Research On Cost Based Scientific Workflow Scheduling In Cloud Computing Environment

Workflow is a common model of scientific experiments,which consists of many tasks,data flows and computational dependencies.With the rapid development of the Internet industry,cloud computing realizes the unified management of resources and provides humanized services.Users can formulate corresponding virtual resources according to their needs.How to minimize the execution cost of workflow scheduling in cloud computing environment under the condition of satisfying the quality of service has become a research hotspot.In the process of workflow scheduling,we need to consider security factors,especially scientific workflow.If the data security is not guaranteed,it may lead to information leakage or data change,which will have a huge negative impact on scientific experiments.In this paper,based on cost optimization,aiming at the problem that the existing algorithms ignore the security,we study the optimization of the calculation cost,completion time and security of workflow task scheduling,the main contents of this paper are as follows:For the cost optimization problem under completion time and security constraints,a hybrid symbiotic biological search scheduling algorithm is proposed.Firstly,the workflow scheduling problem is modeled,and the security is introduced into the model.The workflow is preprocessed,and the scheduling sequence is generated,Based on the evolution idea,the coding method is designed,the penalty function is introduced into the fitness function,and the population evolution method is improved based on the opposition learning strategy to improve the convergence speed of the algorithm and achieve the cost optimization goal.For the multi-objective optimization problem of cost,completion time and security.Firstly,a mathematical model of multi-objective optimization problem of workflow task scheduling is constructed,and an improved multi-objective symbiotic biological search scheduling algorithm is proposed.Based on the idea of multi-objective symbiotic biological search algorithm,the greedy algorithm is used to initialize the population,and the elite preservation strategy is introduced to enhance the diversity of the population,to protect the excellent individuals from being lost,an optimal solution selection mechanism is designed based on angle selection,which improves the accuracy and convergence speed of the solution.Combined with chaotic local search strategy,the quality of elite solution is improved,and the local optimal solution is avoided.In this paper,we use the Workflow Sim platform for simulation experiments to compare the proposed algorithm.The scheduling algorithm based on hybrid symbiotic biological search is evaluated from multiple perspectives,which verifies that the algorithm performs better than the comparison algorithm in terms of computational cost,and improves the security of scheduling within the deadline;based on the improved scheduling algorithm of multi-objective symbiotic biological search,the comparative experiments show that the algorithm can obtain a better compromise solution and has certain advantages.