Intrusion Tolerance Scheduling Algorithm for Microservice Workflow Based on Deep Reinforcement Learning

With the rapid development of microservices and container technology,applications executed in the cloud can be completed by multiple microservices with dependencies.However,microservices for container clouds face many security threats due to shared resources.Attackers in the cloud can destroy them directly or indirectly through side channels,container escape,resulting in incorrect output results,which will bring huge losses to users in the cloud.Therefore,an intrusion tolerance scheduling algorithm for microservice workflow(ITSAMW) is proposed to improve the security of the system under the container clouds.Firstly,ITSAMW builds three replicas of each microservice and uses a voting mechanism to guarantee security.ITSAMW studies how to schedule these microservice replicas and proves the location constraints that microservice intrusion tolerance scheduling needs to meet.Secondly,it constructs a microservices scheduling and completion delay model,redefines the security scheduling problem of microservices,and solves the problem with deep reinforcement learning.Finally,in order to verify the effectiveness of ITSAMW,experiments are conducted by using the container clouds simulation platform that Kubernetes builds and are evaluated by using intrusion tolerance,completion delay and load balancing.Experimental results show that compared with the existing methods,under the condition that the completion delay of ITSAMW is increased by 17.6%,the intrusion tolerance is increased by 28.1%,and the load balancing is reduced by 13.7%.