A robust adaptive metric for deadline assignment in heterogeneous distributed 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 component 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 metrics for assigning local task deadlines. Using experimental 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 order of magnitude. In addition, the new technique is shown to be extremely robust for various system configurations.