DP-FAIR: A Simple Model for Understanding Optimal Multiprocessor Scheduling

Levin, Greg; Funk, Shelby; Sadowski, Caitlin; Pye, Ian; Brandt, Scott

doi:10.1109/ecrts.2010.34

Cited by 132 publications

(102 citation statements)

References 23 publications

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“…These algorithms can also achieve full system utilization and guarantee all the timing constraints of tasks (Cho et al 2006(Cho et al , 2010Funaoka et al 2008). More recently, by adopting the idea of deadline partitioning, a general scheduling model named DP-Fair was studied for continuous-time systems by Levin et al (2010). In addition to periodic tasks, the authors also showed how to extend DP-Fair to sporadic task sets with unconstrained deadlines.…”

Section: Related Workmentioning

confidence: 99%

“…1). But the continuous-time based optimal global schedulers may either require scheduling decisions at any time instant (Cho et al 2006;Funaoka et al 2008) or yield arbitrary small CPU time allocation to individual tasks (Levin et al 2010). Moreover, the well-known discrete-time based Pfair scheduler and its variations (Anderson and Srinivasan 2000a;Buruah et al 1995Buruah et al , 1996 could incur quite high scheduling overhead by making scheduling decision at every time unit, especially for systems with small time quantum.…”

Section: An Efficient Optimal Global Scheduling Algorithm: Bfairmentioning

confidence: 99%

“…More recently, a generalized deadline-partitioned fair (DP-Fair) scheduling model was investigated in Levin et al (2010). However, those algorithms may generate arbitrarily small time allocation to tasks due to the continuous-time property and thus incur very high scheduling overhead.…”

Section: Introductionmentioning

confidence: 99%

“…With the emergence of multicore processors, there is a reviving interest in the multiprocessor real-time scheduling problem and many results have been reported recently (Andersson et al 2008;Baruah and Baker 2008;Davis and Burns 2009a;Devi and Anderson 2005;Guan et al 2009Guan et al , 2010Kato and Yamasaki 2007;Levin et al 2010).…”

Section: Introductionmentioning

confidence: 99%

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Cluster scheduling for real-time systems: utilization bounds and run-time overhead

Zhu

Aydın

2011

Real-Time Syst

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Cluster scheduling, where processors are grouped into clusters and the tasks that are allocated to one cluster are scheduled by a global scheduler, has attracted attention in multiprocessor real-time systems research recently. In this paper, assuming that an optimal global scheduler is adopted within each cluster, we investigate the worst-case utilization bounds for cluster scheduling with different task allocation/partitioning heuristics. First, we develop a lower limit on the utilization bounds for cluster scheduling with any reasonable task allocation scheme. Then, the lower limit is shown to be the exact utilization bound for cluster scheduling with the worstfit task allocation scheme. For other task allocation heuristics (such as first-fit, bestfit, first-fit decreasing, best-fit decreasing and worst-fit decreasing), higher utilization bounds are derived for systems with both homogeneous clusters (where each cluster has the same number of processors) and heterogeneous clusters (where clusters have different number of processors). In addition, focusing on an efficient optimal global scheduler, namely the boundary-fair (Bfair) algorithm, we propose a period-aware task allocation heuristic with the goal of reducing the scheduling overhead (e.g., the number of scheduling points, context switches and task migrations). Simulation results indicate that the percentage of task sets that can be scheduled is significantly improved under cluster scheduling even for small-size clusters, compared to that of the partitioned scheduling. Moreover, when comparing to the simple generic task Real-Time Syst (2011) 47: 253-284 allocation scheme (e.g., first-fit), the proposed period-aware task allocation heuristic markedly reduces the scheduling overhead of cluster scheduling with the Bfair scheduler.

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Section: Related Workmentioning

confidence: 99%

Section: An Efficient Optimal Global Scheduling Algorithm: Bfairmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cluster scheduling for real-time systems: utilization bounds and run-time overhead

Zhu

Aydın

2011

Real-Time Syst

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“…Several global and hybrid algorithms were proven optimal for scheduling asynchronous sets of implicitdeadline periodic tasks, e.g. [1,2,6,15]. The minimum number of processors needed to schedule Γ using an optimal scheduling algorithm, denoted M OPT , is given by:…”

Section: Scheduling Asynchronous Set Of Implicit Deadline Periodic Tasksmentioning

confidence: 99%

Hard-real-time scheduling of data-dependent tasks in embedded streaming applications

Bamakhrama

Stefanov

2011

Proceedings of the Ninth ACM International Conference on Embedded Software

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Most of the hard-real-time scheduling theory for multiprocessor systems assumes independent periodic or sporadic tasks. Such a simple task model is not directly applicable to modern embedded streaming applications. This is because a modern streaming application is typically modeled as a directed graph where nodes represent actors (i.e. tasks) and edges represent data-dependencies. The actors in such graphs have data-dependency constraints and do not necessarily conform to the periodic or sporadic task models. Therefore, in this paper we investigate the applicability of hardreal-time scheduling theory for periodic tasks to streaming applications modeled as acyclic Cyclo-Static Dataflow (CSDF) graphs. In such graphs, the actors are data-dependent, however, we analytically prove that they (i.e. the actors) can be scheduled as implicitdeadline periodic tasks. As a result, a variety of hard-real-time scheduling algorithms for periodic tasks can be applied to schedule such applications with a certain guaranteed throughput. We compare the throughput resulting from such scheduling approach to the maximum achievable throughput of an application for a set of 19 real streaming applications. We find that in more than 80% of the cases, the throughput resulting from our approach is equal to the maximum achievable throughput.

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