Dynamic Bandwidth Management for TCP Flows in a Diffserv-Enabled Mobile Wireless Access Network
Bibliographic record
Abstract
With the soaring demand to provide global mobility for wide range of services, a growing trend for wireless access networks is to support multiple radio technologies that can be achieved efficiently by using TCP/IP protocols in the access network. In the thesis, we consider a mobile wireless access network where DiffServ is deployed as the QoS solution and Mobile IP is employed as the handover protocol. We first conducted a study on the impact of handover on DiffServ flows. Then we introduced a transient service level for handover flows and propose our QoS scheme and admission control algorithms for handover flows, which protect local flows from losing bandwidth to handover flows by separating the two flows into different service classes. We also proposed a service upgrade algorithm to upgrade the service level of handover flows based on the dynamic information of bandwidth utilization and different service upgrade priorities. To guarantee the proper provisioning for each service class, we proposed a dynamic bandwidth-provisioning algorithm that allows dynamic adjustment of bandwidth allocations to different service classes by adjusting their respective weights configured at the scheduler. We evaluated the feasibility of our QoS scheme and algorithm by simulating different handover situations and results show that the proposed scheme is viable under variety of provisioning scenarios.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".