Bibliographic record
Abstract
In this paper, we present the design and implementation of Beehive, a distributed control platform with a simple programming model. In Beehive, control applications are centralized asynchronous message handlers that optionally store their state in dictionaries. Beehive's control platform automatically infers the keys required to process a message, and guarantees that each key is only handled by one light-weight thread of execution (i.e., bee) among all controllers (i.e., hives) in the platform. With that, Beehive transforms a centralized application into a distributed system, while preserving the application's intended behavior. Beehive replicates the dictionaries of control applications consistently through mini-quorums (i.e., colonies), instruments applications at runtime, and dynamically changes the placement of control applications (i.e., live migrates bees) to optimize the control plane. Our implementation of Beehive is open source, high-throughput and capable of fast failovers. We have implemented an SDN controller on top of Beehive that can handle 200K of OpenFlow messages per machine, while persisting and replicating the state of control applications. We also demonstrate that, not only can Beehive tolerate faults, but also it is capable of optimizing control applications after a failure or a change in the workload.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".