Scheduling theory holds great promise as a means to a priori validate timing correctness of real-time applications. However, there currently exists a wide gap between scheduling theory and its implementation in operating system kernels running on specic hardware platforms. The implementation of any particular scheduling algorithm introduces overhead and blocking components which must be accounted for in the timing correctness validation process. This paper presents a methodology for incorporating the costs of scheduler implementation within the context of xed priority scheduling algorithms. Both event-driven and timerdriven scheduling implementations are analyzed. We show that for the timer-driven scheduling implementations the selection of the timer interrupt rate can dramatically aect the schedulability of a task set, and we present a method for determining the optimal timer rate. We analyzed both randomly generated and two well dened task sets and found that their schedulability can be signicantly degraded by the implementation costs. Task sets that have ideal breakdown utilization over 90% may not even be schedulable when the implementation costs are considered. This work provides a rst step towards bridging the gap between realtime scheduling theory and implementation realities. This gap must be bridged for any meaningful validation of timing correctness properties of real-time applications. Keywords| Real-time, scheduling, periodic, schedulability, feasibility. This research is supported in part by grants from the Oce of Naval Research and the Naval Ocean Systems Center under contract N00014-91-J-1304 scheduling theory and its implementation in operating system kernels running on specic hardware platforms. This work provides a rst step towards bridging the gap between real-time scheduling theory and implementation realities. This gap must be bridged for any meaningful validation of timing correctness properties of real-time applications. This paper will take into account the costs of the kernel scheduling mechanisms, which in turn are a function of the underlying hardware support. We dene the kernel costs as either overhead or blocking. Overhead is the time spent in the kernel performing a service on behalf of a specic task, such as invoking or terminating it. Blocking, or priority inversion, is time spent, either
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