FScaler: Automatic Resource Scaling of Containers in Fog Clusters Using Reinforcement Learning
Bibliographic record
Abstract
Several studies leverage fog computing as a solution to overcome cloud delays, including computation, network, and data storage. Along with the increase in demands for computing resources in fog infrastructures, heterogeneous fog devices are used towards forming highly available clusters. Existing approaches support the use of heterogeneous fogs and enable dynamic updates and management of services through containerization and orchestration technologies. However, none of the existing works proposed a proactive solution to horizontally scale these resources based on the IoT workload fluctuations, in addition to deciding on proper placement of the scaled instances on fogs with minimal cost on the fly. An effective scaling results in improving the response time and avoid service instability on fog devices. Therefore, we propose in this work FScaler, a reinforcement learning agent that horizontally scales container's instances after studying user's demands, and schedules the placement of newly created instances based on defined cost functions after studying the change in resources availability. The environment of FScaler is modeled as an MDP to be solved by any RL algorithm. For this work, we study the efficiency of our MDP formulation by solving the problem using SARSA. Promising results are shown through testing using a real-life dataset presenting the variation of user's demands of a particular service and the change in resource availability over time.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".