Delay-Optimized Video Traffic Routing in Software-Defined Interdatacenter Networks
Bibliographic record
Abstract
Many video streaming applications operate their geo-distributed services in the cloud, taking advantage of superior connectivities between datacenters to push content closer to users or to relay live video traffic between end users at a higher throughput. In the meantime, inter-datacenter networks also carry high volumes of other types of traffic, including service replication and data backups, e.g., for storage and email services. It is an important research topic to optimally engineer and schedule inter-datacenter traffic, taking into account the stringent latency requirements of video flows when transmitted along inter-datacenter links shared with other types of traffic. Since inter-datacenter networks are usually overprovisioned, unlike prior work that mainly aims to maximize link utilization, we propose a delay-optimized traffic routing scheme to explicitly differentiate path selection for different sessions according to their delay sensitivities, leading to a software-defined inter-datacenter networking overlay implemented at the application layer. We show that our solution can yield sparse path selection by only solving linear programs, and thus, in contrast to prior traffic engineering solutions, does not lead to overly fine-grained traffic splitting, further reducing packet resequencing overhead and the number of forwarding rules to be installed in each forwarding unit. Real-world experiments based on a deployment on six globally distributed Amazon EC2 datacenters have shown that our system can effectively prioritize and improve the delay performance of inter-datacenter video flows at a low cost.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".