Rule based lightweight approach for resources monitoring on IoT Edge devices
Bibliographic record
Abstract
Edge computing is a recent tendency in the IoT domain that contributes to reduce the reliance on Cloud. This is due to the cost efficiency and the improvement of the system responsivity since services are deployed close to the data source as well as actuator nodes. However, IoT services are created on the top of a diverse and heterogeneous stack of technologies in terms of their representative hardware components. Thus, leading to some kind of barrier to accomplish system interoperability. To mitigate this heterogeneity issue, lightweight virtualization techniques such as containers have been widely adopted for IoT service deployment. However, edge devices (e.g., gateway devices) are likely to be resource-constrained, which in some cases limits their abilities to support the deployment of multiple containers. In fact, this raises the need for an effective management of resources at the edge devices. To achieve that, tools for monitoring resources consumption of container based IoT systems at the network edge level, and to launch notifications in case of any change that requires intervention are of utmost necessity. However, the current efficient monitoring tools are greedy for computing and storage resources; especially when a considerable number of IoT microservices are needed to be deployed on the same gateway. Therefore, they do not meet the constraint of limited resources when scalability is required. This work proposes a lightweight rule-based monitoring approach, taking into consideration resources needed for the monitoring process, especially with the increased number of containers deployed on IoT gateways. Our evaluation shows a significant reduction in the communication of monitoring metrics.
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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 | 0.001 | 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".