A Real-Time Task Availability Improving Fault-Tolerant Scheduling Algorithm on Heterogeneous Platform

With the rapid development of Internet plus, cloud computing, big data and other fields, heterogeneous system has become an important platform for the deployment of scientific computing, industrial control, cloud storage and other key applications. Because of the heterogeneity of processor performance and software/hardware structure, heterogeneous platform shows better scalability and high cost-performance ratio. However, with the scale of platform becoming larger and the system application becoming more complex, system schedulability becomes worse, and availability decreases. To solve this problem, we propose a fault-tolerant scheduling algorithm aiming to improve availability for real-time tasks on heterogeneous platform, namely AIFSAL. The algorithm uses processor utilization and availability cost to design real-time task scheduling model, and combines availability cost and primary/backup copy (PB) method together for fault-tolerant. During task scheduling, no matter task’s primary or backup copy, processors with lower availability cost is chosen preferentially in order to improve system availability, meanwhile tasks’ backup copies are executed as the type of passive backup copy preferentially in order to achieve fault-tolerant and ensure the schedulability of task allocation. Simulation experiments and comparison analysis with other task scheduling algorithms, including availability approached task scheduling algorithm (AATSAL), task partition based fault tolerant rate-monotonic (TPFTRM) and the earliest completion algorithm (MinMin), verify the effectiveness of the proposed algorithm on availability improving and schedulability assuring. Hence, the system comprehensive cost is reduced and comprehensive performance is improved significantly.