Multi-view Learning Based Edge Storage Management Strategy

With the rapid growth of Internet of Thing(IoT) and mobile internet(4G/5G), there will be a huge volume of data generated by things that are immersed in our daily life, and hundreds of applications will be deployed at the edge to consume these data. Cloud computing as the widely used high performance processing center is not efficient enough to support these applications emerging in IoT and mobile Internet. It is mainly shown in the following two aspects, 1) Centralized cloud center is hard to deal with explosive growth of edge data, 2) Transmitting massive data from edge devices to cloud centers causes network load and service delay increasing dramatically. Therefore, edge computing for massive edge data appears, which collaboration with cloud computing to solve the above problems and provide low latency service. Edge storage as one of the main ways of resource management in edge computing is an important support for low latency service, which is based on the prediction of resources according to user's needs, and basing on user's location and the state of the edge nodes, the resources are prestored in different edge servers to reduce the edge load and improve the response time. In this paper, a edge storage method based on multi-view learning is proposed, it predicts the resources that need to be prestored through scenario demand based on multi-feature linear discriminant analysis, and multiple edge servers cooperate to store the above resources based on their real time performance and user's location. The experiments are carried out based on edge computing environment which distributed at three universities, and experimental results show that the proposed method is more effective than cloud storage when dealing with part of the mobile application.