Design and Performance Evaluation of Containerized Microservices on Edge Gateway in Mobile IoT
Bibliographic record
Abstract
Recently, Internet of Things (IoT) has drawn a great deal of attention and is envisioned in various sectors in the near future due to its promising benefits. However, the constant and rapid growth in IoT devices also brings new challenges due to constrained power and resources associated to them. One of the challenges is to provide seamless connectivity in mobile IoT. Secondly, IoT devices may stream enormous amount of data; hence, providing a solution that can effectively reduce service cost of data transfer. Finally, there are challenges in management and deployment of services running at mobile IoT Edge Gateway. In this context, containerized virtualization solution could play a key role in support of efficient management and deployment of microservices to provide seamless connectivity. This paper proposes a lightweight container-based virtualization technology for IoT, which employs Docker container-based microservices architecture for effectively deploying applications in a virtualized ecosystem. We evaluated the performance of the proposed solution on real IoT testbed using Raspberry Pi 3 as a mobile IoT Edge Gateway for network handover decision making among various alternatives, such as Wi-Fi, Radio, and Satellite. The results demonstrated better performance compared with the native environment, i.e., the one without introduction of a virtualization layer. The results also showed that the Docker container produces negligible resource overhead and can be used on resource constrained mobile IoT Edge Gateway devices like Raspberry Pi 3 for efficiently managing IoT application and services.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".