Research On Edge-Cloud Multi-modal Workflow Scheduling Based On Containerized Hybrid Cloud

In recent years,cloud computing technology has made great progress.With the rise of virtualization technology,various computing resources are delivered to users through network transmission in the form of services.However,cloud computing workflow cluster resource management is related to resource utilization and management.The problems brought about by cost are becoming more and more serious,and the overall scientific workflow scheduling capability for user service quality needs to be improved.The emergence of containerized hybrid cloud and edge computing has brought greater optimization space for process optimization and resource allocation,and also put forward higher requirements for decision-making models and methods.Most of the current researches are limited to a single container cluster,a single resource allocation method,or the matching relationship between tasks and virtual machines.In reality,multiple container sharing virtual machines brings more complex and larger-scale orientation.End-to-end optimization problem at the user business process level.Therefore,this article is mainly based on the containerized hybrid cloud scheduling strategy to solve the multi-mode,large-scale microservice workflow scheduling problem.This article focuses on the side-cloud multi-mode batch microservice workflow scheduling problem of containerized hybrid cloud,taking into account the order relationship of task instances in the workflow,cloud resource types,and the multi-mode configuration of containers,combined with the batch processing work in real cloud computing In the flow scheduling scenario,under the limited cloud computing resources,a mathematical model is established with the goal of minimizing the completion time of the workflow;by summarizing related classic problems,a genetic algorithm to solve the workflow scheduling problem in cloud computing is proposed;using this algorithm An analogy experiment was carried out for different types of workflow clusters;in order to reflect the practical significance of the research in this article,the Alibaba Cloud cluster log data was tracked and the relevant data set was applied to the experimental scenario,which verified the containerized hybrid cloud workflow scheduling.Compared with the advantages of virtual machine scheduling mode.Therefore,the research in this article has a certain reference value for solving the problem of how enterprises effectively use cloud computing resources.In order to avoid the shortcomings of traditional genetic algorithm,it can more efficiently solve the large-scale workflow scheduling problem under the background of cloud computing.This paper is based on the genetic algorithm framework of the traditional workflow scheduling problem.In the genetic operation,the tabu neighborhood search rule proposed in this paper is added to ensure the quality of the new population;the population balance strategy based on the clonal immune algorithm is used to ensure the diversity of the initial solution;combined with the above The improved rule puts forward a staged genetic algorithm based on the large-scale batch workflow scheduling problem.Through a large number of experimental verifications,the results show that each step of the improved algorithm in this paper has a significant improvement over the original ordinary genetic algorithm.In addition,the algorithm proposed in this article is not limited to solving large-scale workflow scheduling optimization problems in cloud computing.It is in the algorithm design for the complex scheduling scenarios,large workflow scales,and high workflow repetition rates similar to those studied in this article.It also provides a certain theoretical reference.