Scheduling Distributed Real-Time Tasks in the DGMF Model

Analytical results on the real-time scheduling of multiprocessor systems have focussed on partitioning and distributing the task set for scheduling on identical single processors, with the assumption that all of a task can be executed on any of a pool of identical processors. A distributed system is a set of nodes networked together. Each node may have a different type of processor and performs different functions. In the distributed environment, a real-time task may require processing on multiple nodes. However, all the nodes have to satisfy the time constraints of the task. In this paper, we show that the Liu & Layland-type classical task models must be extended to handle this type of distributed real-time tasks. We propose a Distributed Generalized Multiframe (DGMF) task model. The scheduling problem of DGMF tasks can be reduced to that of scheduling Generalized Multiframe (GMF) tasks on a single processor. We analyze the fixed priority scheduling of (D)GMF tasks and prove that the deadline-monotonic priority assignment is not optimal for the general case of frame-wise priority assignment. In the context of relative deadline assignment, we show that there are simple cases for which deadlinemonotonic assignment is optimal. This work is supported in part by a grant from the Office of Naval Research under grant number N00014-99-1-0402 and a grant from the National Science Foundation under grant number CCR-9972105.