Edge Cache Application Research Based On User Central Point Access Context

The proliferation of mobile users and the rise of streaming media services have resulted in huge consumption of backhaul link bandwidth.To effectively solve this problem,the new 5G network architecture combining with mobile edge computing(MEC)is proposed to move content delivery to the mobile edge,so that requests can be served locally.Meanwhile,due to the limitation of the edge cache capacity,efficient and intelligent edge cache strategy has become a hot topic in this field.One characteristic of edge cache is that the content request is sensitive to the spatio-temporal mobility of the user.At the same time,the temporal locality of the content also results in the diversity of content requests on the mobile edge.Therefore,the study of spatio-temporal context of the user and the exploration of the diversity can potentially increase the efficiency of edge cache policy.The main achievements of this paper are listed as follows:1.Content request on the mobile edge is affected by the user's spatio-temporal mobility and shows diversity,Thus this paper uses the contextual multi-armed bandit problem to model the edge cache problem.Due to the time-variation of content popularity and the NP-hardness of the solution,we proposes an online edge caching strategy based on user central point access context(OCUC)and elaborates on OCUC's three main modules:user central point feature construction module,group context construction module,and context-specified online exploration module.2.Based on the central point effect in the spatio-temporal characteristics of user on the mobile edge,this paper proposes a central point feature transform algorithm to convert the traditional user spatio-temporal features into user central point feature.Moreover,we uses distance-correlation coefficients(DC)to analyze the correlation between user features and user interests.The result shows that the correlation between central point feature and interests is higher.3.The content cache is influenced by the user group's interest preference.To solve the problem of uncertainty of user population and effectiveness of group context evaluation on user group's preference context construction,this paper proposes a group preference context construction method(GCNN)based on convolutional neural network.Furthermore,the influence of the number of user central point,the number of convolution kernels,and the output dimension of the full-connected layer is analyzed.4.Considering the problem that the context space is a continuous space and the over-exploration caused by the large content space in the real scene,this paper proposes a contextual zooming algorithm(CZ),which zooms in the popular context area to maintain finer-grained relationship between the context and the content preference.5.Based on the real China Mobile User Detail Record(UDR),this paper compares OCUC and other edge cache update algorithms from cache hit rate,algorithm stability and operating efficiency.The results show that OCUC is superior to all comparison algorithms in terms of cache hit rate and algorithm stability.In terms of operating efficiency,OCUC takes slightly more time than MPC,which means OCUC will not cause system overload when applying to real scenes.The OCUC strategy proposed in this paper can effectively learn the relationship between the user's spatio-temporal context and content preference,realize the sharing of experience among the base stations,and thus effectively reduce the backhaul link bandwidth consumption.The research in this paper can not only provide a new insight for the design of edge caching strategy,but also has practical value.