Achieving Scalability in Real-Time Systems

Abstract

Running real-time applications with a variable-speed processor can result in scheduling anomalies and permanent overloads. A proposed computational model varies task response times continuously with processor speed, enabling the system to predictably scale its performance during voltage changes. Mutually exclusive resources and nonpreemptive code can generate scheduling anomalies in a processor with dynamic voltage scaling, causing tasks to increase their response times when the processor runs at higher speeds. Even worse, decreasing the speed can cause a permanent overload that degrades system performance in an uncontrolled fashion. Such problems can be efficiently handled through a set of kernel mechanisms, including cyclic asynchronous buffers and elastic scheduling that let system designers scale the performance of real-time applications as a function of processor speed. As successfully done in the SHaRK kernel, both CABs and elastic scheduling can be easily implemented on top of any real-time operating system, as a middleware layer, and they should be included in current standards to develop embedded systems with real-time and energy requirements.