A Generalized Parallel Task Model for Recurrent Real-time Processes

Baruah, Sanjoy; Bonifaci, Vincenzo; Marchetti-Spaccamela, Alberto; Stougie, Leen; Wiese, Andreas

doi:10.1109/rtss.2012.59

Cited by 143 publications

(98 citation statements)

References 10 publications

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“…A resource augmentation bound of 2 − 1 m for G-EDF was proved for a single DAG with arbitrary deadlines [8] and for multiple DAGs [15,35]. A capacity augmentation bound of 4 − 2 m was proved in [35] for tasks with for implicit deadlines.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Federated and Global Scheduling for Parallel Real-Time Tasks

Chen

Agrawal

et al. 2014

2014 26th Euromicro Conference on Real-Time Systems

175

119

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This paper considers the scheduling of parallel realtime tasks with implicit deadlines. Each parallel task is characterized as a general directed acyclic graph (DAG). We analyze three different real-time scheduling strategies: two well known algorithms, namely global earliestdeadline-first and global rate-monotonic, and one new algorithm, namely federated scheduling. The federated scheduling algorithm proposed in this paper is a generalization of partitioned scheduling to parallel tasks. In this strategy, each high-utilization task (utilization ≥ 1) is assigned a set of dedicated cores and the remaining low-utilization tasks share the remaining cores. We prove capacity augmentation bounds for all three schedulers. In particular, we show that if on unit-speed cores, a task set has total utilization of at most m and the criticalpath length of each task is smaller than its deadline, then federated scheduling can schedule that task set on m cores of speed 2; G-EDF can schedule it with speed 3+ √ 5 2 ≈ 2.618; and G-RM can schedule it with speed 2 + √ 3 ≈ 3.732. We also provide lower bounds on the speedup and show that the bounds are tight for federated scheduling and G-EDF when m is sufficiently large.

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Section: Related Workmentioning

confidence: 99%

Analysis of Federated and Global Scheduling for Parallel Real-Time Tasks

Chen

Agrawal

et al. 2014

2014 26th Euromicro Conference on Real-Time Systems

175

119

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show abstract

“…Usually, the number of sub-tasks spawned in each parallel segment is fixed and may not exceed the number of cores in the system. On the other end of the spectrum, the DAG model [5,8,15,21,3,16] represents each task as a directed acyclic graph (DAG), where each node is a sequential sub-task, and each directed edge defines a precedence constraint between two nodes. In this model, a node becomes ready for execution as soon as all its predecessor nodes (if any) have completed.…”

Section: Introductionmentioning

confidence: 99%

A multi-DAG model for real-time parallel applications with conditional execution

Fonseca

Nélis

Raravi³

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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Owing to the current trends for higher performance and the ever growing availability of multiprocessors in the embedded computing (EC) domain, there is nowadays a strong push towards the parallelization of modern embedded applications. Several real-time task models have recently been proposed to capture different forms of parallelism. However, they do not deal explicitly with control flow information as they assume that all the threads of a parallel task must execute every time the task is activated. In contrast, in this paper, we present a multi-DAG model where each task is characterized by a set of execution flows, each of which represents a different execution path throughout the task code and is modeled as a DAG of sub-tasks. We propose a two-step solution that computes a single synchronous DAG of servers for a task modeled by a multi-DAG and show that these servers are able to supply every execution flow of that task with the required cpu-budget so that the task can execute entirely, irrespective of the execution flow taken at run-time, while satisfying its precedence constraints. As a result, each task can be modeled by its single DAG of servers, which facilitates in leveraging the existing single-DAG schedulability analyses techniques for analyzing the schedulability of parallel tasks with multiple execution flows. ABSTRACTOwing to the current trends for higher performance and the ever growing availability of multiprocessors in the embedded computing (EC) domain, there is nowadays a strong push towards the parallelization of modern embedded applications. Several real-time task models have recently been proposed to capture different forms of parallelism. However, they do not deal explicitly with control flow information as they assume that all the threads of a parallel task must execute every time the task is activated. In contrast, in this paper, we present a multi-DAG model where each task is characterized by a set of execution flows, each of which represents a different execution path throughout the task code and is modeled as a DAG of sub-tasks. We propose a two-step solution that computes a single synchronous DAG of servers for a task modeled by a multi-DAG and show that these servers are able to supply every execution flow of that task with the required cpu-budget so that the task can execute entirely, irrespective of the execution flow taken at run-time, while satisfying its precedence constraints. As a result, each task can be modeled by its single DAG of servers, which facilitates in leveraging the existing single-DAG schedulability analyses techniques for analyzing the schedulability of parallel tasks with multiple execution flows.

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“…Many strategies for parallel synchronous tasks decompose parallel tasks into sets of sequential tasks [1,[13][14][15][16]. Without decomposition, researchers have studied both synchronous tasks [17] and general DAG tasks [18][19][20][21][22]. For hard real-time tasks with worst-case parameters, the best capacity augmentation bound known for general DAGs is 2 using federated scheduling (a partition-like strategy) without decomposition [2]; 2.6 using GEDF without decomposition [2]; 3.73 for rate-monotonic with and without decompostion [1,2]; and 3.42 for a more restricted class of synchronous tasks [13].…”

Section: Related Workmentioning

confidence: 99%

Federated scheduling for stochastic parallel real-time tasks

Agrawal

Gill

et al. 2014

2014 IEEE 20th International Conference on Embedded and Real-Time Computing Systems and Applications

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Abstract-Federated scheduling is a strategy to schedule parallel real-time tasks: It allocates a dedicated cluster of cores to each high-utilization task (utilization ≥ 1); It uses a multiprocessor scheduling algorithm to schedule and execute all low-utilization tasks sequentially, on a shared cluster of the remaining cores. Prior work has shown that federated scheduling has the best known capacity augmentation bound of 2 for parallel tasks with implicit deadlines. In this paper, we explore the soft realtime performance of federated scheduling and address averagecase workloads instead of worst-case ones. In particular, we consider stochastic tasks -tasks for which execution time and critical-path length are random variables. In this case, we use bounded expected tardiness as the schedulability criterion. We define a stochastic capacity augmentation bound and prove that federated scheduling algorithms guarantee the same bound of 2 for stochastic tasks. We present three federated mapping algorithms with different complexities for core allocation. All of them guarantee bounded expected tardiness and provide the same capacity augmentation bound. In practice, however, we expect them to provide different performance, both in terms of the task sets they can schedule and the actual tardiness they guarantee. Therefore, we present numerical evaluations using randomly generated task sets to examine the practical differences between the three algorithms.

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A Generalized Parallel Task Model for Recurrent Real-time Processes

Cited by 143 publications

References 10 publications

Analysis of Federated and Global Scheduling for Parallel Real-Time Tasks

Analysis of Federated and Global Scheduling for Parallel Real-Time Tasks

A multi-DAG model for real-time parallel applications with conditional execution

Federated scheduling for stochastic parallel real-time tasks

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