INGR Roadmap Edge Services and Automation Chapter
Bibliographic record
Abstract
This third edition (2023) continues to reflect the Edge service roadmap and journey with evolving key drivers and developments. It replaces the two earlier versions. 5G deployments have reached critical markets and are fast evolving towards 6G promising higher performance and near sub-millisecond latency. Higher automation in use cases fueled by AI (e.g. like ChatGPT) is expected to be the core of future applications and services. The service providers have started re-focusing on use cases that can leverage the new and improved capabilities for better average revenue per unit (ARPU) for both consumers and enterprises. Some of the edge relevant Service oriented use cases are based on key constraints (low latency, high throughput, low jitter) ▪Industrial Internet of Things (IIoT) Industry 4.0 ▪Vehicle to anything - V2X (autonomous vehicle / intelligent transportation / traveling edge) ▪Telehealth / telemedicine / remote diagnostics ▪Content delivery (with caching, real time, rich media internet applications) ▪Ad hoc, temporary or on as needed mission specific edge services (emergency, ad hoc major events, DoD combat mission, and more) ▪Enabling sustainable development edge applications. Key elements of the edge-inspired infrastructure to support such services may be noted as follows: ▪Enabling edge with AIML, e.g., OpenAI / *ChatGPT ▪Radio-based Multi-Access Edge (MEC) to provide different services ▪Real-time / near real time enabled radio control, management, and xApps based on O-RAN ▪Constraint-based edge infrastructure to optimize power, form factor, and updates to NFV-SDN with the IoT middleware standards OneM2M from ETSI to cover IoT applications ▪The focus on data privacy, security, data “localization and analytics” ▪Intelligent edge microservices and applications deployed using newer infrastructure components from Semiconductor innovations e.g. DPU, IPU, etc. ▪MEC and 5G integration to support location-based edge platform API adaption ▪Small cells and private B5G networks We classify platform aspects with Edge Platform Framework (EPF) and Services with Edge Service Framework (ESF) updates and overall aspects as Edge Service and Platform Framework (ES&PF) for an integrated view.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".