Resource Scheduling Strategy And Experimental Platform Verification For Edge Intelligence

The multimedia Internet of Things,which combines image/video processing,computer vision,and communication networks,is expected to be applied to fields such as intelligent driving and video surveillance.For image/video processing and computer vision and other related fields,deep learning has become a mainstream solution.To meet the needs of time-sensitive tasks,deep learning can be combined with edge computing that processes and analyzes data at the edge of the network to form edge intelligence,that is,deep learning model inference is performed at edge nodes closer to the data source.However,edge intelligence is still in the early stages of development,and there are currently many problems,especially the limited storage resources of edge nodes make it impossible to place a large number of deep learning models,and the limited computing resources of a single edge node make it difficult to complete computing tasks in a short time.In response to these problems,this paper proposes edge computing resource placement algorithms and task allocation algorithms,and builds an experimental platform and conducts verification.The main research work of this paper is as follows:1.Aiming at the problem of insufficient storage resources of edge nodes,this article specifically considers the unequal probability of each node being requested.Based on the request popularity of the deep learning model and the communicable network architecture between nodes,a multi-round edge computing resource oriented to edge intelligence is proposed.Placement algorithm,this algorithm predicts user requests to a certain extent,and takes into account the cooperation between edge nodes,and can make full use of idle storage resources of nodes with a lower frequency of requests.Aiming at the problem of insufficient computing resources at edge nodes,this paper,based on the idea of collaborative processing tasks,organizes multiple nodes into clusters and processes computing tasks in clusters.The goal is to reduce task processing time and balance the load between nodes.Based on the measured data and the NSGA-II(Non-dominated Sorting Genetic Algorithm-II)algorithm,the collaborative task processing strategy is designed,and the allocation of subtasks and the placement of each application in multiple collaborative edge nodes are studied.The numerical simulation experiments based on the Matlab platform shows that the proposed resource placement algorithm has differentprobability of being requested at each node,and the node capacity is 10 GB,and when there are 100 types of user requests,the storage resource utilization rate of the proposed algorithm is 70%?77% higher than the comparison algorithm,the average delay can be reduced by 5% to 21%;when the total video task size is 8MB,the proposed task allocation algorithm can reduce the task processing time by 45% compared with the comparison algorithm.2.In order to make up for the shortcomings of only parameter simulation in existing work,this paper designs and builds a semi-physical experimental platform based on Docker and Kubernetes,takes video processing tasks as user requests,and uses applications created based on deep learning models as the smallest task processing unit.The experimental platform verifies the task allocation algorithm.The experimental results show that compared with the comparison algorithm,the task allocation algorithm proposed in this paper can reduce the task processing time by 38% and the CPU load difference between nodes by 50%.