Deadline assignment in distributed hard real-time systems with relaxed locality constraints

Abstract

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 offline, 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.