SAVI-IoT: A Self-Managing Containerized IoT Platform
Bibliographic record
Abstract
Internet of Things (IoT) as a service is the ultimate goal of employing cloud computing paradigm for initiating IoT application scenarios. Due to the nature of IoT ecosystems, an IoT application should be distributed, programmable and autonomic; also, it requires to support heterogeneity, security and privacy by following design patterns involved in creating IoT systems. A multi-layer cloud architecture comprising of a high-capacity core center that is connected, through high speed links, to geographically distributed smart edges seem appropriate for highly distributed and heterogeneous IoT applications. Building upon our previous initiatives and inspired by the Infrastructure as Code (IoC) paradigm, in this paper, we propose and evaluate a hierarchical, programmable and autonomic IoT platform based on the microservice models. Our platform supports big data, local/edge data processing, high level of programmability and runtime autonomic management. The autonomic management system ensures the service availability, quality of service and optimized resource utilization in the whole IoT application components autonomously. The primary results affirm a promising future of our platform toward realization of IoT as a service.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".