Energy-aware Fault-tolerant Scheduling Scheme based on Intelligent Prediction Model for Cloud Data Center

Abstract

As cloud computing becomes increasingly popular, more and more applications are migrated to clouds. Due to multi-step computation of data streams and heterogeneous task dependencies, task failure occurs frequently, resulting in poor user experience and additional energy consumption. To reduce task execution failure as well as energy consumption, we propose a novel energy-aware proactive fault-tolerant scheduling scheme for cloud data centers(CDCs) in this paper. Firstly, a prediction model based on machine learning approach is trained to classify the arriving tasks into “failure-prone tasks” and “non-failure-prone tasks” according to the predicted failure rate. Then, two efficient scheduling mechanisms are proposed to allocate two types of tasks to the most appropriate hosts in a CDC. Vector reconstruction method is developed to construct super tasks from failure-prone tasks and schedule these super tasks and non-failure-prone tasks to most suitable physical host, separately. All the tasks are scheduled in an earliest-deadline-first manner. Our evaluation results show that the proposed scheme can intelligently predict task failure and achieves better fault tolerance and reduces total energy consumption than existing schemes.