Research On Multi-data Source Task Offloading Technology Based On Edge Collaboration

Edge computing technology has very important applications in the field of data privacy protection.Edge computing can hide the original data from service callers such as the cloud center by deploying services on edge nodes,and only return processed statistical data and result data,thereby protecting sensitive information in the data.However,when the task involves the original task data on multiple edge nodes,the task becomes a multi-data source task in the edge computing scenario.During the execution of a multi-data source task,heterogeneous factors cause the execution time of individual sub-tasks to be too long,making the entire task execution time too long.Task offloading is an effective way to solve the above problems.However,the current research results on task offloading lack a task offloading model and offloading strategy suitable for multi-data source tasks in edge computing scenarios.In view of this problem,this paper studies the related content of multi-data source task offloading technology,including multi-data source task offloading model,multi-data source task offloading strategy and adaptive task control mechanism.The specific research contents of this article are as follows:First,this article analyzes the scenario of multiple data source tasks.Formally describes the problem of multi-data source task offloading,and proposes a offloading model suitable for multi-data source task offloading.In the multi-data source task offloading scenario,factors such as competition in computing resources and competition in communication resources will have an impact on task offloading.Based on the existing basic model of task offloading in edge computing,this paper improves the related basic model based on the above factors,and obtains a offloading model suitable for multidata source task offloading,including a computing model,a communication model and a offloading decision model.Secondly,this article describes the Heuristic Multi-Data Source Task Offloading Strategy(HMSTOS).In multi-data source task offloading,the accumulation of the best task offloading decisions for a single node may not necessarily result in the best offloading decision for multiple data source tasks.Multi-data source task offloading needs to make offloading decisions from a global perspective.This article gives detailed algorithm descriptions for the main points in multi-data source task offloading,including how to divide the offloading node and the offloading target node,calculate the task offloading volume and establish the offloading relationship.Then,this paper introduces the adaptive task control mechanism in HMSTOS(AHMSTOS).The multi-data source task offloading scheme has strong dependence on environmental conditions.In order to reduce the impact of condition changes on task offloading,AMHSTOS regularly collects relevant information on each node.Based on this information,a new scheme is generated,and then the old scheme is replaced according to certain criteria.AHMSTOS also sets a timeout response to avoid situations where the task execution time is too long or the task cannot be completed.Finally,this paper verified the correctness of the HMSTOS and the effectiveness of the adaptive task control mechanism in the technical scheme of this paper through experiments.The experimental results show that the heuristic multi-data source task offloading strategy in this paper is better than the local execution strategy in a static environment,and its effect in a dynamic environment is affected by changes in environmental conditions.And AHMSTOS can adjust the task offloading scheme according to the changes in environmental conditions,which can save more time in a dynamic environment.The adaptive task control mechanism also verifies the task’s timeout response mechanism,and experiments have shown that the timeout response mechanism can terminate the task execution on time.