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Fair Scheduling of Realtime Tasks on Multiprocessors
 Handbook of Scheduling
, 2005
"... There has been much recent interest in fair scheduling algorithms for realtime multiprocessor systems. The roots of much of the research on this topic can be traced back to the seminal work of Baruah et al. on Proportionate fairness (Pfairness) [6]. This work proved that the problem of optimally sc ..."
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There has been much recent interest in fair scheduling algorithms for realtime multiprocessor systems. The roots of much of the research on this topic can be traced back to the seminal work of Baruah et al. on Proportionate fairness (Pfairness) [6]. This work proved that the problem of optimally scheduling periodic tasks on multiprocessors could be solved online in polynomial time by using Pfair scheduling algorithms. Pfair scheduling differs from more conventional realtime scheduling approaches in that tasks are explicitly required to execute at steady rates. In most realtime scheduling disciplines, the notion of a rate is implicit. For example, in a periodic schedule, a task T executes at a rate defined by its required utilization (T.e/T.p) over large intervals. However, T's execution rate over short intervals, e.g., individual periods, may vary significantly. Hence, the notion of a rate under the periodic task model is a bit inexact. Under Pfair scheduling, each task is executed...
Resource Reservation in RealTime Operating Systems a joint industrial and academic position
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An Efficient Algorithm to Reduce the Inflations in Multi Supertask Environment by Using a Transient Behavior Prediction Method
, 2004
"... The supertask approach was proposed by Moir and Ramamthy as a means of supporting nonmigratory tasks in Pfairscheduled systems. In this approach, tasks bound to the same processor are combined into a single server task, called a supertask, which is scheduled as an ordinary Pfair task. When a sup ..."
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The supertask approach was proposed by Moir and Ramamthy as a means of supporting nonmigratory tasks in Pfairscheduled systems. In this approach, tasks bound to the same processor are combined into a single server task, called a supertask, which is scheduled as an ordinary Pfair task. When a supertask is scheduled, one of its component tasks is selected for execution. In previous work, Holman et al. showed that componenttask deadlines can be guaranteed by inflating each supertask’s utilization. In addition, their experimental results showed that the required inflation factors should be small in practice. Consequently, the average inflation produced by their rules is much greater than that actually required by the supertasks. In this paper, we first propose a notion of Transient Behavior Prediction for supertasks, which predicts the latest possible finish time of subtasks that belong to supertasks. On the basis of the notion, we present an efficient schedulability algorithm for Pfair supertasks in which the deadlines of all component tasks can be guaranteed. In addition, we propose a task merging process which combines the unschedulable supertasks with some Pfair tasks; hence, a newly supertask can be scheduled in the system. Finally, we propose the new reweighting functions that can be used when the previous two methods fail. Our reweighting functions produce smaller inflation factor than the previous work does. To demonstrate the efficacy of the supertasking approach, we present the experimental evaluations of our algorithm, which decreases substantially a number of reweights and the size of inflation when there are many supertasks in the Pfairscheduled systems.
PDCS'07 (2007)" A Rapid Heuristic for Scheduling NonPreemptive Dependent Periodic Tasks onto Multiprocessor
, 2009
"... We address distributed realtime applications represented by systems of nonpreemptive dependent periodic tasks. This system is described by an acyclic directed graph. Because the distribution and the scheduling of these tasks onto a multiprocessor is an NPhard problem we propose a greedy heuristic ..."
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We address distributed realtime applications represented by systems of nonpreemptive dependent periodic tasks. This system is described by an acyclic directed graph. Because the distribution and the scheduling of these tasks onto a multiprocessor is an NPhard problem we propose a greedy heuristic to solve it. Our heuristic sequences three algorithms: assignment, unrolling, and scheduling. The tasks of the same, or multiple, periods are assigned to the same processor according to a mixed sort. Then, the initial graph of tasks is unrolled, i.e. each task is repeated according to the ratio between its period and the least common multiple of all periods of tasks. Finally, the tasks of the unrolled graph are distributed and scheduled onto the processors where they have been assigned. Then, we give the complexity of this heuristic, and we illustrate it with an example. A performance analysis comparing our heuristic with an optimal Branch and Cut algorithm concludes that our heuristic is effective in terms of scheduling success ratio and speed. 1
Using Discrete Geometry to model PFair scheduling algorithm for RealTime systems
, 2009
"... Abstract. In this paper, we focus on the use of discrete geometry for the sake of realtime modeling and analysis. We consider multiprocessor context, and we determine the geometrical characterization of PFair scheduling algorithms, which are known to be very performant strategies. A feasability tes ..."
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Abstract. In this paper, we focus on the use of discrete geometry for the sake of realtime modeling and analysis. We consider multiprocessor context, and we determine the geometrical characterization of PFair scheduling algorithms, which are known to be very performant strategies. A feasability test can then be deduced from the geometrical properties.
DIETethic: Fair Scheduling of Optional Computations in
, 2012
"... HPC platforms require users to submit a predetermined number of computation requests (also called jobs). Unfortunately, this is cumbersome when some of the computations are optional, i.e., they are not critical, but their completion would improve results. For example, given a deadline, the number o ..."
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HPC platforms require users to submit a predetermined number of computation requests (also called jobs). Unfortunately, this is cumbersome when some of the computations are optional, i.e., they are not critical, but their completion would improve results. For example, given a deadline, the number of requests to submit for a Monte Carlo experiment is difficult to choose. The more requests are completed, the better the results are, however, submitting too many might overload the platform. Conversely, submitting too few requests may leave resources unused and misses an opportunity to improve the results. This paper introduces and solves the problem of scheduling optional computations. An architecture which autotunes the number of requests is proposed, then implemented in the DIET GridRPC middleware. Reallife experiments show that several metrics are improved, such as user satisfaction, fairness and the number of completed requests. Moreover, the solution is shown to be scalable. Keywords: HPC; GridRPC; malleable applications; Grid’5000
Brief Contributions Pfair Scheduling of Generalized Pinwheel Task Systems
"... Abstract—The scheduling of generalized pinwheel task systems is considered. It is shown that pinwheel scheduling is closely related to the fair scheduling of periodic task systems. This relationship is exploited to obtain new scheduling algorithms for generalized pinwheel task systems. When compared ..."
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Abstract—The scheduling of generalized pinwheel task systems is considered. It is shown that pinwheel scheduling is closely related to the fair scheduling of periodic task systems. This relationship is exploited to obtain new scheduling algorithms for generalized pinwheel task systems. When compared to traditional pinwheel scheduling algorithms, these new algorithms are both more efficient from a runtime complexity point of view, and have a higher density threshold, on a very large subclass of generalized pinwheel task systems. Index Terms—Generalized pinwheels, fairness, realtime scheduling, density threshold.