Reinforcement Learning-based Admission Control in Delay-sensitive\n Service Systems
Why this work is in the frame
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Bibliographic record
Abstract
Ensuring quality of service (QoS) guarantees in service systems is a\nchallenging task, particularly when the system is composed of more fine-grained\nservices, such as service function chains. An important QoS metric in service\nsystems is the end-to-end delay, which becomes even more important in\ndelay-sensitive applications, where the jobs must be completed within a time\ndeadline. Admission control is one way of providing end-to-end delay guarantee,\nwhere the controller accepts a job only if it has a high probability of meeting\nthe deadline. In this paper, we propose a reinforcement learning-based\nadmission controller that guarantees a probabilistic upper-bound on the\nend-to-end delay of the service system, while minimizes the probability of\nunnecessary rejections. Our controller only uses the queue length information\nof the network and requires no knowledge about the network topology or system\nparameters. Since long-term performance metrics are of great importance in\nservice systems, we take an average-reward reinforcement learning approach,\nwhich is well suited to infinite horizon problems. Our evaluations verify that\nthe proposed RL-based admission controller is capable of providing\nprobabilistic bounds on the end-to-end delay of the network, without using\nsystem model information.\n
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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