Dependence-Aware Multitask Scheduling for Edge Video Analytics With Accuracy Guarantee
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we investigate the optimal configuration and dependence-aware task assignment for multi-task edge video analytics. Multi-task video analytics involves multiple objects in video frames and multiple dependent tasks, resulting in existing video configuration and task assignment scheme for single-task unsuitable to this scenario. Our paper aims to efficiently assign dependent tasks to multiple collaborative edge nodes with appropriate video configuration, to achieve low latency while maintain accuracy. Firstly, we conduct extensive experiments on real-world video datasets. The results reveal that the impact of resolution on the detection accuracy varies among different sizes of objects. Moreover, the computing and communication load of dependent tasks varies along the time due to the dynamic video content. Based on the experimental results, we propose a threshold-based downsampling strategy for large objects, aiming at minimizing the transmission latency while guaranteeing task analytic accuracy. In addition, the number of objects and workload of subsequent tasks turn out to be highly correlated, the computation and transmission demands of tasks can be thus estimated for each video chunk. Then, a heuristic dependence-aware task assignment algorithm is proposed to achieve minimum completion time of dependent tasks. Experimental results demonstrate that the proposed scheme can effectively reduce the execution time of multiple tasks while guaranteeing the analytic accuracy, outperforming 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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 it