Research On Security Enhanced Edge Computing Task Scheduling Method In Industrial Internet

With the accelerated process of intelligence,the Industrial Internet has increasingly high demands for computing resources.There are many problems in the industrial environment that cloud computing cannot solve,such as central load pressure,insufficient transmission bandwidth,and data privacy threats.Edge computing,as a new computing paradigm,can provide intelligent services near the device source with high bandwidth,low latency,and high reliability.Therefore,scheduling industrial computing tasks to the edge side for processing can meet the key needs of real-time business and data optimization in industrial manufacturing.The task scheduling method directly affects the execution effect of industrial computing tasks.Irrational task scheduling cannot improve the efficiency of edge computing in industrial manufacturing.However,the existing task scheduling methods have shortcomings such as insufficient utilization of edge caching resources,low latency sensitivity during task scheduling,and weak data privacy protection capabilities.In view of these problems,this paper conducts in-depth research on the security-enhanced edge computing task scheduling problem in the Industrial Internet environment,and proposes a task scheduling method for edge computing based on blockchain and task caching,as well as a heterogeneous edge-cloud computing task scheduling method based on blockchain node division and task caching,aiming to reduce latency and costs while improving system security.The main work includes:Firstly,in this paper,a two-layer task scheduling model is proposed for the Industrial Internet scenario with local devices,edge servers,and single task requirements.The model utilizes a joint approach of blockchain and task caching to cache the computation results of similar tasks and enhance system security by leveraging the blockchain.Furthermore,it aims to further reduce task latency.The task scheduling problem that incorporates both task caching and blockchain-assisted caching is formulated as a minimum weighted cost problem subject to delay constraints.An improved genetic algorithm-based task scheduling strategy is proposed by adjusting the initialization and mutation operations of the genetic algorithm.In addition,the Least Recently Used(LRU)algorithm is improved to improve the content hit rate of task caching.Compared with existing edge computing scheduling algorithms,the proposed algorithm has faster optimal solution solving ability and shorter running time.The proposed model not only saves 62.19% of the cost compared to local computing,but also has significant advantages in protecting data security,reducing latency,and lowering costs.Secondly,this paper proposes a three-layer task scheduling model combining blockchain and edge caching for the heterogeneous multi-edge servers,local,and cloud servers in the Industrial Internet scenario with various task requirements.The model can fully utilize the storage resources of edge servers to cache the required resources for tasks,thereby reducing data transmission latency and improving data transmission security.To further enhance the security of heterogeneous edge servers,this paper designs a node grading mechanism that can divide edge servers into different security levels based on their own attributes and security status.The model that integrates task caching,cache resource scheduling,and node grading mechanism is modeled as a minimum weighted cost problem of latency and energy consumption.By linearizing and relaxing the problem,it is transformed into a convex optimization problem,and an optimal and safest scheduling strategy is obtained using a task scheduling algorithm based on alternating direction multiplier method.Experimental results show that the proposed model not only reduces the total weighted cost by 42%compared with local computing,but also improves the security of the system.