Online Dependency-aware Task offloading in Cloudlet-based Edge Computing Networks
Bibliographic record
Abstract
The demand for low-latency processing in mobile devices is dramatically increasing. However, mobile devices inherently lack the capacity to handle heavy and low-latency processing. Edge computing techniques, which offload the tasks of user applications to a nearby server, are being used to mitigate this problem. Moreover, applications requiring rapid processing have evolved, becoming more complex. Application tasks are no longer merely computational and independent; each task requires its associated libraries and dependencies, all of which must be considered during offloading. The dependency between tasks should also be taken into account. Centralized decision-making for offloading for a large number of users is not practically feasible. In response to this issue, we designed a distributed method based on deep reinforcement learning. We defined the states of the learning agent in a manner that enables users to learn about the environment with respect to the level of task dependency. Through simulation, we demonstrate that the proposed algorithm surpasses existing benchmarks in terms of application completion time by identifying the optimal server for offloading dependent tasks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".