M-Lab 2.0 Platform: Global Pilot Entry
For a while, we’ve been developing M-Lab 2.0 [1, 2]. This month, we are launching a global pilot for the new software stack. The changes include:
- Stock Linux 4.19 LTS kernels with modern TCP and Cubic congestion control
- Standard instrumentation for all experiments using tcp-info
- Virtualization and container management using Kubernetes and Docker
- Reimplementation of the NDT server
Update to M-Lab Policies
Earlier this month, M-Lab published updates to our policies after completing a comprehensive review to ensure our compliance with the EU General Data Protection Regulation (GDPR) and in preparation for the M-Lab 2.0 platform modernization update that will be rolled out this fall. This post outlines the changes and additions to our policies for the general public, for experiment developers hosting tests on the M-Lab platform, and for partners who provide hosting for M-Lab servers.
M-Lab Participates in Internet Measurements Workshop at 2019 African Internet Summit
M-Lab was pleased to be invited to contribute to the Internet Measurements workshop at the 2019 African Internet Summit, June 15-16, 2019 in Kampala, Uganda. M-Lab tech lead, Peter Boothe, and advisor, Georgia Bullen, presented a hands-on tutorial on querying and visualizing performance and routing datasets.
Traceroute BigQuery Table New Data Temporarily Halted for Schema Change
M-Lab is working on replacing the current traceroute BigQuery table with new schema, which will put all hops of one test in one row of BigQuery table. The new table will have all the information in the current table but make the search of hops within one test much easier. To make this happen, we will stop the new data feed of current traceroute BigQuery table in early July, 2019. The details of new schema will be published once the conversion of all data to BigQuery tables with the new traceroute schema is completed and available to the public.
Naming BQ Datasets after M-Lab Measurement Services & Data Types
Earlier this year, M-Lab published blog post outlining our new ETL pipeline and transition to new BigQuery tables. That post also outlined where we’ve saved our datasets, tables, and views in BigQuery historically, and recommended tables and views for most researchers to use. At that time we also implemented semantic versioning to new dataset and table releases at that time, and began publishing BigQuery views that unify our NDT data across multiple schema iterations and migrations.