Internet Performance Analysis of South Asian Countries Using End-to-End Internet Performance Measurements
Bibliographic record
Abstract
Internet performance is highly correlated with key economic development metrics of a region. According to World Bank, the economic growth of a country increases 1.3% with a 10% increase in the speed of the Internet. Therefore, it is necessary to monitor and understand the performance of the Internet links in the region. It helps to figure out the infrastructural inefficiencies, poor resource allocation, and routing issues in the region. Moreover, it provides healthy suggestions for future upgrades. Therefore, the objective of this paper is to understand the Internet performance and routing infrastructure of South Asian countries in comparison to the developed world and neighboring countries using end-to-end Internet performance measurements. The South Asian countries comprise nearly 32% of the Internet users in Asia and nearly 16% of the world. The Internet performance metrics in the region are collected through the PingER framework. The framework is developed by the SLAC National Accelerator Laboratory, USA and is running for the last 20 years. PingER has 16 monitoring nodes in the region, and in the last year PingER monitors about 40 sites in South Asia using the ubiquitous ping facility. The collected data is used to estimate the key Internet performance metrics of South Asian countries. The performance metrics are compared with the neighboring countries and the developed world. Particularly, the TCP throughput of the countries is also correlated with different development indices. Further, worldwide Internet connectivity and routing patterns of the countries are investigated to figure out the inconsistencies in the region. The performance analysis revealed that the South Asia region is 7-10 years behind the developed regions of North America (USA and Canada), Europe, and East Asia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".