Quantitative Measurement of the Digital Divide Prepared by: Les Cottrell SLAC with Shahryar Khan NIIT

Outline Why does it Matter How do we measure it? What is it telling us? –RTT, Unreachability, Losses, Jitter, VoIP, Throughput Other Information: –Routing in Developing Countries –Costs of Internet –Comparisons with “Development” Indices Conclusions Acknowledgements, more information …

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 Heloise Emdon, Acacia Southern Africa UNDP Global Meeting for ICT for Development, Ottawa 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 1.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.

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

PingER Deployment PingER project originally (1995) for measuring network performance for US, Europe and Japanese HEP community Extended this century to measure Digital Divide: –Collaboration with ICTP Science Dissemination Unit –ICFA/SCIC: Monitor 44 sites in S. Asia >120 countries (99% world’s connected population) >35 monitor sites in 14 countries

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

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 US & Canada Europe E Asia C Asia SE Europe SE Asia S Asia Oceania Africa L AmericaM East Russia Developed Regions Developing Regions

Losses N. America, Europe, E. Asia, Oceania < 0.1% Underdeveloped % loss, Africa worst. Mainly distance independent Big impact on performance, time outs etc. Losses > 2.5 % have big impact on interactivity, VoIP etc.

~ Distance independent Calculated as Inter Packet Delay Variation (IPDV) –IPDV = Dr i = R i – R i-1 Measures congestion Little impact on web, Decides length of VoIP codec buffers, impacts streaming Impacts (with RTT and loss) the quality of VoIP Trendlines for IPDV from SLAC to World Regions N. America E. Asia Europe Australasia S. Asia Africa Russia L. America SE Asia C Asia M East Usual division into Developed vs Developing Jitter

VoIP & MOS Telecom uses Mean Opinion Score (MOS) for quality –1=bad, 2=poor, 3=fair, 4=good, 5=excellent –With VoIP codecs best can get is 4.2 to 4.4 –Typical usable range 3.5 to 4.2 –Calc. MOS from PingER: RTT, Loss, Jitter ( MOS of Various Regions from SLAC Improvements very clear, often due to move from satellite to land line. Similar results from CERN (less coverage) Usable

World thruput seen from US 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 Throughput ~ 1460Bytes / (RTT*sqrt(loss)) (Mathis et al)

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

Routing Between developing countries often use transcontinental links (like Europe in 80’s), e.g.: –Pak to Pak or India to India is direct, however, –Between Pak & India via US or Canada or Europe –Between India or Pak and Bangladesh via US or UK –From S. Africa to African countries only Botswana and Zimbabwe are direct Most go via Europe or USA Wastes costly transcontinental bandwidth Need International eXchange Points (IXPs)

Costs compared to West Sites in many countries have bandwidth< US residence –“10 Meg is Here”, 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

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

Development Indices The size of the Internet infrastructure is a good indication of a country's progress towards an information-based economy. Measuring numbers of users not easy in developing countries because many people share accounts, use corporate and academic networks, or visit the rapidly growing number of cyber cafés, telecentres and business services. Furthermore, number of users does not take into account the extent of use, from those who just write a couple of s a week, to people who spend many hours a day on the net browsing, transacting, streaming, or downloading. New measures of Internet activity are needed to take these factors into account. Most of the Internet traffic in a developing country is international (75-90%) We measure international Internet performance which is an interesting (good?) indicator.

“Development” Indices There are many “development” indices today: –UNDP Human Development Index (2006, 177 countries) –UNDP Technology Achievement Index (2001, 72 countries) –ITU Digital Access Index (2003) and the Digital Opportunity Index (2006), both 180 countries –World Economic Forum’s Network Readiness Index (2004, 2005, : 122 countries) –Harvard University Network Readiness Index (2002, 75 countries) Values 0 – 1. Typically some subset of: GDP/capita, knowledge (e.g. tertiary education enrollment), life expectancy, network (hosts/capita, access, policy, usage, affordability, users/capita); technology (patents, royalties, exports, phones/capita, electricity)

How do they Look? The indices show very similar behaviors world wide. –Developed countries (US, Canada, Europe, E.Asia (jp, kr, tw), Australia/NZ, have high DOI –Most of Non-Mediterranean or Southern Africa have poor DOI –Land-locked countries plus Somalia, Tanzania, Myanmar, Iraq, Afghanistan have poor DOI Example: DOI Digital Opportunity Index from ITU, 2005

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

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

Digital Access Index (DAI) Most European countries > 1500 Kb/s throughput and greater than 0.6 DAI. Exceptions: –Malta, Belarus and Ukraine. –Balkans is catching up with Europe, exception Albania is way down. E. Asia apart from China clusters M East: Israel & Cyrus close to Europe, Iran way down SE Asia 3 cluster: Singapore at top, Malaysia and Brunei middle, Vietnam & Indonesia at bottom S. Asia 2 clusters: –India, Pakistan, Sri Lanka –Bangladesh, Bhutan, Nepal Africa at bottom Correlation strong infrastructure, affordability, knowledge and quality and actual usage of ICTs

DAI vs. Thru & S. Asia More details, also show populations Compare S. Asia with developed countries, C. Asia

Network Readiness Index (NRI) Ability to participate in and benefit from ICT developments –environment for ICT offered by a country or community –readiness of the community's key stakeholders (individuals, business and governments) –usage of ICT among these stakeholders. Strong correlations Very similar to TAI (not shown) and DAI.

Conclusions World divides into developed vs developing regions –Lots of variation within regions Last mile problems, and network fragility Decreasing use of satellites, expensive, but still needed for many remote countries in Africa and C. Asia Performance affects ability to collaborate Africa ~ 10 years behind and falling further behind, leads to “information famine” –E. Africa factor of 100 behind Europe Internet performance correlates strongly with development indices (linear for more technology based indices): –Objective, relatively easy to measure regularly –Need to increase coverage of monitoring to understand Internet performance Need support

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, Maxim Grigoriev (FNAL), Warren Matthews (GATech) for ongoing code development for PingER, Connie Logg (SLAC) and David Martin (IBM?) for earlier developments, 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, www-iepm.slac.stanford.edu/pinger/pingertech.htmlwww-iepm.slac.stanford.edu/pinger sdu.ictp.it/pinger/africa.html www-iepm.slac.stanford.edu/pinger/pingertech.html Case Studies: – Sahara+Case+Studyhttps://confluence.slac.stanford.edu/display/IEPM/Sub- Sahara+Case+Study – se+Studyhttps://confluence.slac.stanford.edu/display/IEPM/South+Asia+Ca se+Study –