Real-time computing systems: the next generation
Tutorial: hard real-time systems
Engineering and analysis of real-time microkernels
IEEE Real-Time Systems Newsletter
The Deferrable Server Algorithm for Enhanced Aperiodic Responsiveness in Hard Real-Time Environments
IEEE Transactions on Computers
Performance of real-time bus scheduling algorithms
SIGMETRICS '86/PERFORMANCE '86 Proceedings of the 1986 ACM SIGMETRICS joint international conference on Computer performance modelling, measurement and evaluation
Priority Inheritance Protocols: An Approach to Real-Time Synchronization
IEEE Transactions on Computers
Engineering and Analysis of Fixed Priority Schedulers
IEEE Transactions on Software Engineering
Aperiodic task scheduling for real-time systems
Aperiodic task scheduling for real-time systems
Reducing the variance of point to point transfers in the IBM 9076 parallel computer
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Supporting Embedded System Design Capture, Analysis and Navigation-
ASWEC '97 Proceedings of the Australian Software Engineering Conference
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Real-time scheduling theory is designed to provide a priori verification that all real-time tasks meet their timing requirements. However, this body of theory generally assumes that resources are instantaneously pre-emptable and ignores the costs of systems services. In previous work [1, 2] we provided a theoretical foundation for including the costs of the operating system scheduler in the real-time scheduling framework. In this paper, we apply that theory to the Real-Time (RT) Mach scheduler. We describe a methodology for measuring the components of the RT Mach scheduler in user space. We analyze the predicted performance of different real-time task sets on the target system using the scheduling model and the measured characteristics. We then verify the model experimentally by measuring the performance of the real-time task sets, consisting of RT Mach threads, on the target system, The experimental measurements verify the analytical model to within a small percentage of error. Thus, using the model we have successfully predicted the performance of real-time task sets using system services, and developed consistent methodologies to accomplish that prediction.