Memory failures in microservices based Cellular IoT systems - An experimental evaluation of service availability
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".