink: positive correlation – HSDPA Uplink: nearly no correlation – TCP slow start period for LTE can be long

• How can we make use of the results? – Signal strength is a factor that affects LTE performance – May need additional information to improve the

prediction (more fine-grained)

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Implications • How can we make use of the results? (cont’d)

– Measurement fails if the bottleneck is not the cellular network part, or TCP connection does not saturate the link

– Data consumption could be high for a single throughput test (> 35MB for ~30Mbps, 10 s)

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Measured Throughput (kbps)

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10000150002000025000300003500040000

Mea

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(kbp

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Data Sharing

• Working on this

• Privacy is the main concern

– Sensitive information: IMEI, location, phone type, carrier, timestamp

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Discussions

• The effectiveness of throughput prediction in cellular network

• Validation on methodology of bandwidth/throughput measurement (to be coherent between datasets)

• Management and analysis of measurement data

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Thank you!

AIMS-5 2013