Bibliographic record
Abstract
In current TCP, UDP and DCCP networks, the finite nature of real time data is not taken into consideration when scheduling of packets occurs on intermediate devices. To overcome this, the research presented in this thesis incorporates deadline scheduling into DCCP. This allows packet age information to be transferred by the DCCP protocol which provides intermediate devices with the ability to make more informed transportation decisions for real time data. By embedding two variables, in the form of options into DCCP-Data packets, a number of new prioritization and scheduling mechanisms that utilize packet age information are made possible. This thesis will show how deadline scheduling is incorporated into DCCP in a stable, backward compatible and DCCP standard complaint manner. \n \nOnce deadline scheduling is incorporated into DCCP, the focus of the thesis then shifts towards mechanisms that can be implemented on intermediate devices that make use of the packet life information. To begin, five unique packet discard mechanisms that purge stale packets from the network are presented. The purpose of these mechanisms is to remove stale packets from the network using intermediate devices in the network in order to free network resources for non stale packets utilizing the same infrastructure. Experimentation carried out to investigate the efficiency and benefits to DCCP performance offered by each of these five mechanisms is shown. The experimentation also explores fairness amongst competing flows when the mechanisms are activated. \n \nFollowing this, a novel probabilistic scheduling (PBS) mechanism is introduced that predicts the probability a packet has of arriving at its intended destination net- work within its useful lifespan. Once this probability is calculated scheduling decisions are then made based on this value in order to offer optimized delivery to real time data. In order to calculate this probability, the PBS mechanism utilizes metrics from the Cisco EIGRP routing protocol. Experiments carried out using the PBS mechanism demonstrate that the PBS mechanism improves DCCP performance in networks where high levels of stale packets occur. \n \nOverall, this research presents a new approach to the transportation of real time data in DCCP networks and aims to improve DCCP adoption through the improved performance capability added to the protocol by this research.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".