1 / 36

Visualizing the Digital Divide from an Internet Point of View & Challenges

Visualizing the Digital Divide from an Internet Point of View & Challenges. Prepared by: Les Cottrell SLAC Umar Kalim NIIT , Shahryar Khan NIIT , Akbar Mehdi NIIT For COMSATS University, Islamabad, Pakistan, March 14, 2007. Outline. Digital Divide:

pascal
Download Presentation

Visualizing the Digital Divide from an Internet Point of View & Challenges

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Visualizing the Digital Divide from an Internet Point of View & Challenges Prepared by: LesCottrellSLAC Umar KalimNIIT, Shahryar KhanNIIT, Akbar MehdiNIIT For COMSATS University, Islamabad, Pakistan, March 14, 2007

  2. Outline • Digital Divide: • Examples of effect of Digital Divide & why it matters • How we measure it • What we find • A network challenge for mathematicians, statisticians

  3. Why Does it Matter 4. Sep 05, international fibre to Pakistan fails for 12 days, satellite backup can only handle 25% traffic, call centres given priority. Research & Education sites cut off from Internet for 12 days • School in a secondary town in an East Coast country with networked computer lab spends 2/3rds of its annual budget to pay for the dial-up connection. • Disconnects 2. Telecentre in a country with fairly good connectivity has no connectivity • The telecentre resorts to generating revenue from photocopies, PC training, CD Roms for content. Heloise Emdon, Acacia Southern Africa UNDP Global Meeting for ICT for Development, Ottawa 10-13 July 3. Primary health care giver, somewhere in Africa, with sonar machine, digital camera and arrangement with national academic hospital and/or international health institute to assist in diagnostics. After 10 dial-up attempts, she abandons attempts to connect

  4. How do we measure it? • PingER project • Arguably the world’s most extensive active end-to-end Internet Performance Project

  5. PingER Methodology >ping remhost Uses ubiquitous ping Remote Host (typically a server) Monitoring host Internet 10 ping request packets each 30 mins Once a Day Ping response packets Data Repository @ SLAC Measure Round Trip Time & Loss

  6. Architecture • Monitor hosts send 21 pings each 30 mins to Remote Hosts and cache results • Archive hosts gather data daily, save, analyze & make results available publicly via web

  7. PingER Deployment • PingER project originally (1995) to measure network performance for US, Europe and Japanese HEP community • Extended this century to measure Digital Divide: • Collaboration with ICTP Science Dissemination Unit http://sdu.ictp.it • ICFA/SCIC: http://icfa-scic.web.cern.ch/ICFA-SCIC/ • >120 countries (99% world’s connected population) • >30 monitor sites in 14 countries • Monitor 44 sites in S. Asia

  8. World Measurements: Min RTT from US • Maps show increased coverage • Min RTT indicates best possible, i.e. no queuing • >600ms probably geo-stationary satellite • Between developed regions min-RTT dominated by distance • Little improvement possible • Only a few places still using satellite for international access, mainly Africa & Central Asia 2000 2006

  9. Effect of Losses • Losses critical, cause multi-second timeouts • Typically depend on a bad link, so ~distance independent • > 4-6% video-conf irritating, non-native language speakers unable to communicate • > 4-5% irritating for interactive telnet, X windows • >2.5% VoIP annoying every 30 seconds or so • Burst losses of > 1% slightly annoying for VoIP

  10. Losses from SLAC to world • # hosts monitored increased seven-fold • Increase in fraction with good loss • Despite adding more hosts in developing world >=12% >=5% <12% >=2.5% < 5% >=1% < 2.5% < 1%

  11. Loss Improvement by Population • Loss by country weighted by population of country

  12. Unreachability • All pings of a set fail ≡unreachable • Shows fragility, ~ distance independent • Developed regions US, Canada, Europe, Oceania, E Asia lead • Factor of 10 improvement in 8 years • Africa, S. Asia followed by M East & L. America worst off • Africa NOT improving SE Asia L America M East C Asia Oceania S Asia SE Europe Russia Developing Regions Africa E Asia Developed Regions US & Canada Europe

