Discovering with QoS the geo-located web services over next generation of mobile networks
Bibliographic record
Abstract
Discovering with QoS the geo-located Web services is a great challenge in the client mobility context. In this paper, we propose a mechanism that collects for a particular geo-located Web service in a specific domain controlled by a geo-located discovery server GLWSM (geo-located Web services manager) the network bandwidth and the utilization factor of a supplier application server (SAS). Data collected are used in the application servers' selection and migration criterion. For a specific geo-located Web service, a GLWSM sends periodically a collect traffic request to all SAS that offer the concerned service in his domain. Then, each implicated SAS executes two processes: the network bandwidth quotation and the SAS utilization factor collection. In the network bandwidth quotation, a SAS interact with the mobile anchor point (MAP) of a GLWSM domain, to collect the network bandwidth. Meanwhile, the SAS utilization factor consists of collecting the SAS processor rate. To justify the benefits of our concept, we built a prototype that uses the NS-2 simulator and the RSVP (resource reservation protocol) protocol and we analyzed the consistency of the GLWSA system. Results prove that the proposed concept has a better responsiveness compare to a similar discovery servers with QoS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".