Time-triggered scheduling of query executions for active diagnosis in distributed real-time systems

In recent years, many control applications have replaced safety critical mechanical systems with distributed realtime embedded systems comprising of many processors, sensors and actuators interlaced together with a dedicated communication network and without any mechanical backup e.g., steer-bywire used in steering systems of automotive vehicles. Such systems demand a high level of reliability and performance. These systems also have severe cost constraints so including redundant components in the end product is not a viable solution for improving reliability and performance. Another solution is to continuously monitor the system and introduce fault diagnosis to ensure that the dependability of the system is greater than the dependability of its constituent hardware and software components. Active diagnosis is one such technique that improves the reliability of the system by diagnosing facts at run-time for fault isolation and error recovery. The presented work addresses an active diagnosis scenario that uses diagnostic queries and a real-time database to find faults within a distributed system that has limited resources and strict deadlines. Since scheduling the diagnostic tasks is an important aspect of a timely analysis of the system, a list schedule has been proposed that calculates the points in time when the diagnostic queries are executed and data is replicated to the database. This a priori knowledge about the behavior of the query executions will bound the time required for inferring faults that will lead to a realizable diagnostic framework. The proposed algorithm utilizes a priority scheme to schedule the diagnostic tasks onto free processors within minimum time while respecting their precedence and periodicity constraints. The paper presents the approach in detail with the help of examples and results with different design constraints.