A Fast and Efficient Task Offloading Approach in Edge-Cloud Collaboration Environment
Bibliographic record
Abstract
Edge-cloud collaboration fully utilizes the advantages of sufficient computing resources in cloud computing and the low latency of edge computing and better meets the needs of various Internet of Things (IoT) application scenarios. An important research challenge for edge-cloud collaboration is how to offload tasks to edge and cloud quickly and efficiently, taking into account different task characteristics, resource capabilities, and optimization objectives. To address the above challenge, we propose a fast and efficient task offloading approach in edge-cloud collaboration systems that can achieve a near-optimal solution with a low time overhead. First, it proposes an edge-cloud collaborative task offloading model that aims to minimize time delay and resource cost while ensuring the reliability requirements of the tasks. Then, it designs a novel Preprocessing-Based Task Offloading (PBTO) algorithm to quickly obtain a near-optimal solution to the Task Offloading problem in Edge-cloud Collaboration (TOEC) systems. Finally, we conducted extended simulation experiments to compare the proposed PBTO algorithm with the optimal method and two heuristic methods. The experimental results show that the total execution time of the proposed PBTO algorithm is reduced by 87.23%, while the total cost is increased by only 0.0004% compared to the optimal method. The two heuristics, although better than PBTO in terms of execution time, have much lower solution quality, e.g., their total costs are increased by 69.27% and 85.54%, respectively, compared to the optimal method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".