The traditional multi-access edge computing (MEC) capacity is overwhelmed by the increasing demand for vehicles, leading to acute degradation in task offloading performance. There is a tremendous number of resource-rich and idle mobile connected vehicles (CVs) in the traffic network, and vehicles are created as opportunistic ad-hoc edge clouds to alleviate the resource limitation of MEC by providing opportunistic computing services. On this basis, a novel scalable system framework is proposed in this paper for computation task offloading in opportunistic CV-assisted MEC. In this framework, opportunistic ad-hoc edge cloud and fixed edge cloud cooperate to form a novel hybrid cloud. Meanwhile, offloading decision and resource allocation of the user CVs must be ascertained. Furthermore, the joint offloading decision and resource allocation problem is described as a Mixed Integer Nonlinear Programming (MINLP) problem, which optimizes the task response latency of user CVs under various constraints. The original problem is decomposed into two subproblems. First, the Lagrange dual method is used to acquire the best resource allocation with the fixed offloading decision. Then, the satisfaction-driven method based on trial and error (TE) learning is adopted to optimize the offloading decision. Finally, a comprehensive series of experiments are conducted to demonstrate that our suggested scheme is more effective than other comparison schemes.

With the booming development of the Internet of Things, mobile edge computing of UAVs, as an emerging computing paradigm, offloads intensive tasks to network edge servers, thereby improving user data processing capacity. This paper designs a service caching and resource allocation algorithm that combines the quantum genetic algorithm and the traditional algorithm to address the needs of diversified and different prioritized user application services. Taking into account storage, computation, and energy constraints, the maximum user satisfaction and minimum service placement cost are achieved by jointly optimizing service caching, user offloading policy, time slot allocation, computational resource allocation, and flight trajectory. Specifically, the original problem is decomposed into three subproblems. First, the subproblem of service caching and user offloading is solved based on the quantum genetic algorithm. Second, the closed-form optimal solution for computational resource allocation is obtained based on the Lagrangian duality function. Then, the subproblem of time slot allocation and UAV trajectory optimization is solved using the successive convex approximation technique. Finally, the three subproblems are iterated several times to obtain their optimal solutions. The simulation results show that the algorithm can satisfy the diversified needs of users well, and can also have low services caching.