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Keynote Speech, ACIS SNPD 2011, Sydney, 6 July 2011
Network Geointelligence Coping Bandwidth Uncertainty in High-Speed Mobility
MAHBUB HASSAN Professor – Computer Science and Engineering
University of New South Wales, Sydney, Australia
Acknowledgements
Dr. Salil Kanhere (my colleague at UNSW) Mr. Jun Yao (former PhD student, now with Freelancer.com)
Two Amazing Developments in Mobile Computing
2000: 9kbps (GPRS) 2008: 3.6 Mbps (HSDPA) 2010: 21 Mbps (HSPA)
Exceeded 1GHz processing speed Ultra fast and high capacity memory
Sharp Increase in Mobile Network Speed is Creating New Market Opportunities
Vehicular Broadband
Smart phones can enjoy streaming!
Heading for a Seamless Mobile Internet
(Fixed) Internet
mobile access is surpassing fixed access
mobile access to Internet
Bandwidth Challenges for Mobile Internet
Peak capacity is not a challenge anymore Telstra already announced 42 Mbps peak rate
But wireless bandwidth is extremely uncertain Actual available bandwidth can vary anywhere from 0 to peak rate
High bandwidth uncertainty hinders reliable and quality commercial services Optimal delivery critically depends on knowledge of bandwidth
Quiz: if peak capacity increases, would uncertainty increase or decrease?
Presentation Overview
Geo-sensitivity of mobile bandwidth Geointelligence (to reduce bandwidth uncertainty) Applications of geointelligence
Mobile streaming Mobile multihoming
Conclusion and on-going work
Is mobile bandwidth geo-sensitive?
If so, what are the implications?
It seems that location determines your luck with bandwidth
Wired.com’s iPhone 3G survey in 2008 (www.wired.com)
3G bandwidth varied significantly at country and region level
High-speed mobility escalates bandwidth uncertainty
Taken from “HSDPA performance and evolution”, Ericsson Review, No. 3, 2006
Stationary Driving
UNSW MEASUREMENT CAMPAIGN IN 2008
It seems 3G bandwidth is geo-sensitive. Can we quantify this geo-sensitiveness?
J. Yao, S. Kanhere and M. Hassan, "An Empirical Study of Bandwidth Predictability in Mobile Computing", WiNTECH’08 (in ACM MOBICOM 2008), San Francisco, 19 Sep 2008.
Measurement Architecture bandwidth is measured every 200 meters of a road
Internet
Probe Server @ UNSW
Probe Client
Provider C (pre-wimax)
Provider A (HSDPA)
Provider B (HSDPA)
Downlink Probe (Packet Train)
Probe Trigger (every 200m)
Measurement Hardware/Software Off-the-shelf Hardware (Soekris) Totally user-driven (no support from service provider)
Routes and Trips Inbound 7Km & Outbound 16.5Km (total 23.5 Km) at 70-80 Kmph 75 repeated trips (Aug’07 – Apr’08)
inbound
outbound
UNSW
Quantizing the bandwidth “signal”
Probability distribution of different locations location = 500 meter of road
They can be very different! Bandwidth is indeed geo-sensitive.
31 19
Differences in bandwidth distributions between adjacent road segments (L1 distance values are within 0-2)
Mobile apps could be in for a bumpy ride!
Bandwidth Varies Significantly at Many Geographical Scales (individual trip data for 3 trips - inbound)
Data for Provider C (pre-WIMAX)
Bandwidth Varies Significantly at Many Geographical Scales (average bandwidth from 75 trips)
3G bandwidth exhibits significant geo-sensitivity
A CASE FOR GEOINTELLIGENCE
Bandwidth Entropy Quantifying Bandwidth Uncertainty with Information Theory
Entropy quantifies uncertainty in data
Lower the entropy, lower the uncertainty, better the predictability
Entropy=0 completely deterministic Entropy=log2X completely random
Example of a random variable with 2 possible outcomes, 0,1.
When X is a completely random process
Location-based analysis reduces bandwidth uncertainty (case for geointelligence)
How Geointelligence Can Help (assume it stores average bandwidth observed in a given location from the previous trips)
At the entry to location #7, geointelligence would give 133 kbps, but a link monitor agent would give 544
Convergence to 68 would be faster and smoother if started from 133 instead of 544
Avg(114,153) = 133
Root Mean Square Error Comparison (averaged over all 75 trips)
Error with link monitor (no geointelligence)
Error with geointelligence
MOBILE STREAMING
J. Yao, S. Kanhere, and M. Hassan, "Quality Improvement of Mobile Video Using Geo-intelligent Rate Adaptation", IEEE WCNC 2010, Sydney, 18 April 2010.
