Reliability analysis of real-time fault-tolerant task models

摘要

One notable advantage of Model-Driven Architecture (MDA) method is that software developers could do sufficient analysis and tests on software models in the design phase, which helps construct high confidence on the expected software behaviors and performance, especially for safety-critical real-time software. Most existing literature of reliability analysis ignores the effects from those deadline requirements of tasks which are critical properties for real-time software and thus cannot be ignored. Considering the contradictory relationship between the deadline requirements and time costs of fault tolerance in real-time tasks, in this paper, we present a novel reliability model, which takes schedulability as one of the major factors affecting the reliability, to analyze reliability of the task execution model in real-time software design phase. The tasks in this reliability model has no restrictions on their distributions and thus could be distributed on a multiprocessor or on a distributed system. Furthermore, the tasks also define arrival rates of faults and fault-tolerant mechanisms to model the occurrences of non-permanent faults and the corresponding time costs of fault handling. By analyzing the probability of tasks still being schedulable in the worst-case execution scenario with faults occurring, reliability and schedulability are combined into an unified analysis framework, and two algorithms for reliability analysis are given. To make this reliability model more pragmatic, we also present an estimation technique for estimating the fault arrival rate of each task. We show through two case studies respectively the detailed derivation process under static-priority scheduling in a multiprocessor system and in the design process of avionics software, and then analyze the factors affecting the reliability analysis by setting up simulation experiments. When no assumptions of fault occurrences made on the task model, this reliability model regresses to a generic schedulability model.