In recent years, with the rapid growth in the demand for online streaming services, video streaming platforms are becoming more and more popular, and users' demand for low latency and high-quality services is increasing. Therefore, in order to be able to allocate caching resources reasonably to serve as many user requests as possible, and reduce latency and energy consumption, the edge cooperative caching method based on delay and energy consumption balance in SDN based mobile edge computing is proposed. In the proposed caching method, firstly, the multilayer perceptron neural network is used to predict the video content requested by the mobile user. Secondly, an objective function to minimize delay and energy consumption is established, and an edge cache optimization model is constructed. Finally, the branch-and-bound algorithm is used to obtain the optimal edge cache strategy. Meanwhile, to provide users with seamless service migration to ensure service continuity and high-quality services, the dynamic service migration method based on deep Q learning is proposed. In the proposed service migration method, firstly, the service migration problem is expressed as a Markov decision process. Secondly, the service migration process is analyzed and a service migration reward function is constructed. Finally, deep Q learning is used to obtain the optimal service migration strategy. In the experiment, the proposed edge caching algorithm can effectively improve the cache hit rate, reduce the backhaul traffic load, and control the average access delay and energy cost. Moreover, the proposed service migration algorithm can effectively reduce the number of service migrations and transmission cost, improve the success rate of service migration, and reduce the average traffic consumed by migration.

近年来，随着在线流媒体服务需求的快速增长，视频流媒体平台变得越来越流行，用户对低延迟和高质量服务的需求也在不断增长。因此，为了能够合理地分配缓存资源以尽可能多地满足用户请求，并减少时延和能耗，提出了一种基于时延和能耗平衡的边缘协作缓存方法。在提出的缓存方法中，首先，使用多层感知器神经网络来预测移动用户请求的视频内容。其次，建立了最小化延迟和能耗的目标函数，并建立了边缘缓存优化模型。最后，分支定界算法用于获得最佳边缘缓存策略。同时，为了给用户提供无缝的服务迁移，以确保服务的连续性和高质量的服务，提出了一种基于深度Q学习的动态服务迁移方法。在提出的服务迁移方法中，首先，将服务迁移问题表示为马尔可夫决策过程。其次，分析了服务迁移过程，构建了服务迁移奖励函数。最后，通过深度Q学习获得最佳的服务迁移策略。在实验中，提出的边缘缓存算法可以有效提高缓存命中率，减少回程流量负载，并控制平均访问延迟和能耗。此外，