Offloading framework for computation service in the edge cloud and core cloud: A case study for face recognition
Bibliographic record
Abstract
Summary A fast rate of progress has allowed the proliferation of smartphones and eased their extensive presence in people's daily life. However, low processing speed and limited battery capacity have hindered improvements in the smartphone's computational capabilities. Offloading computational tasks to the cloud could solve this problem by enabling users to access these services over the Internet. Edge cloud computing has been recognized as an emerging field within the cloud computing paradigm, where computation servers are situated at the edge of the Internet to reduce network delay and traffic. Nevertheless, offloading tasks to the cloud is not always beneficial due to variable network conditions and increased processing costs. In this paper, a deep reinforcement learning‐based offloading framework has been presented that provides smartphones with the ability to make decisions for local processing in the smartphone or to offload processing tasks to the cloud (edge and/or core). Thus, a smartphone can minimize the combination of the processing time, energy consumption, and monetary cost and maximize the accuracy of face recognition as well. Simulation results under synthetic scenarios show that the proposed offloading framework can effectively adapt to the dynamic cloud computing and networking environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".