FLoadNet: Load Balancing in Fog Networks With Cooperative Multiagent Using Actor–Critic Method
Why this work is in the frame
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Bibliographic record
Abstract
The growing demands of the Internet of Things (IoT) require a platform that supports real-time interactions and high availability of services to devices. In this context, the fog computing paradigm has emerged as an attractive solution for processing the data of IoT applications. Owing to the unpredictable traffic demands and resource heterogeneity in the fog environment, a smart workload distribution is essential to achieve high resource utilization and computing efficiency. To this end, this paper considers a joint link and server load balancing problem with multiple cooperative access points, (APs), in a combined edge-fog-cloud environment. The joint optimization problem is formulated as a stochastic game, and an actor-critic reinforcement learning framework, called FLoadNet, is proposed to optimize the joint policy of the multi-agents. FLoadNet consists of a centralized critic network, with parameter sharing and distributed individual actor networks in all the APs. Due to the learning dynamics and partially observable environment, we propose an extended critic network model, where cooperative APs learn to communicate among themselves while evaluating the value function. Unlike previous studies, the proposed critic network is designed to train both value and message functions, which is shown to significantly reduce the computational cost. The main goal of this work is to advance the development of efficient edge learning and the application of distributed learning algorithms specifically to fog network load balancing. The experimental results show that FLoadNet outperforms baseline load balancing methods.
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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 | 0.001 | 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 | 0.001 | 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 it