IoT Management with Container Orchestration
Bibliographic record
Abstract
Internet of Things (IoT) systems manage microcontroller devices through cloud infrastructure. The modern cloud infrastructure is composed of containerized applications that facilitate internal and external communications. Containers have been used in recent years in a wide range of applications. Containerization of applications and services allows improved modularity for traditional monolithic applications. Combined with IoT, containerization allows efficient allocation, fast execution, and deployment of hardware resources. Docker is the most popular and open-source containerization tool currently used in the field. However, the need to manage these various containers warranted the advent of container orchestration technologies to facilitate the deployment, status monitoring, and scaling of containerized applications. In this study, the container management engine Kubernetes was implemented through the Google Cloud Platform to create an IoT infrastructure composed of containers. The infrastructure used the Message Queuing Telemetry Transport (MQTT) protocol for machine-to-machine (M2M) communications. The design of the IoT infrastructure also involved system architecture trade-off analysis of architectural alternatives. The process improves both functional and nonfunctional requirements such as reliability, maintainability, and scalability. The implemented IoT infrastructure in potential applications and reliable for failure scenarios had containers and container orchestration in the Google Kubernetes Engine. The orchestration infrastructure facilitates efficient and effective management of IoT devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".