DEFT: Dynamic Fault-Tolerant Elastic scheduling for tasks with uncertain runtime in cloud

Abstract With the widespread use of clouds, the reliability and efficiency of cloud have been the main concerns of the service providers and users. Thus, fault tolerance has become a hotspot in both industry and academia, especially for real-time applications. To achieve fault tolerance in cloud, a great number of in-depth researches have been conducted. Nevertheless, for addressing the issue of fault tolerance, few studies have taken into account the uncertainty of task runtime, which is however more practical and really needs urgent attention. In this paper, we introduce the uncertainty to our task runtime estimation model and we propose a fault-tolerant task allocation mechanism that strategically uses two fault tolerant task scheduling models while the uncertainty is considered. Moreover, we employ the overlapping mechanism to improve the resource utilization of cloud. Based on the two mechanisms, we propose an innovative D ynamic F ault- T olerant E lastic scheduling algorithm- DEFT for scheduling real-time tasks in the cloud where the system performance volatility should be considered. The purpose of DEFT is to achieve both fault tolerance and resource utilization efficiency. We compare DEFT with three baseline algorithms: NDRFT, DRFT , and NWDEFT . The results from our extensive experiments on the workload of the Google tracelogs show that DEFT can guarantee fault tolerance while achieving high resource utilization.