计算机科学
隐藏物
延迟(音频)
移动边缘计算
服务质量
边缘计算
能源消耗
分布式计算
计算机网络
服务器
边缘设备
马尔可夫决策过程
缓存算法
GSM演进的增强数据速率
CPU缓存
马尔可夫过程
云计算
操作系统
电信
生态学
统计
数学
生物
作者
Chunlin Li,Yong Zhang,Xiang Gao,Yingwei Luo
标识
DOI:10.1016/j.jpdc.2022.03.001
摘要
Mobile edge computing sinks computing and storage capabilities to the edge of the network to provide reliable and low-latency services. However, the mobility of users and the limited coverage of edge servers can cause service interruptions and reduce service quality. A cooperative edge caching strategy based on energy-latency balance is proposed to solve high power consumption and latency caused by processing computationally intensive applications. In the cache selection phase, the request prediction method based on a deep neural network improves the cache hit rate. In the cache placement stage, the objective function is established by comprehensively considering power consumption and latency, and We use the branch-and-bound algorithm to get the optimal value. We propose an improved service migration method to solve the problem of service interruption caused by user movement. The service migration problem is modeled using a Markov decision process (MDP). The optimization goal is to reduce service latency and improve user experience under the premise of specified cost and computing resources. Finally, the optimal solution of the model is solved by the deep Q-Network (DQN) algorithm. Experiments show that our edge caching algorithm has lower latency and energy consumption than other algorithms in the same conditions. The service migration algorithm proposed in this paper is superior to different service migration algorithms in migration cost and success rate.
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