MARO - MinDrift affinity routing for resource management in heterogeneous computing systems
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
This paper deals with designing effective resource management strategies for systems of heterogeneous computers. Each computer is represented as an abstract server, capable of serving different task demands at different rates. We consider a system with I types of independent Poisson task demand arrival streams and J parallel servers with independent non-identical processing time distributions for each arrival type. The decision of routing each type i task immediately upon arrival to a server j is made by comparing the state information of a subset of the J servers. We show that choosing the subset according to a linear programming (LP) problem which maximizes the system capacity can not only significantly reduce the amount of state information required in making the routing decision, but also yield shorter total mean queue length (and hence mean time in system) compared with the policies requiring global state information. In addition, we explore means of limiting flexibility to further reduce the required state information.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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