The intensive mobile data traffic poses a great challenge for energy-constrained mobile devices. In the mobile edge environment, effective computing offloading and resource allocation can improve the service performance of edge computing systems. Therefore, a dynamic computation offloading model based on genetic algorithm is proposed in this paper. In this strategy, a task weight cost model based on processing delay and energy consumption is built, which can optimize processing delay and energy consumption simultaneously. Moreover, in view of the limited computing resources of edge servers, a resource allocation model based on utility maximization is proposed. In this strategy, the bidding strategies of users and edge nodes are studied and the resource is allocated to the high-unit bidding users based on the greedy strategy during the double auction process. A large number of experimental results show that the proposed computation offloading algorithm can significantly reduce task processing delay and energy consumption. For instance, the proposed offloading algorithm can save energy up to 14.81% and reduce processing delay up to 7.71% compared with the COPSO algorithm. Besides, the proposed resource allocation algorithm can promote the number of successful auction users and maximize the utility of the users and the edge nodes.

密集的移动数据流量对能耗有限的移动设备提出了巨大的挑战。在移动边缘环境中，有效的计算分载和资源分配可以提高边缘计算系统的服务性能。因此，本文提出了一种基于遗传算法的动态计算卸载模型。该策略建立了基于处理时延和能耗的任务权重成本模型，可以同时优化处理时延和能耗。此外，鉴于边缘服务器的计算资源有限，提出了一种基于效用最大化的资源分配模型。在这个策略中 研究了用户和边缘节点的竞价策略，并在二次竞价过程中基于贪婪策略将资源分配给高单位竞价用户。大量实验结果表明，本文提出的计算卸载算法可以显着减少任务处理的延迟和能耗。例如，与COPSO算法相比，所提出的卸载算法可以节省多达14.81％的能量，并减少多达7.71％的处理延迟。此外，所提出的资源分配算法可以增加成功拍卖用户的数量，并使用户和边缘节点的效用最大化。与COPSO算法相比，所提出的卸载算法可以节省多达14.81％的能量，并减少高达7.71％的处理延迟。此外，所提出的资源分配算法可以增加成功拍卖用户的数量，并使用户和边缘节点的效用最大化。与COPSO算法相比，所提出的卸载算法可以节省多达14.81％的能量，并减少高达7.71％的处理延迟。此外，所提出的资源分配算法可以增加成功拍卖用户的数量，并使用户和边缘节点的效用最大化。