  13. World thruput seen from US Throughput ~ 1460Bytes / (RTT*sqrt(loss)) (Mathis et al) Behind Europe 6 Yrs: Russia, Latin America 7 Yrs: Mid-East, SE Asia 10 Yrs: South Asia 11 Yrs: Cent. Asia 12 Yrs: Africa South Asia, Central Asia, and Africa are in Danger of Falling Even Farther Behind

  14. Normalized for Details • Note step changes • Africa v. poor • S. Asia improving • N. America, Europe, E Asia, Oceania lead

  15. Overall (Aug 06) • ~ Sorted by Average throughput • Within region performance better (black ellipses) • Europe, N. America, E. Asia generally good • M. East, Oceania, S.E. Asia, L. America acceptable • C. Asia, S. Asia poor, Africa bad (>100 times worse) Monitored Country

  16. South Asia • Population

  17. S Asia Bandwidth & Internet use • Note Log scale for BW • India region leader • Pakistan leads bw/pop • Nepal very poor • Pakistan leads % users • Sri Lanka leads hosts%% • Pakistan leads bw/pop • Nepal, Bangladesh, Afghanistan very poor

  18. S Asia PingER Coverage Min-RTT from CERN • Monitor 44 sites in region. • 6 Monitoring hosts (3 ea in India & Pakistan) Loss from CERN

  19. Derived thruput • Divides into 2 • India, Maldives, Pakistan, Sri Lanka • Bangladesh, Nepal, Bhutan, Afghanistan • Weekend vs. weekday indicates heavy congestion

  20. Digital Access Index (DAI): Infrastructure availability, Affordability of access, Education, Quality of ICT, & Internet usage Europe, E Asia (except China), Oceania top right Israel & Singapore with top group Middle East in middle, Iran poorest Africa bottom left S. Asia split: Bhutan, Nepal, Bangladesh with Africa India, Pak, Sri Lanka better Strong positive linear correlation, C Asia

  21. DAI & S. Asia

  22. D.D. Conclusions • Last mile problems, and network fragility • Decreasing use of satellites, expensive, but still needed for many remote countries in Africa and C. Asia • Africa ~ 10 years behind and falling further behind, leads to “information famine” • E. Africa factor of 100 behind Europe • EASSy project will bring fibre to E. Africa, hopefully better access than SAT3 • Africa big target of opportunity • Growth in # users 2000-2005 200%, Africa 625% • Need more competitive pricing • Fibre competition, government divest for access, low cost VSAT licenses • Consortiums to aggregate & get better pricing ($/BW reduces with BW) • Need better routing - IXPs • Need training & skills for optimal bandwidth management • Internet performance correlates strongly with UNDP & ITU development indices • Increase coverage of monitoring to understand Internet performance

  23. Challenge, however… • Elegant graphics are great to understand problems BUT: • Can be thousands of graphs to look at (many site pairs, many devices, many metrics) • Need automated problem recognition AND diagnosis • So developing tools to reliably detect significant, persistent changes in performance • Initially using simple plateau algorithm to detect step changes

  24. Examples of real data Caltech: thrulay • Misconfigured windows • New path • Very noisy • Seasonal effects • Daily & weekly 800 Mbps 0 Nov05 Mar06 UToronto: miperf 250 Mbps 0 Jan06 Nov05 Pathchirp UTDallas • Some are seasonal • Others are not • Events may affect multiple-metrics 120 thrulay Mbps 0 iperf Mar-20-06 Mar-10-06 • Events can be caused by host or site congestion • Few route changes result in bandwidth changes (~20%) • Many significant events are not associated with route changes (~50%)

  25. Changes in network topology (BGP) can result in dramatic changes in performance Hour Samples of traceroute trees generated from the table Los-Nettos (100Mbps) Remote host Snapshot of traceroute summary table Notes: 1. Caltech misrouted via Los-Nettos 100Mbps commercial net 14:00-17:00 2. ESnet/GEANT working on routes from 2:00 to 14:00 3. A previous occurrence went un-noticed for 2 months 4. Next step is to auto detect and notify Drop in performance (From original path: SLAC-CENIC-Caltech to SLAC-Esnet-LosNettos (100Mbps) -Caltech ) Back to original path Dynamic BW capacity (DBC) Changes detected by IEPM-Iperfand AbWE Mbits/s Available BW = (DBC-XT) Cross-traffic (XT) Esnet-LosNettos segment in the path (100 Mbits/s) ABwE measurement one/minute for 24 hours Thurs Oct 9 9:00am to Fri Oct 10 9:01am

