Job arrival rate aware scheduling for asymmetric multi-core servers in the dark silicon era
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 rate at which jobs arrive for processing at servers in a data-center (i.e., the job arrival rate) can vary significantly with time. Each server in a data-center is a multi-core processor, allowing jobs to be processed with different degrees of parallelism (DoPs) (i.e., number of threads per job). In this paper, we show both analytically and empirically that the optimal DoP that minimizes mean service time varies with job arrival rate. In addition, we show that for asymmetric multi-core server processors (i.e., processors with multiple clusters, each consisting of cores of a different type, and assuming that only one cluster is active at any given time while the others are dark), the best cluster to select is also dependent on job arrival rate. Based on these observations, we propose a run-time scheduler that determines the optimal DoP and performs inter-cluster migration to minimize mean service time within a power budget. Experimental results demonstrate significant reduction in mean service time compared to job arrival rate unaware schedulers.
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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 | 0.001 |
| 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