Job arrival rate aware scheduling for asymmetric multi-core servers in the dark silicon era
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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".