Adaptive Video Streaming
Store several streams of different quality (PSNR) for the same video Current bandwidth is continuously monitored Switch streams (quality or PSNR) according to current bandwidth Adaptation algorithms – TFRC, 3GPP PSS, HTTP, proprietary,
TFRC – TCP Friendly Rate Control
A widely discussed algorithm for UDP-based adaptive multimedia
TCP-like AIMD (additive increase multiplicative decrease) congestion control Slow ramp up for sudden low to high bandwidth
(wastes high PSNR opportunities) Packet loss for sudden high to low bandwidth
(quality may degrade beyond acceptable level)
Geo-TFRC (TFRC with access to geointelligence)
Goal: To help TFRC adapt to sudden bandwidth variations at location crossings
TFRC and Geo-TFRC Simulation
Foreman.qcif self-concatenated to create a 30 min video lasting the entire trip
geointelligence
Video Rate Adaptation Evaluation in ns-2 Based on Evalvid-RA (Lei et al. ’07) , Evalvid (Ke et al. ’08)
Video quality measurement (video quality is affected by packet loss from buffer overflow at cellular tower) The PSNR metric
For acceptable video quality: PSNR >= 31 (viewing is ‘disrupted’ for low PSNR)
PSNR Comparison (cont.) Geo-TFRC
TFRC
Fraction of Time With Poor Streaming Quality (PSNR < 31)
50% more disruptions
500% more disruptions
MULTIHOMING
J. Yao, S. S. Kanhere, M. Hassan, "Geo-intelligent Traffic Scheduling For Multi-Homed On-Board Networks", MOBIARCH'09 in ACM MOBISYS'09, Krakow, Poland, 22 June 2009.
NEMO (Network Mobility Standard from IETF) Downlink load balancing at Mobile Router Home Agent (HA)
Sudden change in bandwidth at the entry to a new location may overload a link causing buffer overflow at the cellular tower
Load Balancing Algorithm
Balance load using Proportional Fair Scheduler (assign load to a link proportional to its bandwidth capacity)
No geointelligence: estimate bandwidth every 2 sec and reshuffle loads if necessary
With geointelligence: continue as before, but upon entering a new location, fetch bandwidth information from geointelligence and reshuffle load if necessary
Simulation Model
Application 64Kbps Audio steaming (G771 Codec) Poisson streaming session arrival Exponential session duration (mean 2 minutes)
Measuring User Perceived QoS We use Mean Opinion Score (MOS)
Packet loss statistics (loss rate, burst size, etc.) are converted to MOS using ITU E-model
If MOS drops below 3, we will call it a `glitch’ (because it will ‘annoy’ the user)
MOS Quality Impairment 5 Excellent Imperceptible 4 Good Perceptible 3 Fair Slightly Annoying 2 Poor Annoying 1 Bad Very Annoying
ITU E-Model
R factor:
Ro: Basic signal-to-noise ratio, Ro =93.2 for G711. Is: impairments which occur with the voice signal, set to 0. Id: impairments caused by delay and the effective equipment impairment factor, set to 0 A: Compensation of impairment factors, set to 0 Ie-eff: impairments due to packet-losses of random distribution.
Ppl: Packet-loss Probability Packet-loss Robustness Factor Bpl =25.1 for G.711.A (with PLC) BurstR: Average Burst Length of Burst Lost Packets
E-Model (cont.)
For R < 0:
For 0 < R < 100:
For R > 100:
E-Model (cont.)
Converting R factor to MOS:
Average Number of Glitches per Trip
100-300% more glitches if no geointelligence used
Conclusion (1)
Location, even at 500m scale, seems to influence 3G bandwidth (bandwidth is geo-sensitive at many scales)
Is bandwidth geo-sensitivity just a Sydney phenomenon? No. See “performance comparison of 3G and metro-scale wifi for
vehicular network access”, ACM International Measurement Conference 2010 --- confirming geo-sensitivity for New York roads
Using our ‘bandwidth entropy’ method, they observed geo-sensitivity even at 10m scale for 3G as well as WiFi
Conclusion (2)
Even simple geointelligence of past average of a location seems to provide significant improvement for streaming quality of experience (at least for TFRC platform)
Are these improvements only applicable to TFRC? No. NOKIA has recently demonstrated that 3GPP streaming standard
can also benefit from such geointelligence. See “Geo-predictive real-time media delivery in mobile environment”, in ACM Mobile Video Delivery (in conjunction with ACM Multimedia 2010)
Future Directions
How to gather geointelligence for every roads on this earth? We are currently working on a croudsourcing concept
What’s the best way to integrate geointelligence in streaming or other application platforms Many issues to consider --- client driven or server driven, user’s
location privacy, impact on installed base, etc.
Key Publications http://www.cse.unsw.edu.au/~vnet
J. Yao, S. Kanhere, and M. Hassan, ”Improving QoS in high-speed mobility using bandwidth maps", IEEE Transactions on Mobile Computing (in press)
J. Yao, S. Kanhere, and M. Hassan, "Quality Improvement of Mobile Video Using Geo-intelligent Rate Adaptation", IEEE WCNC 2010, Sydney, 18 April 2010.
J. Yao, S. S. Kanhere, M. Hassan, "Geo-intelligent Traffic Scheduling For Multi-Homed On-Board Networks", MOBIARCH'09 in ACM MOBISYS'09, Krakow, Poland, 22 June 2009.
J. Yao, S. Kanhere and M. Hassan, "An Empirical Study of Bandwidth Predictability in Mobile Computing", WiNTECH’08 in ACM MOBICOM 2008, San Francisco, 19 September 2008.