HEALERS: a heterogeneous energy‐aware low‐overhead real‐time scheduler

Moulik, Sanjay; Devaraj, Rajesh; Sarkar, Arnab

doi:10.1049/iet-cdt.2019.0023

Cited by 15 publications

(8 citation statements)

References 39 publications

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“…The result of (H1 � W2 + b2) is again passed through the ReLU function. We call the achieved matrix H2 [see Equation (11)].…”

Section: The Training Stagementioning

confidence: 99%

“…HEALERS [11] proposes an energy-aware fully migrated real-time scheduling on a heterogeneous multicore system. It utilises both DVFS and DPM to minimise the energy consumption in the processing cores.…”

Section: Related Workmentioning

confidence: 99%

“…Dynamic Power Management (DPM) is another technique that attempts to turn off unnecessary processors and hardware parts of the device in order to reduce the energy consumption [9,10] in MPSoCs. Some techniques use a combination of DVFS and DPM for energy consumption [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Q‐scheduler: A temperature and energy‐aware deep Q‐learning technique to schedule tasks in real‐time multiprocessor embedded systems

Mohammadi

Beitollahi

2022

IET Computers & Digital Tech

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Reducing energy consumption under processors' temperature constraints has recently become a pressing issue in real‐time multiprocessor systems on chips (MPSoCs). The high temperature of processors affects the power and reliability of the MPSoC. Low energy consumption is necessary for real‐time embedded systems, as most of them are portable devices. Efficient task mapping on processors has a significant impact on reducing energy consumption and the thermal profile of processors. Several state‐of‐the‐art techniques have recently been proposed for this issue. This paper proposes Q‐scheduler, a novel technique based on the deep Q‐learning technology, to dispatch tasks between processors in a real‐time MPSoC. Thousands of simulated tasks train Q‐scheduler offline to reduce the system's power consumption under temperature constraints of processors. The trained Q‐scheduler dispatches real tasks in a real‐time MPSoC online while also being trained regularly online. Q‐scheduler dispatches multiple tasks in the system simultaneously with a single process; the effectiveness of this ability is significant, especially in a harmonic real‐time system. Experimental results illustrate that Q‐scheduler reduces energy consumption and temperature of processors on average by 15% and 10%, respectively, compared to previous state‐of‐the‐art techniques.

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“…The result of (H1 � W2 + b2) is again passed through the ReLU function. We call the achieved matrix H2 [see Equation (11)].…”

Section: The Training Stagementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Q‐scheduler: A temperature and energy‐aware deep Q‐learning technique to schedule tasks in real‐time multiprocessor embedded systems

Mohammadi

Beitollahi

2022

IET Computers & Digital Tech

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“…The scheduler first computes a set of fragments of the execution and schedules each task in order to allow their appropriate execution share, and configure the operating frequencies in each core to minimize energy consumption. They devised an improved algorithm [24] taking system-wide energy consumption into consideration with better optimal frequency selection method. Chwa et al [26] designed fully-migrative approach to two-type heterogeneous multicore scheduling called Hetero-Fair.…”

Section: Related Workmentioning

confidence: 99%

A Method to Construct Task Scheduling Algorithms for Heterogeneous Multi-Core Systems

Kim

2019

IEEE Access

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The use of heterogeneous multicore processors (HMP) is spreading rapidly from data centers to large-scale deployment in smartphones because they give greater flexibility to adapt to power constraints and performance needs. In this study, we show that an intelligent task scheduler is critical for improving the performance and energy efficiency in an HMP environment. We assume that the tasks are independent in the environment, with hard real-time constraints and multicore systems, where the processors can be manipulated to change the clock cycle speed and power levels. Tasks are assumed to arrive aperiodically where the tasks are applications from the SPEC CPU 2006 benchmark suite. In the evaluation, we used a real system comprising of two multicore processors, which supported on-the-fly dynamic voltage/frequency scaling. We extracted several important components from previously proposed algorithms and combined them to construct algorithms with better performance. Our results showed that some of the best combinations reduced the energy consumption and achieved a better completion rate in the environment. In addition, a method is proposed for calculating the upper-bound of the task completion rate and energy consumption so that there is a guide as to how near the results are to the optimal performance. INDEX TERMS Task scheduling, energy-aware scheduling, heterogeneous multi-core, real-time scheduling, dynamic voltage/frequency scaling.

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“…Scheduling typed DAG tasks with real-time constraints on heterogeneous multicore platforms is an emerging research topic. Most of the prior work [3], [9], [16], [18] focuses on scheduling un-typed real-time DAG tasks on heterogeneous platforms, and proposes methods for determining the core type each code segment (i.e., each vertex in a DAG) should be executed on. For typed DAG tasks, Han et al [13] • C.-C. Lin, J. Shi, N. Ueter, M. Günzel and J.-J.…”

Section: Introductionmentioning

confidence: 99%

Type-Aware Federated Scheduling for Typed DAG Tasks on Heterogeneous Multicore Platforms

Lin

Shi

Ueter

et al. 2023

IEEE Trans. Comput.

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To utilize the performance benefits of heterogeneous multicore platforms in real-time systems, we need task models that expose the parallelism and heterogeneity of the workload, such as typed DAG tasks, as well as scheduling algorithms that effectively exploit this information. In this paper, we introduce type-aware federated scheduling algorithms for sporadic typed DAG tasks with implicit deadlines running on a heterogeneous multicore platform with two different types of cores. In type-aware federated scheduling, a task can be executed in one of the three strategies: Exclusive Allocation, Semi-Exclusive Allocation, and Sequential and Share. In Exclusive Allocation, clusters of cores of both core types are exclusively allocated to tasks, while cores of only one type are exclusively allocated to tasks in Semi-Exclusive Allocation. The workload of the other type from tasks in Semi-Exclusive Allocation and the workload from tasks in Sequential and Share share the cores that are not exclusively allocated to any task. We prove that our type-aware federated scheduling algorithm has a capacity augmentation bound of 7.25. We also show that no constant capacity augmentation bound can be obtained without Semi-Exclusive Allocation. Compared to the state of the art, the type-aware federated scheduling algorithm achieves better schedulability, especially for task sets with skewed workload.

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HEALERS: a heterogeneous energy‐aware low‐overhead real‐time scheduler

Cited by 15 publications

References 39 publications

Q‐scheduler: A temperature and energy‐aware deep Q‐learning technique to schedule tasks in real‐time multiprocessor embedded systems

Q‐scheduler: A temperature and energy‐aware deep Q‐learning technique to schedule tasks in real‐time multiprocessor embedded systems

A Method to Construct Task Scheduling Algorithms for Heterogeneous Multi-Core Systems

Type-Aware Federated Scheduling for Typed DAG Tasks on Heterogeneous Multicore Platforms

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