Statistic Rate Monotonic Scheduling


Show simple item record Atlas, Alia en_US Bestavros, Azer en_US 2011-10-20T05:07:34Z 2011-10-20T05:07:34Z 1998-05-02 en_US
dc.identifier.citation Atlas, Alia; Bestavros, Azer. "Statistical Rate Monotonic Scheduling", Technical Report BUCS-1998-010, Computer Science Department, Boston University, May 2, 1998. [Available from:] en_US
dc.description.abstract In this paper we present Statistical Rate Monotonic Scheduling (SRMS), a generalization of the classical RMS results of Liu and Layland that allows scheduling periodic tasks with highly variable execution times and statistical QoS requirements. Similar to RMS, SRMS has two components: a feasibility test and a scheduling algorithm. The feasibility test for SRMS ensures that using SRMS' scheduling algorithms, it is possible for a given periodic task set to share a given resource (e.g. a processor, communication medium, switching device, etc.) in such a way that such sharing does not result in the violation of any of the periodic tasks QoS constraints. The SRMS scheduling algorithm incorporates a number of unique features. First, it allows for fixed priority scheduling that keeps the tasks' value (or importance) independent of their periods. Second, it allows for job admission control, which allows the rejection of jobs that are not guaranteed to finish by their deadlines as soon as they are released, thus enabling the system to take necessary compensating actions. Also, admission control allows the preservation of resources since no time is spent on jobs that will miss their deadlines anyway. Third, SRMS integrates reservation-based and best-effort resource scheduling seamlessly. Reservation-based scheduling ensures the delivery of the minimal requested QoS; best-effort scheduling ensures that unused, reserved bandwidth is not wasted, but rather used to improve QoS further. Fourth, SRMS allows a system to deal gracefully with overload conditions by ensuring a fair deterioration in QoS across all tasks---as opposed to penalizing tasks with longer periods, for example. Finally, SRMS has the added advantage that its schedulability test is simple and its scheduling algorithm has a constant overhead in the sense that the complexity of the scheduler is not dependent on the number of the tasks in the system. We have evaluated SRMS against a number of alternative scheduling algorithms suggested in the literature (e.g. RMS and slack stealing), as well as refinements thereof, which we describe in this paper. Consistently throughout our experiments, SRMS provided the best performance. In addition, to evaluate the optimality of SRMS, we have compared it to an inefficient, yet optimal scheduler for task sets with harmonic periods. en_US
dc.description.sponsorship National Science Foundation (CCR-970668) en_US
dc.language.iso en_US en_US
dc.publisher Boston University Computer Science Department en_US
dc.relation.ispartofseries BUCS Technical Reports;BUCS-TR-1998-010 en_US
dc.subject Real-time computing and communication en_US
dc.subject Admission control en_US
dc.subject Operating systems en_US
dc.subject Probabilistic analysis en_US
dc.subject Quality of Service (QoS) management en_US
dc.subject Scheduling algorithms and analysis en_US
dc.title Statistic Rate Monotonic Scheduling en_US
dc.type Technical Report en_US

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