End to end IP QoS assurance using policy based multi-agent SLA management systems
Bibliographic record
Abstract
The development of Internet as well overall communication technologies has created a very competitive communication market.In fact, there is no doubt that every company and perhaps every person in the world will have an Internet physical or wireless access via ISPs (Internet Service Provider).Thus the Internet became a very complex worldwide network which objective it to provide not only connectivity to end user but also services with a certain QoS (Quality of Service).Although, the effort made by each ISP to provide this QoS, it is not very easy to provide this QoS from access point of the customer to the destination point.The main difficulties are the negotiation process between ISPs in order to agree for the terms of collaboration and the deployment and management of the network in order to satisfy these agreements.It is recognized now that Policy-Based Networking became a key concept to facilitate the deployment of management strategy in IP based networks.Although, already existing solutions, they can operate only in a particular domain while the customer request for an endto-end deployment.Thus, it is necessary to extend this approach in order to integrate mechanisms that permit satisfy end-to-end SLA (Service Level Agreement) upon a number of administrative domains.Thus the objective of this paper is to propose a solution that allow the interoperability between various policy domains.The approach is based on mobile agents to facilitate the negotiation between the different domains.Customer or ISP Policy Based Management System delegate to a mobile agents the responsibility to negotiate the terms of SLA on their behalf .The mobile agent negotiate according to a set of policies defined by the Customer or the initiating ISP.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".