Robust adaptive metrics for deadline assignment in distributed hard real-time systems

In a real-time system, tasks are constrained by global end-to-end (E-T-E) deadlines. In order to cater for high task schedulability, these deadlines must be distributed over com-ponent tasks in an intelligent way. In this paper, we present an improved version of the slicing technique and extend it to heterogeneous distributed hard real-time systems. The salient feature of the new technique is that it utilizes adaptive met-rics for assigning local task deadlines. Using experimen-tal results we show that the new technique exhibits superior performance with respect to the success ratio of a heuristic scheduling algorithm. For smaller systems, the new adaptive metric outperforms a previously-proposed adaptive metric by 300%, and existing non-adaptive metrics by more than an or-der of magnitude. In addition, the new technique is shown to be extremely robust for various system configurations.