Supporting Deadline Constrained Distributed Computations on Grids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The growing popularity of grid and cloud computing has led to a renewed interest in resource control and coordination. The Actor model, which encapsulates objects along with threads of execution, offers a convenient way for scheduling computations' access to resources by way of scheduling of the actor threads. However, efficient Actor implementations do not use a thread for each actor, making implementation of fine-grained resource scheduling decisions difficult. This paper presents our work on integrating mechanisms for deadline assurance into an optimized implementation of Actors. We achieve this by using deadline-driven adaptive scheduling, which prioritizes individual message deliveries and method executions involved in a distributed computation, based on the calculated deadlines by which each must be completed. These deadlines can be efficiently calculated at run-time for an important class of computations which use pipeline interaction style. Additionally, a tuner dynamically balances -- manually or automatically -- the overhead of the control mechanisms against the extent of control exercised. Experimental evaluation shows that the approach offers effective support for timeliness requirements (for multimedia QoS, for example) at the cost of a relatively modest and adjustable overhead.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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