A distributed multi-level model with dynamic replacement for the storage of smart edge computing

With the development of Internet of things (IoT), billions of sensors, actuators and other intelligent terminal devices (ITDs) are connected to the Internet. In this situation, traditional cloud computing models are not fully suitable because these ITDs generate too much data which may cause severe network congestion. As a result, edge computing is proposed to solve this problem, which can provide computation and storage services for ITDs with a distributed model on the edge networks. While, this novel edge paradigm brings emerged problems of restricted computation, limited storage and unstable network, as the storage is distributed typically as a key role. So, in this paper, it proposes a distributed multi-level storage (DMLS) model with a multiple-factors least frequently used (mLFU) algorithm to solve the problem. In this model, storage levels are composed of ITDs on the edge, so when the storage space of a node is not enough, the mLFU is used to remove a part of data from the current nodes and upload the date to the upper storage levels. To reduce the impact of data loss caused by unstable edge networks, a factor of importance is introduced in the mLFU, which means that data with high importance is uploaded to the upper levels first. Experiments show that the hit rate of the mLFU is stabilized at 74% that is almost equal to the typical LFU, while the important data loss rate is about 35% lower than the LFU and the random replacement algorithm.