Memory failures in microservices based Cellular IoT systems - An experimental evaluation of service availability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ensuring service availability for large-scale distributed systems, like IoT systems, has always been a challenge. Some IoT systems are safety-critical, a service outage could lead to severe damage or fatality, and therefore demand high-availability to ensure reliability and continuity of service. Microservice architecture combined with Kubernetes orchestrator have become a popular approach to achieve high-availability in such type of systems. However, while these architectures provide scalability and quick recoverability from many types of failures, their effectiveness is limited when addressing memory-related application failures. In this paper, we present an experimental evaluation of the service availability provided by microservice architectures deployed on Kubernetes in scenarios involving memory-related failures, through a case study on Tele-operated driving, which is a safety-critical cellular IoT use case. Our findings indicate that Kubernetes lacks robustness when confronted with memory-related faults, leading to extended recovery times and service disruptions. Therefore, advanced fault-tolerance mechanisms are required to better support high-availability requirements in safety-critical cellular IoT systems.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it