Effective Scheduling Algorithms for I/O Blocking with a Multi-Frame Task Model

A task that suspends itself to wait for an I/O completion or to wait for an event from another node in distributed environments is called an I/O blocking task. Conventional hard real-time scheduling theories use framework of rate monotonic analysis (RMA) to schedule such I/O blocking tasks. However, most of them are pessimistic. In this paper, we propose effective algorithms that can schedule a task set which has I/O blocking tasks under dynamic priority assignment. We present a new critical instant theorem for the multi-frame task set under dynamic priority assignment. The schedulability is analyzed under the new critical instant theorem. For the schedulability analysis, this paper presents saturation summation which is used to calculate the maximum interference function (MIF). With saturation summation, the schedulability of a task set having I/O blocking tasks can be analyzed more accurately. We propose an algorithm which is called Frame Laxity Monotonic Scheduling (FLMS). A genetic algorithm (GA) is also applied. From our experiments, we can conclude that FLMS can significantly reduce the calculation time, and GA can improve task schedulability ratio more than is possible with FLMS. key words: I/O blocking, multi-frame task model, schedulability analysis, laxity, genetic algorithm