Research On Dynamic Aware And Collaborative Aggregation Of Mobile Edge Resources

With the rapid development of the mobile Internet,mobile infrastructure equipment is becoming more and more complete,and the computing power,storage capacity,information response capability and security protection capability of mobile devices are becoming stronger and stronger,and mobile edge resources are becoming more and more abundant.In order to realize the full utilization of mobile edge resources and the timely and efficient processing of mobile services,Mobile Edge Computing(MEC)came into being.However,the dynamic,disordered and local characteristics of mobile networks lead to a serious decline in the effectiveness of the acquired mobile network resources,which makes mobile services processing delay is still long.Therefore,how to solve the problem of resource management of mobile edge network,realize the effect of mobile edge computing to balance network load,reduce the delay of mobile network service processing,and improve the utilization of mobile edge resources has become one of the key concerns of the academic community.Based on the perception and aggregation of mobile edges,this paper deeply studies the dynamic perception and collaborative aggregation of mobile edge resources,aiming at realizing dynamic collaborative management of mobile edge resources,facilitating coordinated scheduling and sharing of resources,improving the utilization of mobile edge resources,and reducing mobile as much as possible.Business processing delay.The research focus of the article is as follows:(1)In view of the existing mobile edge resource awareness technology is mostly task-centric,the dominant needs of service objects as the basis for resource search.There is a lack of service-oriented,and the analysis of the implicit requirements.A dynamic perception model of mobile edge resources is constructed.The model is centered on the service object and analyzes the implicit requirements of the user.The research mainly includes two parts:resource description and pre-aggregation.Firstly,the characteristics of resource are analyzed,and then the process of implicit demand analysis is described,and the multi-situations analytic hierarchy process is proposed as the basis of resource selection in pre-aggregation of resources.Finally,a case study is given: compared with the common resource awarenesstechnology,which is known as perceptual storage / reservation,the user-centered resource awareness technology will not affect the efficiency of resource awareness.At the same time,it can save resource storage space to a certain extent.(2)Aiming at the problem of mobile edge resource mobility and frequent resource failure,it affects the execution of mobile network services.In this chapter,a collaborative aggregation model of resources based on MEC awareness is constructed.In this paper,based on the technologies of resource on demand aggregation and resource prediction aggregation,a resource cooperative aggregation strategy based on MEC awareness is proposed,and an architecture of resource aggregation strategy based on MEC awareness is constructed.Then several important resource elements are defined and the building process of resource collaborative aggregation model is described.Finally,the simulation results show that the model can facilitate the scheduling and sharing of resources and reduce the processing delay of mobile services.(3)Based on the above content,the prototype of the resource aggregation subsystem of the mobile service platform is constructed,and the dynamic perception and collaborative aggregation model of the mobile edge resource is further verified.According to the theoretical content,the functional structure of the platform is designed,the functional flow of the platform and related parameters are analyzed.The model algorithm proposed in the paper is embedded into the platform,and the virtual mobile device platform is used to realize the engineering verification of the model.Finally,through the engineering subsystem and mobile virtual platform experiments,it is verified that the proposed model achieves the purpose of reducing the delay of network service processing and improving the utilization of network resources to a certain extent.