Online Partial Offloading and Task Scheduling in SDN-Fog Networks With Deep Recurrent Reinforcement Learning
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Bibliographic record
Abstract
Smart industries enabling automation and data exchange in manufacturing technologies demanding real-time processing, nearby storage, and reliability, all of which can be satisfied by the fog computing architecture. With the emergence of smart devices coupled with a diverse range of application requirements, it is essential to have an intelligent fog network where intelligence is spread across all network segments, taking network nodes self-aware and self-decision making. In fog networks, an optimal distribution decision faces challenges due to uncertainties associated with user workload and available resources at the fog nodes and also the wide range of node’s computing power. Given this challenge, a computational offloading and CPU resource scheduling method for minimizing energy consumption is proposed. To investigate the characteristics for offloading and optimizing their allocation, we consider two types of tasks, namely, offloadable and nonoffloadable tasks. The independent fog nodes adopt the same strategy without prior knowledge of the dynamic statistics and global observations, aiming to maximize a common goal with cooperative behaviors. Then, the deep recurrent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (DRQN) is applied to deal with the partial-observability from limited information. The proposed DRQN-based method requires comparatively less computational complexity than the conventional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm. The simulation results show that the proposed method can effectively deal with both transmission and CPU energy consumptions while guaranteeing convergence in a limited time.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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