Event-Triggered Stochastic Model Predictive Control for Constrained Queueing Networks
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
An event-triggered stochastic model predictive control (MPC) approach is proposed for the scheduling problem of constrained queueing networks with a dynamic topology. A discrete-time Markov chain (DTMC) in combination with a Bernoulli trial is used to model the time-varying routing of queueing networks. The constituency and positiveness constraints on queue lengths together with the dynamic topology and the stochasticity in packet arrival are incorporated into a stochastic MPC optimization problem. An event-triggered scheme with adaptive event checking involving an estimated waiting horizon is designed to trigger the solution of the optimization problem when necessary, leading to reduced computational burden and improved utilization of communication resources. The stability is analyzed by the relation between the inter-execution time and objective function. The proposed approach is applied to two queueing networks to show its effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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