R-BATCH: Task Partitioning for Fault-tolerant Multiprocessor Real-Time Systems

Abstract

Many emerging embedded real-time applications such as SCADA (Supervisory Control and Data Acquisition), autonomous vehicles and advanced avionics, require a high degree of dependability. Dealing with tasks having both hard real-time requirements and high reliability constraints is a key challenge faced in such systems. This paper addresses the problem of guaranteeing reliability requirements with bounded recovery times on fail-stop processors in fault-tolerant multiprocessor real-time systems. We classify tasks based on their recovery-time requirements into (i) Hard Recovery, (ii) Soft Recovery, and (iii) Best-Effort Recovery tasks. Then, the notion of a Hot Standby for Hard Recovery tasks along with a Cold Standby for Soft Recovery and Best-Effort Recovery tasks is introduced. In order to maximize the benefits of using a Hot Standby, replicas should not be co-located on the same processor. For this purpose, we propose a task allocation algorithm for Hot Standby replicas called R-BFD (Reliable Best-Fit Decreasing) that uses 37% fewer number of processors than BFD-P (Best-Fit Decreasing augmented with placement constraints). For tasks with more relaxed recovery-time constraints, however, additional optimization can be applied by using a Cold Standby that gets activated only when failures occur. Given a system reliability requirement and hence a maximum number of processor failures to tolerate, the required resource overprovisioning for Cold Standby replicas from multiple processors can be consolidated. An algorithm called R-BATCH (Reliable Bin-packing Algorithm for Tasks with Cold standby and Hot standby) reduces the required number of processors by up to 45% compared to R-BFD-based pure Hot Standby replication technique.