Microservices for Reliable Safety-Critical Cellular IoT Systems – A Case Study
Bibliographic record
Abstract
Safety-critical cellular IoT systems require a high level of reliability. Due to the distributed and heterogenous nature of such systems, software applications are usually deployed on different platforms including IoT devices, on-premises servers, cloud, and edge nodes. Traditional monolithic software applications cannot provide the desired level of reliability and flexibility. The microservices architecture, on the other hand, may provide better scalability, reliability, and decentralization. Furthermore, when coupled with efficient container orchestration platforms, like Kubernetes, microservices architecture can improve further the reliability for safety-critical cellular IoT systems. In this paper, we look into the Tele-operated Driving (ToD) case study. It is a safety-critical cellular IoT system which requires a high level of reliability and availability. We propose an initial microservices based architecture for the ToD. We conduct experiments to evaluate the service availability of the proposed microservices based ToD. Although the microservices architecture has strong potential for safety-critical cellular IoT systems, the desired level of availability is not achieved. Advanced availability mechanisms/architectures are required to achieve the desired level of availability for safety-critical cellular IoT systems.
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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".