An Information Centric Networking approach towards contextualized edge service
Bibliographic record
Abstract
Information Centric Networking (ICN) has been a popular research topic in the last few years, but has not attracted industry attention because of its disruptive view; this is expected considering the evolution from PSTN to IP. Towards its adoption, ICN should not only address challenges raised by current applications, but also enable a compelling service framework for next generation of networking. We envision that in the next generation networks, the network narrow waist will allow an efficient distribution of intelligence across terminals, access, edge and core network. This will enable new applications, services and future business models to be realized. Two other technologies, NFV and SDN, which in essence are frameworks that enable service-centric networking, fit well with the objective of information-centric networking, where the delivered content is a result of contextual interaction between consumers and services orchestrated to meet service objectives. Most significant benefit of this interaction will be in the network-edge considering sensitivity to service latency, customization, and contextualization. This paper provides an overview of an ICN based edge service framework, with comprehensive discussion on service composition, orchestration, and routing logic with mapping to resources in the underlying substrate. We also provide a discussion of the prototype to realize this platform and a network based conferencing system scalable to large number of participants; however the platform itself is generic to handle any service type including content distribution, video conferencing, and M2M applications.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".