Spatio-Temporal Provisioning in Multi-UAV MEC With Pricing Games and Evolutionary Optimization

Abstract

Spatio-temporal volatility of mobile demand and the surge of computation-intensive, delay-sensitive applications pose significant challenges to cost-aware Mobile Edge Computing (MEC) deployments. This paper investigates a multi-UAV-assisted MEC framework and proposes a hierarchical three-time-scale resource provisioning and allocation scheme. At the large time scale, we optimize long-horizon resource provisioning decisions to jointly minimize service degradation and resource cost. At the medium time scale, a spatio-temporal fluctuation model is established for urban users and a Future-Aware UAV Strategy (FAUS) is introduced to proactively dispatch UAV-mounted servers toward predicted hotspot regions. Meanwhile, we design a semi-competitive and semi-cooperative Stackelberg pricing game between stationary edge servers and UAV clusters to ensure individual rationality and induce effective workload splitting. At the small time scale, we implement a lightweight per-slot routine for price-aware offloadingand KKT-based resource allocation for efficient execution. To solve the resulting expensive black-box provisioning optimization, we develop an Environment Switching Evolutionary Optimization (ESEO) algorithm to accelerate convergence and generate diverse Pareto-optimal solutions, enabling flexible choices such as resource-saving MEC and performance-oriented MEC. Extensive evaluations based on a real urban mobility dataset demonstrate that the proposed scheme outperforms benchmark methods in reducing offload failures and delay while providing flexible cost–performance trade-offs.