Static-priority scheduling on multiprocessors

Andersson, Björn; Baruah, Sanjoy; Jönsson, Jan Åke

doi:10.1109/real.2001.990610

Cited by 202 publications

(220 citation statements)

References 17 publications

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“…For global RM scheduling, system utilization of (m/2)(1 − α) + α can be guaranteed [5]. Andersson et al also studied one scheduling algorithm named RM-US, where tasks with utilizations higher than some threshold θ have the highest priority [3]. For θ = 1 3 , Baker showed that RM-US can guarantee a system utilization of (m + 1)/3 [5].…”

Section: Related Workmentioning

confidence: 99%

An optimal boundary fair scheduling algorithm for multiprocessor real-time systems

Zhu

Mossé

et al. 2011

Journal of Parallel and Distributed Computing

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Although the scheduling problem for multiprocessor real-time systems has been studied for decades, it is still an evolving research field with many open problems. In this work, focusing on periodic real-time tasks, we propose a novel optimal scheduling algorithm, namely boundary fair (Bfair), which follows the same line of research as the well-known Pfair scheduling algorithms and can also achieve full system utilization. However, different from the Pfair algorithms that make scheduling decisions at every time unit to enforce proportional progress (i.e., fairness) for each task, Bfair makes scheduling decisions and enforces fairness to tasks only at tasks' period boundaries. The correctness of the Bfair algorithm to meet the deadlines of all tasks' instances * A preliminary version of this paper appeared in IEEE RTSS 2003. This work is supported in part by NSF award CNS-0720651. 1 is formally proved. The performance of Bfair is evaluated through extensive simulations. The results show that, compared to that of the Pfair algorithms, Bfair can significantly reduces the number of scheduling points (by upto 94%) and the time overhead of Bfair is comparable to that of the most efficient Pfair algorithm (i.e., P D 2 ). Moreover, by aggregating the time allocation of tasks for the time interval between consecutive period boundaries, the resulting Bfair schedule needs dramatically reduced number of context switches and task migrations, as low as 18% and 15%, respectively, when compared to those of Pfair schedules.

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Section: Related Workmentioning

confidence: 99%

An optimal boundary fair scheduling algorithm for multiprocessor real-time systems

Zhu

Mossé

et al. 2011

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

show abstract

“…Some multiprocessor studies in the past (e.g., Andersson et al 2001;Cho et al 2002;Srinivasan and Baruah 2002) have focused on adapting existing uniprocessor scheduling to multiprocessors, and some others have developed novel policies specific to multiprocessors (e.g., Baruah et al 1996;Anderson and Srinivasan 2000;Cho et al 2006;Andersson and Tovar 2006;Funaoka et al 2008;Easwaran et al 2009;Levin et al 2010). In spite of some significant achievements of these studies, many important scheduling problems continue to pose challenges including the efficient scheduling of general task systems such as those in which task deadlines differ from their periods.…”

Section: Introductionmentioning

confidence: 99%

Laxity dynamics and LLF schedulability analysis on multiprocessor platforms

2012

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LLF (Least Laxity First) scheduling, which assigns a higher priority to a task with a smaller laxity, has been known as an optimal preemptive scheduling algorithm on a single processor platform. However, little work has been made to illuminate its characteristics upon multiprocessor platforms. In this paper, we identify the dynamics of laxity from the system's viewpoint and translate the dynamics into LLF multiprocessor schedulability analysis. More specifically, we first character-ize laxity properties under LLF scheduling, focusing on laxity dynamics associated with a deadline miss. These laxity dynamics describe a lower bound, which leads to the deadline miss, on the number of tasks of certain laxity values at certain time instants. This lower bound is significant because it represents invariants for highly dynamic system parameters (laxity values). Since the laxity of a task is dependent of the amount of interference of higher-priority tasks, we can then derive a set of condi-tions to check whether a given task system can go into the laxity dynamics towards a deadline miss. This way, to the author's best knowledge, we propose the first LLF multiprocessor schedulability test based on its own laxity properties. We also develop an improved schedulability test that exploits slack values. We mathematically prove that the proposed LLF tests dominate the state-of-the-art EDZL tests. We also present simulation results to evaluate schedulability performance of both the original and improved LLF tests in a quantitative manner.

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“…Similarly, for global-RMS scheduling, a system utilization of (m/2)(1 − α) + α can be guaranteed (Baker 2003). Andersson et al also studied the scheduling algorithm RM-US, where tasks with utilization higher than some threshold θ have the highest priority (Andersson et al 2001). For θ = 1 3 , Baker showed that RM-US can guarantee a system utilization of (m + 1)/3 (Baker 2003).…”

Section: Related Workmentioning

confidence: 99%

“…Despite its flexibility that allows tasks to migrate and execute on different processors, it has been shown that simple global scheduling policies (e.g., global-EDF and global-RMS) could fail to schedule task sets with very low system utilization (Dhall and Liu 1978). For task sets where the maximum task utilization is limited, some utilization bounds have also been studied for global-EDF and global-RMS scheduling algorithms (Andersson et al 2001;Baker 2003;Goossens et al 2003).…”

Section: Introductionmentioning

confidence: 99%

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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Static-priority scheduling on multiprocessors

Cited by 202 publications

References 17 publications

An optimal boundary fair scheduling algorithm for multiprocessor real-time systems

An optimal boundary fair scheduling algorithm for multiprocessor real-time systems

Laxity dynamics and LLF schedulability analysis on multiprocessor platforms

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

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