Fault-Tolerant Scheduling for Hybrid Real-Time Tasks Based on CPB Model in Cloud

Abstract

Clouds are becoming a very important platform for hybrid real-time tasks. To enhance the reliability of cloud, fault tolerance of cloud becomes a critical issue. However, the complexities and specialties of traditional fault-tolerant mechanisms cannot meet the fault-tolerant requirements of clouds. To address this issue, we propose a novel fault-tolerant scheduling algorithm named ARCHER for hybrid tasks in cloud. ARCHER has three significant characteristics: 1) it integrates the traditional primary/backup model and checkpoint technology which can flexibly determine the execution time of the backup copies of tasks, so it greatly enhances the resource utilization and produces more time slots to execute tasks as many as possible; 2) it employs task classification mechanism to realize precise scheduling for different types of tasks and virtual machines, which reduces the response time of clouds; and 3) it uses time slot exploiting mechanism, task forward mechanism, and task transform mechanism to achieve high-resource utilization. We conduct extensive simulations to evaluate the performance of ARCHER by comparing it with four baseline algorithms. The experimental results show that ARCHER can effectively improve the resource utilization of cloud while guaranteeing fault tolerance.