Collaborative Container-based Parked Vehicle Edge Computing Framework for Online Task Offloading
Bibliographic record
Abstract
As most vehicles spend over 95% of their time in the parking lots, the powerful computing resources of parked vehicles (PVs) are underutilized, that can be considered as available computing nodes to run tasks as well as an extension of the existing infrastructure. In this paper, we propose EdgePV, a collaborative computing paradigm to efficiently improve online heterogeneous task scheduling. To guarantee service reliability, a container orchestration (e.g. Kubernetes) is advocated to be integrated into this proposed architecture due to its notable advanced features such as load-balancing, auto-healing, resource isolation, security, etc,. Kubernetes coordinates PVs to run sufficient numbers of task replicas, providing high service availability against possible failure caused by the mobility of PVs. We investigate how efficient PVs can handle the online computational tasks during peak hours. We also present the dual cost and utility-aware heuristic algorithm, compared with a set of heuristics to solve the problem of task scheduling, that can be devised for replacing the default scheduler in Kubernetes platform. Extensive simulation results show that our proposed design improves the task acceptance ratios and average costs at least 23% and 64%, respectively, at lowest task arrival rate compared to the cooperated cloud-edge architecture. Furthermore, owners of PVs can significantly benefit from incentives received by sharing the idle resources of their PVs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".