Dynamic extension and placement method and device for edge computing service

Abstract

The invention provides a dynamic extension and placement method and device for edge computing services, and the method comprises the steps: carrying out the dynamic extension and placement of the edge computing services according to the current respective work load intensity prediction result of each micro-service corresponding to each application in an edge computing platform and the current respective work performance evaluation result of each edge node; performing automatic expansion processing on the number of the micro-service copies according to the target data processing request so as to determine the scaling optimization number of the micro-service copies; and adopting a preset self-adaptive discrete binary particle swarm optimization algorithm to obtain a mapping relationship between each micro-service copy and each available edge node according to the scaling optimization number of the micro-service copies, the number of currently placeable edge nodes and the performance information, so as to respectively place each micro-service copy to the corresponding edge node. According to the invention, the automatic expansion reliability and effectiveness of the edge computing service can be improved under the conditions of unbalanced edge load and unreliable network state in the edge environment, and the accuracy and reliability of edge computing service placement are improved.

Description

technical field [0001] This application relates to the field of edge computing technology, in particular to a method and device for dynamic expansion and placement of edge computing services. Background technique [0002] As a supplement and optimization to cloud computing, Edge Computing focuses on the analysis and processing of real-time and short-period data, and sinks the application to the edge closer to the user to achieve a more efficient response. Dadi relieves the traffic pressure from the device to the cloud. The edge computing cluster consists of a cloud-controlled master node and distributed edge nodes, and the master node can automatically deploy and schedule multi-regional microservices on each edge node. Most approaches to dynamically scaling edge computing services address only one of the two separate problems of automatic scaling and service placement. Assuming resource-constrained edge nodes cannot host scalable service replicas, they must be migrated to ...

AI Technical Summary

Problems solved by technology

[0003] At present, although the existing methods have studied the scalability problem and service placement problem in the cloud environment, they are not suitable for the heterogeneous edge computing environment with limited resources. Scale service replicas without considering resource limitations and heterogeneity of edge nodes and unstable network conditions

In addition, the impact of service placement strategies on scaling decisions has not been fully considered, i.e. how to properly place scalable service replicas on edge nodes with different performance is also an important issue to be solved

For edge computing environments with limited resource capacity and high communication latency, different service placement strategies may reduce the efficiency of the original expansion decision

However, the above studies regard these two issues as independent issues, ignoring the limited edge resources and dynamic network conditions, and cannot guarantee the service quality of edge applications under dynamic load fluctuations.

Images