Maximizing Reliability of Data-Intensive Workflow Systems with Active Fault Tolerance Schemes in Cloud
Weiling Li, Xiaoning Sun, Kewen Liao, Yunni Xia, Feifei Chen, Qiang He · 2020
Most existing researches on cloud workflow systems have focused on resource scheduling with the aims to minimize system delay under budget constraints or optimize system cost under deadline constraints. However, cloud providers cannot guarantee a failure-free cloud environment, a compact scheduling plan is prone to failure, thus, workflow system reliability has been identified as a critical and challenging issue in the volatile cloud environment. With the ability of cloud, it is easy for users to implement the active fault tolerance schemes, e.g., Scale-Out. However, it will lead to issues like security problem and extra management cost. In this paper, we first investigate Scale-Up and Scale-Hybrid schemes to fully explore the possibilities offered by the ability of cloud. We formally model the problem of optimizing the reliability of a cloud workflow system under budget constraints with these three fault-tolerance schemes. These optimization problems are discrete and non-convex. Thus, we propose a genetic algorithm based method for workflow fault tolerance (GA4WFT). Finally, we evaluate the effectiveness and efficiency of proposed GA4WFT with three different fault-tolerance schemes through experiments conducted on Amazon EC2 data.