  26. On the other hand • Route changes may affect the RTT (in yellow) • Yet have no noticeable effect on on available bandwidth or throughput Available Bandwidth Achievable Throughput Route changes

  27. Seasonal Effects on events • Change in bandwidth (drops) between 19:00 & 22:00 Pacific Time (7:00-10:00am PK time) • Causes more anomalous events around this time

  28. Forecasting • Over-provisioned paths should have pretty flat time series • Short/local term smoothing • Long term linear trends • Seasonal smoothing • But seasonal trends (diurnal, weekly need to be accounted for) on about 10% of our paths • Use Holt-Winters triple exponential weighted moving averages

  29. Econometrics • Econometrists use forecasting techniques for predicting the behavior of economic metrics • Auto Regressive Integrated Moving Average (ARIMA & ARMA) • Very mathematical, maybe multiple parameters • Our (Fareena Saqib) first look at was promising • Do not curently have someone working on it.

  30. Experimental Alerting • Have false positives down to reasonable level (few per week), so sending alerts to developers • Saved in database • Links to traceroutes, event analysis, time-series

  31. More information/Questions • Acknowledgements: • Harvey Newman and ICFA/SCIC for a raison d’etre, ICTP for contacts and education on Africa, Mike Jensen for Africa information, NIIT/Pakistan for developing valuable tools, Maxim Grigoriev (FNAL), Warren Matthews (GATech) for ongoing code development for PingER, USAID MoST/Pakistan for development funding, SLAC for support for ongoing management/operations support of PingER • PingER • www-iepm.slac.stanford.edu/pinger, sdu.ictp.it/pinger/africa.html • Human Development • http://www.gapminder.org/ • Case Studies: • https://confluence.slac.stanford.edu/display/IEPM/Sub-Sahara+Case+Study • http://sdu.ictp.it/lowbandwidth/program/case-studies/index.html

  32. Extra Slides Follow

  33. Costs compared to West • Sites in many countries have bandwidth< US residence • “10 Meg is Here”, www.lightreading.com/document.asp?doc_id=104415 • Africa: $5460/Mbps/m • W Africa $8K/Mbps/m • N Africa $520/Mbps/m • Often cross-country cost dominates cf. international 1 yr of Internet access > average annual income of most Africans, Survey by Paul Budde Communnications

  34. UNDP Human Development Index (HDI) • A long and healthy life, as measured by life expectancy at birth • Knowledge, as measured by the adult literacy rate (with two-thirds weight) and the combined primary, secondary and tertiary gross enrolment ratio (with one-third weight) • A decent standard of living, as measured by GDP per capita. Africa PingER - Strong Correlation - Non subjective - Quicker / easier to update

  35. Med. & Africa vs HDI • N. Africa has 10 times poorer performance than Europe • Croatia has 13 times better performance than Albania • Israel has 8 times better performance than rest of M East Med. Countries • E. Africa poor, limited by satellite access • W. Africa big differences, some (Senegal) can afford SAT3 fibre others use satellite • Great diversity between & within regions

  36. Why does it matter: Business Traditional MNC Business Model >$20K per year 75 to 100 million people Some MNCs >$1,500 - 20K per year 1.5 to 1.75 billion people Local Firms Future Opportunity? <$1,500 per year 4 billion people • G8 specifically pledged support for African higher education and research by “Helping develop skilled professionals for Africa's private and public sectors, through supporting networks of excellence between African's and other countries' institutions of higher education and centres of excellence in science and technology institutions” G8 specifically pledged support for African higher education and research by “Helping develop skilled professionals for Africa's private and public sectors, through supporting networks of excellence between African's and other countries' institutions of higher education and centres of excellence in science and technology institutions” Prahalad and Hart • Saturating western markets • High growth IT markets: BRIC • NOT business as usual • New business models • Distinct needs • Dearth of distribution channels Karen Coppock RDVP, Stanford

More Related