Deadline-Aware Scheduling and Routing for Inter-Datacenter Multicast Transfers
Bibliographic record
Abstract
Many applications like geo-replication need to deliver multiple copies of data from a single datacenter to multiple datacenters, which has benefits of improving fault tolerance, increasing availability and achieving high service quality. These applications usually require completing multicast transfers before certain deadlines. Some of the existing works only consider unicast transfers, which is not appropriate for the multicast transmission type. An alternative approach proposed by existing works was to find a minimum weight Steiner tree for each transfer. Instead of using only one tree for each transfer, we propose to use one or multiple trees, which increases the flexibility of routing, improves the utilization of available bandwidth, and increases the throughput for each transfer. In this paper, we focus on the multicast transmission type, propose an efficient and effective solution that maximizes throughput for all transfer requests while meeting deadlines. We also show that our solution can reduce packet reordering by selecting very few Steiner trees for each transfer. We have implemented our solution on a software-defined overlay network at the application layer, and our real-world experiments on the Google Cloud Platform have shown that our system effectively improves the network throughput performance and has a lower traffic rejection rate compared to existing related works.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".