Online Multiple Workflow Scheduling under Privacy and Deadline in Hybrid Cloud Environment

Abstract

Organizations overcome resource shortages by utilizing the multiple services of cloud providers. This leads to sharing resources among various public and private clouds in order to improve the performance while executing the organization's complex workflow systems. Executing multiple workflows in such a hybrid environment needs an effective mapping between workflow's tasks and cloud resources that considers the trade-off between budget and time. There is also a challenge when organizations are forced to deploy workflow's tasks on public resources to execute the tasks before their requested deadlines without violating customers' privacy. In recent years, several online and static approaches were presented to schedule single or multiple workflows considering deadline and budget in cloud environments. However, these studies neglect the privacy constraint along with other SLAs such as deadline and budget. In this paper, we present two online algorithms to schedule multiple workflows under deadline and privacy constraints, while considering the dynamic nature of hybrid cloud environment. The proposed algorithms were evaluated with a series of simulation as well as real experiments using real-life privacy constrained healthcare workflows. Our two algorithms use different methods to rank the tasks: one utilises a novel technique for ranking, the other uses a similar approach to current existing studies. Results show that the novel approach outperforms the current existing ranking methods.