Cloudifying the 3GPP IP multimedia subsystem for 4G and beyond: A survey
Bibliographic record
Abstract
4G systems have been continuously evolving to cope with the emerging challenges of human-centric and M2M applications. Research has also now started on 5G systems. Scenarios have been proposed and initial requirements derived. 4G and beyond systems are expected to easily deliver a wide range of human-centric and M2M applications and services in a scalable, elastic, and cost-efficient manner. The 3GPP IMS was standardized as the service delivery platform for 3G networks. Unfortunately, it does not meet several requirements for provisioning applications and services in 4G and beyond systems. However, cloudifying it will certainly pave the way for its use as a service delivery platform for 4G and beyond. This article presents a critical overview of the architectures proposed so far for cloudifying the IMS. There are two classes of approaches; the first focuses on the whole IMS system, and the second deals with specific IMS entities. Research directions are also discussed. IMS granularity and a PaaS for the development and management of IMS functional entities are the two key directions we currently foresee.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".