Evolving Scheduling Strategies for Multi-Processor Real-Time Systems

In recent years the multi-core era started to affect embedded systems, changing some of the rules: While on a single processor, Earliest Deadline First has been proven to be the best algorithm to guarantee the correct execution of priorized tasks, Dhall et al. have shown that this approach is not feasible for multi-processor systems anymore. A variety of new scheduling algorithms has been introduced, competing to be the answer to the challenges multi-processor real-time scheduling is imposing. In this paper, we study the solution space of prioritizationbased task scheduling algorithms using genetic programming and state-of-the-art accelerator technologies. We demonstrate that this approach is indeed feasible to generate a wide variety of capable scheduling algorithms with pre-selected characteristics, the best of which outperform many existing approaches. For a static predefined set of tasks, overfitting even allows us to produce optimal algorithms.