Vehicular computation offloading is a well-received strategy to execute delay-sensitive and/or compute-intensive tasks of legacy vehicles. The response time of vehicular computation offloading can be shortened by using mobile edge computing that offers strong computing power, driving these computation tasks closer to end users. However, the quality of communication is hard to guarantee due to the obstruction of dense buildings or lack of infrastructure in some zones. Unmanned Aerial Vehicles (UAVs), therefore, have become one of the means to establish communication links for the two ends owing to its characteristics of ignoring terrain and flexible deployment. To make a sensible decision of computation offloading, nevertheless vehicles need to gather offloading-related global information, in which Software-Defined Networking (SDN) has shown its advances in data collection and centralized management. In this paper, thus, we propose an SDN-enabled UAV-assisted vehicular computation offloading optimization framework to minimize the system cost of vehicle computing tasks. In our framework, the UAV and the Mobile Edge Computing (MEC) server can work on behalf of the vehicle users to execute the delay-sensitive and compute-intensive tasks. The UAV, in a meanwhile, can also be deployed as a relay node to assist in forwarding computation tasks to the MEC server. We formulate the offloading decision-making problem as a multi-players computation offloading sequential game, and design the UAV-assisted Vehicular computation Cost Optimization (UVCO) algorithm to solve this problem. Simulation results demonstrate that our proposed algorithm can make the offloading decision to minimize the Average System Cost (ASC).

Due to the limited local computation resource, industrial vehicular computation requires offloading the computation tasks with time-delay sensitive and complex demands to other intelligent devices (IDs) once the data is sensed and collected collaboratively. This article considers offloading partial computation tasks of the industrial vehicles (IVs) to multiple available IDs of the industrial mobile edge computing (MEC), including unmanned aerial vehicles (UAVs), and the fixed-position MEC servers, to optimize the system cost including execution time, energy consumption, and the ID rental price. Moreover, to increase the access probability of IV by the UAVs, the geographical area is divided into small partitions and schedule the UAVs regarding the regional IV density dynamically. A minimum incremental task allocation algorithm is proposed to divide the whole task and assign the divided units for the minimum cost increment each time. Experimental results show the proposed solution can significantly reduce the system cost.

The problem of task allocation of UAV systems is a hot issue in scientific research. Aiming at overcome the shortcomings of existing algorithms in terms of load balancing and execution efficiency, an algorithm Improved Self-Organizing Mapping (ISOM) is proposed in this paper. Firstly, considering the two factors of the UAV flight distance and the mission execution time, the load balance of the UAV is designed. The sum of the track lengths of multiple UAVs and the total time required by multiple UAVs to complete all the tasks in the task area are used to evaluate the quality of cooperative task assignment of multiple UAVs. Secondly, the learning rate and neighborhood function of nonlinear change are designed to ensure the stability and accelerate its convergence. With the increase of the number of iterations, the radius of the neighborhood gradually decreases, and the renewal range of the superior neighborhood is determined by the nonlinear function. Finally, in order to solve the problem of new tasks of UAV and UAV failure, the attention mechanism based on the ISOM algorithm is introduced. Different attention is allocated by the distance between the UAV and the mission points to ensure the missions can be fully executed. Compared with algorithms PSO-GA, Gurobi and ORTools, the time to complete the task is effectively reduced, respectively. At the same time, the algorithm is verified in a large task environment. When the number of tasks is 200 and the number of UAVs is 8, in the TSPLIB task set, the simulation results demonstrate the high efficiency and flexibility of the proposed algorithm.

With offloading the tasks that mobile users (MUs) running in their mobile devices (MDs) to the data centers of remote public clouds, mobile cloud computing (MCC) can greatly improve the computing capacity and prolong the battery life of MDs. However, the data centers of remote public cloud are generally far from the MUs, thus long delay will be caused due to the transmission from the base station to the public clouds over the Internet. Mobile edge computing (MEC) is recognized as a promising technique to augment the computation capabilities of MDs and shorten the transmission delay. Nevertheless, compared with the traditional MCC and MEC generally has a limited number of cloud resources. Therefore, making a choice on offloading task to the MCC or MEC is a challenging issue for each MU. In this paper, we investigate service selection in a mobile cloud architecture, in which MUs select cloud services from two cloud service providers (CSPs), i.e., public cloud service provider (PSP) and an edge cloud service provider (ESP). We use M/M/$\infty $ queue and M/M/1 queue to model PSP and ESP, respectively. We analyze the interaction of the two CSPs and MUs by adopting Stackelberg game, in which PSP and ESP set the prices first, and then the MUs decide to select cloud services based on performances and prices. In particular, we study the relationship between PSP and ESP in the simultaneous-play game (SPG) scenario, in which they compete to set prices of their cloud services simultaneously. Our numerical results show that MUs prefer to select service from the edge cloud if the number of tasks they run is small. In another hand, more tasks will be offloaded to the remote public cloud if the number of tasks they run becomes large.

As a new computing paradigm, cloud computing has received much attention from research and economics fields in recent years. Cloud resources can be priced according to several pricing options in cloud markets. Usage-based and reserved pricing schemes are commonly adopted by leading cloud service providers (CSPs) such as Amazon and Google. With more and more CSPs entering cloud computing markets, the pricing of cloud resources is an important issue that they need to consider. In this paper, we study how to segment cloud resources using hybrid pricing schemes in order to obtain the maximum revenue by means of optimal pricing schemes in what is a largely monopolized cloud market. We first study how the revenue of a cloud provider can be maximised using an on-demand pricing scheme. We then turn to the study of revenue maximization with a reserved pricing scheme and, finally, we compare the revenues obtained from the two pricing schemes.