Energy Efficient Scheduling of Real-Time Tasks on Multicore Processors

Seo, Euiseong; Jeong, Jinkyu; Park, Seonyeong; Lee, Joonwon

doi:10.1109/tpds.2008.104

Cited by 135 publications

(11 citation statements)

References 21 publications

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“…In addition, several techniques to achieve energy efficiency for systems executing periodic independent real-time tasks have been proposed. Among these techniques, the ones presented in [10] and [21] are closely related to our approach because they consider global VFS. The authors in [10] study the problem of energy minimization when executing a periodic workload on homogeneous multiprocessor systems.…”

Section: Related Workmentioning

confidence: 99%

“…In our approach, instead, we consider semi-partitioned scheduling and we show that this approach yields significant energy savings compared to a pure partitioned one. The authors in [21] also address the problem of energy minimization under a periodic workload with real-time constraints. However, their approach allows migration of tasks at any time and to any processor.…”

Section: Related Workmentioning

confidence: 99%

“…The model comprises dynamic power p dyn and static power p sta , and the value of p dyn assumes that the core is fully utilized. Note that expression (21) assumes that the processor runs at one of the supported normalized speeds α i ∈ A A9 . From this α i , we can derive the processor frequency F cpu = F i by Definition 1.…”

Section: Power Modelmentioning

confidence: 99%

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Energy efficient semi-partitioned scheduling for embedded multiprocessor streaming systems

Cannella

Stefanov

2016

Des Autom Embed Syst

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In this paper, we study the problem of energy minimization when mapping streaming applications with throughput constraints to homogeneous multiprocessor systems in which voltage and frequency scaling is supported with a discrete set of operating voltage/frequency modes. We propose a soft real-time semi-partitioned scheduling algorithm which allows an even distribution of the utilization of tasks among the available processors. In turn, this enables processors to run at a lower frequency, which yields to lower energy consumption. We show on a set of real-life applications that our semi-partitioned scheduling approach achieves significant energy savings compared to a purely partitioned scheduling approach and an existing semi-partitioned one, EDF-os, on average by 36 % (and up to 64 %) when using the lowest frequency which guarantees schedulability and is supported by the system. By using a periodic frequency switching scheme that preserves schedulability, instead of this lowest supported fixed frequency, we obtain an additional energy saving up to 18 %. Although the throughput of applications is unchanged by the proposed semi-partitioned approach, the mentioned energy savings come at the cost of increased memory requirements and latency of applications.Keywords Energy efficient multiprocessor scheduling · Energy-efficient design · Real-time multiprocessor scheduling · Model-based design · Embedded streaming systems

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Energy efficient semi-partitioned scheduling for embedded multiprocessor streaming systems

Cannella

Stefanov

2016

Des Autom Embed Syst

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“…In the computer science, there are also some works in reducing energy consumption for the general high-speed microprocessors (typically ≥ 600 MHz) [35][36][37][38][39][40][41][42][43][44][45]. Popular techniques include Dynamic Voltage Scaling (DVS) [38,44], Dynamic Voltage and Frequency Scaling (DVFS) [37,38,44,46,47], Dynamic Power Management (DPM) [35,43], Feedback Control Scheduling (FCS) [36], as well as power-aware task scheduling [36, 39-42, 44, 46-48].…”

Section: Power On Miniaturized Satellitesmentioning

confidence: 99%

“…Apart from the satellite-specific research areas, there are various works in computer science to reduce energy consumption [35][36][37][38][39][40][41][42][43][44][46][47][48]. The popular techniques include Dynamic…”

Section: Reducing Power Consumption Of Satellitementioning

confidence: 99%

Autonomous system clock frequency controller for pico- and nano- satellites

Kamajaya¹

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Power‐aware scheduling with effective task migration for real‐time multicore embedded systems

March

Sahuquillo

Petit

et al. 2012

Concurrency and Computation

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SUMMARYA major design issue in embedded systems is reducing the power consumption since batteries have a limited energy budget. For this purpose, several techniques such as Dynamic Voltage Scaling (DVS) or task migration are being used. DVS allows reducing power by selecting the optimal voltage supply, while task migration achieves this effect by balancing the workload among cores. This paper focuses on power-aware scheduling allowing task migration to reduce energy consumption in multicore embedded systems implementing DVS capabilities. To address energy savings, the devised schedulers follow two main rules: migrations are allowed at specific points of time and only one task is allowed to migrate each time. Two algorithms have been proposed working under real-time constraints. The simpler algorithm, namely, Single Option Migration (SOM) only checks just one target core before performing a migration. In contrast, the Multiple Option Migration (MOM) searches the optimal target core. In general, the MOM algorithm achieves better energy savings than the SOM algorithm, although differences are wider for a reduced number of cores and frequency/voltage levels. Moreover, the MOM algorithm reduces energy consumption as much as 40% over the typical WF algorithm.

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Energy Efficient Scheduling of Real-Time Tasks on Multicore Processors

Cited by 135 publications

References 21 publications

Energy efficient semi-partitioned scheduling for embedded multiprocessor streaming systems

Energy efficient semi-partitioned scheduling for embedded multiprocessor streaming systems

Autonomous system clock frequency controller for pico- and nano- satellites

Power‐aware scheduling with effective task migration for real‐time multicore embedded systems

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