Supporting Resource Control for Actor Systems in Akka
Why this work is in the frame
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Bibliographic record
Abstract
Although there are models and prototype implementations for controlling resource use in Actor systems, they are difficult to implement for production implementations of Actors such as Akka. This is because the messaging and scheduling infrastructures of runtime systems are increasingly complex and significantly different from one system to another. This paper presents our efforts in implementing resource control support for Actor systems implemented using the Akka library. Particularly, given the lack of support in Akka for direct scheduling of actors, we compare two different ways of approximating actor-level control support. The first implementation expects messages to actors to provide estimates of resources likely to be consumed for processing them; these estimates are then relied upon to make scheduling decisions. In the second implementation, resource use of scheduled actors is tracked, and compared against allocations to decide when they should be scheduled next. We present experimental results on the performance cost of these resource control mechanisms, as well as their impact on resource utilization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it