Network Geointelligence

Transcription

1 Network Geointelligence Coping Bandwidth Uncertainty in High-Speed Mobility MAHBUB HASSAN Professor Computer Science and Engineering University of New South Wales, Sydney, Australia Keynote Speech, ACIS SNPD 2011, Sydney, 6 July 2011

2 Acknowledgements Dr. Salil Kanhere (my colleague at UNSW) Mr. Jun Yao (former PhD student, now with Freelancer.com)

3 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

4 Sharp Increase in Mobile Network Speed is Creating New Market Opportunities Vehicular Broadband Smart phones can enjoy streaming!

5 Heading for a Seamless Mobile Internet mobile access to Internet (Fixed) Internet mobile access is surpassing fixed access

6 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?

7 Presentation Overview Geo-sensitivity of mobile bandwidth Geointelligence (to reduce bandwidth uncertainty) Applications of geointelligence Mobile streaming Mobile multihoming Conclusion and on-going work

8 Is mobile bandwidth geo-sensitive? If so, what are the implications?

9 It seems that location determines your luck with bandwidth Wired.com s iphone 3G survey in 2008 ( 3G bandwidth varied significantly at country and region level

10 High-speed mobility escalates bandwidth uncertainty Stationary Driving Taken from HSDPA performance and evolution, Ericsson Review, No. 3, 2006

11 It seems 3G bandwidth is geo-sensitive. Can we quantify this geo-sensitiveness? UNSW MEASUREMENT CAMPAIGN IN 2008 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.

12 Measurement Architecture bandwidth is measured every 200 meters of a road Probe UNSW Downlink Probe (Packet Train) Probe Trigger (every 200m) Internet Provider B (HSDPA) Provider A (HSDPA) Provider C (pre-wimax) Probe Client

13 Measurement Hardware/Software Off-the-shelf Hardware (Soekris) Totally user-driven (no support from service provider)

14 Routes and Trips Inbound 7Km & Outbound 16.5Km (total 23.5 Km) at Kmph 75 repeated trips (Aug 07 Apr 08) outbound inbound UNSW

15 Quantizing the bandwidth signal

16 Probability distribution of different locations location = 500 meter of road They can be very different! Bandwidth is indeed geo-sensitive.

17 Differences in bandwidth distributions between adjacent road segments (L 1 distance values are within 0-2) Mobile apps could be in for a bumpy ride!

18 Bandwidth Varies Significantly at Many Geographical Scales (individual trip data for 3 trips - inbound) Data for Provider C (pre-wimax)

19 Bandwidth Varies Significantly at Many Geographical Scales (average bandwidth from 75 trips) 3G bandwidth exhibits significant geo-sensitivity

20 A CASE FOR GEOINTELLIGENCE

21 Bandwidth Entropy Quantifying Bandwidth Uncertainty with Information Theory Entropy quantifies uncertainty in data When X is a completely random process Lower the entropy, lower the uncertainty, better the predictability Entropy=0 completely deterministic Entropy=log 2 X completely random Example of a random variable with 2 possible outcomes, 0,1.

22 Location-based analysis reduces bandwidth uncertainty (case for geointelligence)

23 How Geointelligence Can Help (assume it stores average bandwidth observed in a given location from the previous trips) Avg(114,153) = 133 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

24 Root Mean Square Error Comparison (averaged over all 75 trips) Error with link monitor (no geointelligence) Error with geointelligence

25 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.

26 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,

27 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)

28 Geo-TFRC (TFRC with access to geointelligence) Goal: To help TFRC adapt to sudden bandwidth variations at location crossings

29 TFRC and Geo-TFRC Simulation geointelligence Foreman.qcif self-concatenated to create a 30 min video lasting the entire trip

30 Video Rate Adaptation Evaluation in ns-2 Based on Evalvid-RA (Lei et al. 07), Evalvid (Ke et al. 08)

31 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)

32 PSNR Comparison (cont.) Geo-TFRC TFRC

33 Fraction of Time With Poor Streaming Quality (PSNR < 31) 50% more disruptions 500% more disruptions

34 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.

35 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

36 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

37 Simulation Model Application 64Kbps Audio steaming (G771 Codec) Poisson streaming session arrival Exponential session duration (mean 2 minutes)

38 Measuring User Perceived QoS We use Mean Opinion Score (MOS) MOS Quality Impairment 5 Excellent Imperceptible 4 Good Perceptible 3 Fair Slightly Annoying 2 Poor Annoying 1 Bad Very Annoying 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)

39 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.

40 E-Model (cont.) 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

41 E-Model (cont.) Converting R factor to MOS: For R < 0: For 0 < R < 100: For R > 100:

42 Average Number of Glitches per Trip % more glitches if no geointelligence used

43 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 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

44 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 realtime media delivery in mobile environment, in ACM Mobile Video Delivery (in conjunction with ACM Multimedia 2010)

45 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.

46 Key Publications 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 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 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.