A Hybrid Genetic Algorithm for Privacy and Cost Aware Scheduling of Data Intensive Workflow in Cloud

Abstract

In the context of cloud computing and big data, the data of all walks of life has been obtained conveniently. Some information of users in the business process is in need of protection with the popularity of workflow applications, which will greatly affect the scheduling of workflow. Meanwhile, the amount of data is usually very large in workflow, the data privacy protection in workflow has also become an important research problem. In this paper, in order to satisfy the requirement of data privacy protection from user and minimize the total scheduling cost in workflow scheduling, we proposed a privacy and cost aware method based on genetic algorithm for data intensive workflow applications which takes into account computation cost, data transmission cost and data storage cost in cloud to solve this problem on finding the best scheduling solution. The proposed algorithm uses the summation of upward and downward rank values for prioritizing workflow tasks, then merges it to make an optimal initial population to obtain a good solution quickly. Besides, a series of operations like selection, crossover and mutation have been used to optimize the scheduling. In the workflow task scheduling, we assign the datacenter for tasks needing privacy protection, which data of these tasks cannot be moved or copied to other datacenter. Finally, we demonstrate the potential of proposed algorithm for optimizing economic cost with user privacy protection requirement. The experimental results show that proposed algorithm can help improve the scheduling and save the time and cost by an average of 3.6 % and 15.6 % respectively.