Cost Minimization Resource Allocation with Service Instance Caching and Task Migration for UAV Mobile Edge Computing
Bibliographic record
Abstract
Air-ground integrated Mobile Edge Computing (MEC) is emerging as a promising technology to achieve the ITU 6G vision of ubiquitous connectivity. Thus, this paper integrates UAVs with ground base stations to provide connectivity for mobile users and to process their computation-intensive and delay-sensitive tasks. However, the limited storage capacity of UAV-side servers makes it infeasible to cache all types of service instances for various tasks. Additionally, the mismatch between server computing resources and user offloading demands leads to insufficient resource utilization, which ultimately deteriorates service delay. Meanwhile, the finite energy of UAVs also calls for reducing energy consumption. Therefore, we aim to minimize the cost, namely the weighted sum of total delay and energy consumption, through joint optimization of the UAV 3D hovering position, service instance caching, task migration, and computing resource allocation. Note that, this problem is characterized as a mixed-integer nonlinear programming (MINLP) issue with dynamic and uncertain system states. As such, we propose a two-stage approach to tackle it. Specifically, in the first stage, we develop an Asynchronous Advantage Actor-Critic (A3C)-based algorithm to design the service instance caching, task migration, and computing resource allocation, enabling multi-thread parallel environment interaction and policy training. In the second stage, UAV 3D hovering positions are optimized using successive convex approximation. Ultimately, we combine the two stages to obtain a high-quality solution. Simulation results demonstrate that our approach reduces the cost by 22.24% and 8.51% compared to DQN and DDPG while converging more quickly and stably.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".