Airlift: Video conferencing as a cloud service using inter-datacenter networks
Bibliographic record
Abstract
It is typical for enterprises to rely on services from cloud providers in order to build a scalable platform with abundant available resources to satisfy user demand, and for cloud providers to deploy a number of datacenters inter-connected with high-capacity links, across different geographical regions. In this paper, we propose that video conferencing, even with its stringent delay constraints, should also be provided as a cloud service, taking full advantage of the inter-datacenter network in the cloud. We design Airlift, a new protocol designed for the inter-datacenter network, tailored to the needs of a cloud-based video conferencing service. Airlift delivers packets in live video conferences to their respective destination datacenters, with the objective of maximizing the total throughput across all conferences, yet without violating end-to-end delay constraints. In order to simplify our protocol design in Airlift, we use intra-session network coding and the concept of conceptual flows, such that the optimization problem that can be conveniently formulated as a linear program. Our real-world implementation of Airlift has been deployed over the Amazon EC2 cloud. We show that Airlift delivers a substantial performance advantage over state-of-the-art peer-to-peer solutions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".