Deadline Assignment in Distributed Hard Real-Time Systems with Relaxed Locality Constraints

In a real-time system, tasks are constrained by global end-to-end deadlines. In order to cater for high task schedulability, these deadlines must be distributed over component subtasks in an intelligent way. Existing methods for automatic distribution of end-to-end deadlines are all based on the assumption that task assignments are entirely known beforehand. This assumption is not necessarily valid for large real-time systems. Furthermore, most task assignment strategies require information on deadlines in order to make good assignments, thus forming a circular dependency between deadline distribution and task assignment. We present a heuristic approach that performs deadline distribution prior to task assignment. The deadline distribution problem is presented in the context of large distributed hard real-time systems with relaxed locality constraints, where schedulability analysis must be performed off-line, and only a subset of the tasks are constrained by predetermined assignments to specific processors. Using experimental results we identify drawbacks of previously-proposed techniques, and then show that our solution provides significantly better performance for a large variety of system configurations.