Performance modeling of systems using fair share scheduling with Layered Queueing Networks
Bibliographic record
Abstract
Fair-share scheduling attempts to grant access to a resource based on the amount of “share” that a task possesses. It is widely used in places such as Internet routing, and recently, in the Linux kernel. Software performance engineering is concerned with creating responsive applications and often uses modeling to predict the behaviour of a system before the system is built. This work extends the Layered Queueing Network (LQN) performance model used to model distributed software systems by including hierarchical fair-share scheduling with both guarantees and caps. To exercise the model, the Completely Fair Scheduler, found in recent Linux kernels, is incorporated into PARASOL, the underlying simulation engine of the LQN simulator, lqsim. This simulator is then used to study the effects of fair-share scheduling on a multi-tier implementation of a building security system. The results here show that fair-share scheduling with guarantees is not sufficient when an application is layered into multiple tiers because of contention at lower layers in the system. Fair-share scheduling with caps must be used instead.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".