Mobile edge computing, which enhances the computing power of mobile devices via computation offloading technology, has been proposed as a promising paradigm to alleviate the mismatch between limited computational resources and ever-increasing computational requirements. An appropriate offloading strategy must be identified to optimize the computation offloading performance. Moreover, a mobile user is generally a selfish individual; therefore, we should consider the fairness problem when determining the offloading strategy. In this paper, we focus on computation offloading in a cloud-assisted edge computing system with multiple users, an edge server and a cloud server and aim to optimize the response time for each user. A fairness-oriented approach, including the determination of the application offloading strategy, the data transmission strategy and the cloud–edge cooperation strategy, is proposed. Initially, the computation offloading problem is formulated as a noncooperative game. Then, we find the optimal cloud–edge cooperation strategy and the optimal data transmission strategy based on a theoretical analysis and propose an iterative algorithm to identify the optimal offloading strategy for each user. Finally, we evaluate the proposed approach by means of extensive experiments, and the experimental results show that the proposed approach can significantly reduce the response time of mobile devices.

移动边缘计算通过计算卸载技术增强移动设备的计算能力，已被提出作为缓解有限计算资源与不断增加的计算需求之间不匹配的有前途的范例。必须确定适当的卸载策略以优化计算卸载性能。此外，移动用户通常是一个自私的人；因此，我们在确定卸载策略时应考虑公平性问题。在本文中，我们专注于具有多个用户、边缘服务器和云服务器的云辅助边缘计算系统中的计算卸载，旨在优化每个用户的响应时间。一种面向公平的方法，包括确定应用程序卸载策略，提出了数据传输策略和云边合作策略。最初，计算卸载问题被表述为一个非合作博弈。然后，我们在理论分析的基础上找到了最优的云边协作策略和最优的数据传输策略，并提出了一种迭代算法来确定每个用户的最优卸载策略。最后，我们通过大量实验评估了所提出的方法，实验结果表明所提出的方法可以显着减少移动设备的响应时间。我们在理论分析的基础上找到了最优的云边协作策略和最优的数据传输策略，并提出了一种迭代算法来确定每个用户的最优卸载策略。最后，我们通过大量实验评估了所提出的方法，实验结果表明所提出的方法可以显着减少移动设备的响应时间。我们在理论分析的基础上找到了最优的云边缘协作策略和最优的数据传输策略，并提出了一种迭代算法来确定每个用户的最优卸载策略。最后，我们通过大量实验评估了所提出的方法，实验结果表明所提出的方法可以显着减少移动设备的响应时间。