Unmanned aerial vehicles (UAVs)-assisted edge computing has emerged as an effective solution for providing contingency task offloading services when ground computing infrastructures are insufficient. However, UAVs face challenges in implementing efficient task offloading strategies due to their limited capabilities and the complexity of the privacy offloading problem. To address these challenges, this study constructs a digital twin (DT)-enabled UAV swarm-assisted secure computing model, which considers collaboration of devices, edges, and cloud resources. The model is designed to represent the three-tier computing environment as a DT virtual framework, allowing for the monitoring of network changes and the exploration of potential strategies. Furthermore, a joint optimization problem that considers time delay and energy consumption within encryption and decryption costs is formulated. To solve this problem, an entropy-enhanced proximal policy optimization-based multi-UAV assisted security-aware task offloading (EP-MUSTO) algorithm is proposed. In EP-MUSTO, the exploration capability is enhanced by utilizing an actor network with policy entropy, and the action cognition is improved through the parameterization of the hybrid action space. Experimental results demonstrate that compared with other advanced algorithms, EP-MUSTO achieves a reduction in security system costs and magnitude of convergence oscillations by at least 9.43% and 54.62%, respectively.

Cloud computing, as a cutting-edge computing paradigm, offers substantial data processing and storage capabilities. In a heterogeneous cloud environment, the diversity among cloud platforms results in varying task execution times, posing challenges in minimizing workflow makespan under budget constraints. On this basis, a novel meta-heuristic optimization algorithm, named snake optimizer (SO), is proposed for workflow scheduling in the cloud. Then, a negative offset mechanism is designed to dynamically guide the offset of individual positions during population update to prevent falling into local optimums, thereby optimizing the search for feasible solutions and improving the success rate. Finally, using the negative offset mechanism, a snake optimization budget-aware scheduling algorithm (NO-SO) is developed to schedule budget-constrained workflows in heterogeneous cloud computing environments and minimize the makespan. A series of comparative experiments conducted on real-world scientific workflows demonstrates that the NO-SO algorithm enhances the success rate in finding a feasible solution by 38.89% and 34.45% compared with the advanced MG-PRO algorithm and the original SO algorithm, respectively. Moreover, it achieves an average reduction in makespan of 30.30% and 32.19%.

Mobile edge computing effectively enhances service quality and decreases system cost by processing resource-intensive tasks at the network edge. Today, unmanned aerial vehicles (UAVs) are increasingly being utilized for task offloading services in remote areas due to their convenient deployment and flexible mobility. However, the complex task environment when using UAVs brings great challenges to the optimization strategy’s capacity to solve and converge in a stable manner. To solve this issue, a differential rate rule (DRR) is proposed in this work with the goal of improving the update stability of the agent in the actor–critic reinforcement learning (RL). Second, a UAV-assisted task offloading algorithm called DSUTO is designed based on DRR and maximum entropy RL. Finally, a UAV-assisted mobile device-edge-cloud collaborative computing model is constructed with time-varying channel obstacles and user movement, thus solving a multi-objective joint optimization problem on the task completion cost (including delay and energy consumption) and UAV endurance under resource constraints. The experiment results demonstrate that DSUTO not only has excellent performance in terms of convergence and stability, but also significantly reduces the total system cost by 21.38% compared with the latest benchmark algorithms under complex environment conditions.