Mobility-Aware Computation Offloading with Adaptive Load Balancing in Small-Cell MEC
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Bibliographic record
Abstract
Mobile edge computing (MEC) is a promising computing paradigm enabling mobile devices to offload computation-intensive tasks to nearby edge servers for fast processing. In this paper, we investigate the computing task offloading in small-cell MEC systems. Considering the unevenly distributed mobile users, it is critical to balance the computing load among edge servers to better utilize the computing resources. To this end, we formulate a joint task offloading control and load balancing problem to minimize the average computational cost of users. The formulated problem is a mixed-integer nonlinear optimization problem and is intractable with system scale. To solve the problem in real time, we propose a reinforcement learning-based grouping and task offloading control (RLGTC) scheme. Specifically, we first decompose the problem into two sub-problems with the Tammer method, i.e., the task offloading control (ToC) and server grouping (SeG) sub-problems. Then, we devise two algorithms based on the Kalman Filter technique and reinforcement learning with Dueling Double DQN to solve them, respectively. Extensive data-driven experiments demonstrate the effectiveness of the RLGTC scheme in achieving load balancing and reducing UEs’ computational costs compared to the state-of-the-art benchmarks.
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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 | 0.003 | 0.001 |
| 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