Mixed-Criticality Multicore Scheduling of Real-Time Gang Task Systems

Bhuiyan, Ashik Ahmed; Yang, Kecheng; Arefin, Samsil; Saifullah, Abusayeed; Guan, Nan; Guo, Zhishan

doi:10.1109/rtss46320.2019.00048

Cited by 21 publications

(3 citation statements)

References 28 publications

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“…To date, a significant amount of works studied the energy minimization scheduling technique considering both the parallel and sequential real-time tasks, in a non-MC platform, few to mention [11], [12], [15], [17], [26]- [28], [41]. On the other hand, extensive research has been done on real-time scheduling of the MC task model considering both the sequential and parallel workload model (e.g., [4]- [6], [14], [20], [29], [34]), without considering the energy-awareness.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Energy in Non-Preemptive Mixed-Criticality Scheduling by Exploiting Probabilistic Information

Bhuiyan

Reghenzani

Fornaciari

et al. 2020

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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The strict requirements on the timing correctness biased the modeling and analysis of real-time systems towards the worst-case performances. Such focus on the worst-case, however, does not provide enough information to effectively steer the resource/energy optimization. In this paper, we integrate a probabilistic-based energy prediction strategy with the precise scheduling of mixed-criticality tasks, where the timing correctness must be met for all tasks at all scenarios. The Dynamic Voltage and Frequency Scaling (DVFS) is applied to this precise scheduling policy to enable energy minimization. We propose a probabilistic technique to derive an energy-efficient speed (for the processor) that minimizes the average energy consumption, while guaranteeing the (worst-case) timing correctness for all tasks, including lo-criticality ones, under any execution condition. We present a response time analysis for such systems under the nonpreemptive fixed-priority scheduling policy. Finally, we conduct an extensive simulation campaign based on randomly generated task sets to verify the effectiveness of our algorithm (w.r.t. energy savings) and it reports up to 46% energy-saving.

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Section: Related Workmentioning

confidence: 99%

Optimizing Energy in Non-Preemptive Mixed-Criticality Scheduling by Exploiting Probabilistic Information

Bhuiyan

Reghenzani

Fornaciari

et al. 2020

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…Since the seminal work by Vestal et al [36], there has been extensive research on real-time scheduling of the sequential and parallel workload model in an MC platform, few to mention [1,4,7,9,13,16,29]. Most of these works considered that the system starts in the normal (lo-) mode, and unless there is a task overrun, all the tasks are guaranteed execution w.r.t their lo-WCET.…”

Section: Related Workmentioning

confidence: 99%

F2vd

Yang¹,

Bhuiyan

Guo

2020

Proceedings of the 39th International Conference on Computer-Aided Design

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“…Many emerging computationintensive real-time applications, such as self-driving cars, rely on parallel processing, i.e., they can simultaneously execute on multiple processors. The real-time community has studied the scheduling strategies for different parallel workload models, such as the Directed Acyclic Graph (DAG) task model [1]- [3], gang task model [4]- [6], and synchronous task model [7]. The parallel task model's fundamental characteristic, i.e., running simultaneously on multiple processors, is essential to exploit the computational power of modern multi-core platforms, including heterogeneous processors.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Level DPM Approach for Real-Time DAG Tasks in Heterogeneous Processors

Reghenzani

Bhuiyan

Fornaciari

et al. 2021

2021 IEEE Real-Time Systems Symposium (RTSS)

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The modeling and analysis of real-time applications focus on the worst-case scenario because of their strict timing requirements. However, many real-time embedded systems include critical applications requiring not only timing constraints but also other system limitations, such as energy consumption.In this paper, we study the energy-aware real-time scheduling of Directed Acyclic Graph (DAG) tasks. We integrate the Dynamic Power Management (DPM) policy to reduce the Worst-Case Energy Consumption (WCEC), which is an essential requirement for energy-constrained systems. Besides, we extend our analysis with tasks' probabilistic information to improve the Average-Case Energy Consumption (ACEC), which is, instead, a common non-functional requirement of embedded systems. To verify the benefits of our approach in terms of reduced energy consumption, we finally conduct an extensive simulation, followed by an experimental study on an Odroid-H2 board. Compared to the state-of-the-art solution, our approach is able to reduce the power consumption up to 32.1%.

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Mixed-Criticality Multicore Scheduling of Real-Time Gang Task Systems

Cited by 21 publications

References 28 publications

Optimizing Energy in Non-Preemptive Mixed-Criticality Scheduling by Exploiting Probabilistic Information

Optimizing Energy in Non-Preemptive Mixed-Criticality Scheduling by Exploiting Probabilistic Information

F2vd

A Multi-Level DPM Approach for Real-Time DAG Tasks in Heterogeneous Processors

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