Age Of Information Optimization Mechanism Of NFV Applications For Industrial Edge Computing

In the era of Industrial Internet,Industrial Edge Computing（IEC）adopts Service Function Graph（SFG）to provide agile and flexible customized services for Industrial Internet applications（NFV applications）based on Network Function Virtualization（NFV）.In SFG deployment,IEC must consider the data timeliness and granularity of NFV applications in order to achieve fast and accurate decision-making and control.Data timeliness is measured by the age of information（AoI）.Under normal circumstances,increasing the data sampling frequency can reduce the time interval between two adjacent data,prevent the data peak AoI from being too large,and realize refined perception of production equipment and environmental status.However,if the data sampling frequency is too high,the network will be congested and the queuing delay of data will be significantly increased,thereby increasing the average AoI of the data.In addition,the average data AoI of NFV applications will increase with the increase of SFG service delay.In response to these problems,this paper reduces the SFG service delay by optimizing the SFG deployment strategy,and adjusts the data sampling frequency to reduce the data queuing delay in the network,and finally achieves AoI optimization in coordination with SFG deployment and data sampling frequency adjustment.The specific work is as follows:（1）This paper proposes a distributed optimization deployment mechanism of SFG based on matching game theory.In this paper,the deployment process of SFG is regarded as the admission process of the university.Each SFG greedily hopes to be deployed on the optimal IEC server to obtain better service quality;each IEC server greedily selects the one that maximizes benefits SFG.This paper firstly analyzes the deployment preference of SFG request and the service preference of IEC server,then uses game theory to model the dual-objective optimal matching problem between SFG request and IEC server,and finally designs a distributed preference matching algorithm to solve the problem of SFG and IEC.Matching game between servers.（2）This paper proposes an AoI optimization mechanism that coordinates SFG deployment and sampling frequency adjustment.Firstly,the influence of data sampling frequency on data flow and queuing delay in IEC network is analyzed,and the correlation between data sampling frequency and data AoI of NFV application is revealed,and a heuristic data sampling frequency optimization algorithm is proposed to achieve Sampling frequency close to optimal AoI.Then,on the basis of optimizing the deployment of SFG to reduce the end-to-end service delay of SFG,this paper proposes an AoI optimization algorithm that coordinates SFG deployment and sampling frequency adjustment.（3）In this paper,a series of simulation experiments are designed to verify the effectiveness of this method,and the performance of this method is evaluated by implementing a comparison algorithm.In the SFG distributed deployment experiment,this paper compares the experimental results of the SFG optimization deployment algorithm based on matching game theory with related algorithms.The experimental results show that the algorithm proposed in this paper is effective in reducing processing delay and improving service acceptance rate.sex.In the AoI optimization experiment of NFV application,this paper compares the AoI optimization results of coordinated SFG deployment and sampling frequency adjustment with the results of the comparison algorithms,and verifies that the method in this paper can effectively reduce AoI and ensure high data fineness.