Reinforcement Learning-based Admission Control in Delay-sensitive\n Service Systems
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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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".