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 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 | 0.001 |
| 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 teacher head, 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".