Periodic GATE Optimization with QoS-awareness for Long-Reach Passive Optical 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
In this paper, we propose a bandwidth allocation scheme working with differentiated services for the Multi-Point Control Protocol (MPCP) in Long-Reach Passive Optical Networks. The proposed scheme is an enhancement to our recently proposed bandwidth allocation scheme Periodic Gate Optimization (PGO), and it is called Periodic Gate Optimization with Quality of Service Awareness (PGO-QoS). Long-Reach PON introduces a challenge by the deployment of passive elements in a long distance up to 100km between the OLT and the ONUs. It becomes more challenging when the subscribers have different Service Level Agreements (SLAs) with specific performance requirements such as delay bounds and/or packet drop probabilities. PGO-QoS consists of two independent modules; intra-ONU scheduling and dynamic bandwidth allocation. Intra-ONU scheduling stands for the burstification of the buffered packets at the ONUs, and it determines the proportion of the packets to be dequeued from the buffer of each SLA class. These proportions are also appended to the REPORT message to be used by the OLT in the dynamic bandwidth allocation module. The bandwidth allocation module runs at the OLT. This module is mostly inherited from recently proposed PGO. Based on the collected REPORT messages, the OLT periodically builds an ILP model to estimate the appropriate GATE credits of the overloaded ONUs until the next optimization period. The ILP model sets the appropriate constraints so that the OLT tends to prioritize the ONUs where dequeuing proportions of the high priority queues are greater. The simulation results show that PGO-QoS leads to a lower average delay and shorter queue length and less packet delay. Moreover, the proposed scheme also introduces decreased delay and low packet loss for the higher priority SLA classes which are class-3 and class-2.
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 | 0.000 |
| 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