<italic>Stemflow:</italic> Software-Defined Inter-Datacenter Overlay as a Service
Bibliographic record
Abstract
Modern Internet applications are typically hosted in the public cloud, with multiple server instances running within geographically distributed datacenters. Thanks to the abundantly available bandwidth on wide-area links that interconnect these datacenters, it is conceivable that bandwidth-intensive applications may improve their performance by relaying their traffic through such an inter-datacenter network. However, there does not yet exist a cloud service that provides a turn-key solution to tap into such available bandwidth resources conveniently. In this paper, we design and implement Stemflow, a new system framework that provides Inter-Datacenter Overlay as a Service based on the software-defined networking principle. It offers an attractive foundation that helps an Internet application to transparently improve its scalability and performance by using inter-datacenter networks for its traffic. With Stemflow, all deployed server instances will construct an overlay atop an inter-datacenter network, and the routing decisions to relay application traffic are made by a centralized controller. The algorithms needed to make these decisions are customized to meet the needs of individual applications, and are cached within the data plane. We motivate and describe the design decisions, and present an extensive experimental evaluation in public cloud infrastructures, using two example applications as our case studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.011 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".