Panda: Reinforcement Learning-Based Priority Assignment for Multi-Processor Real-Time Scheduling

Recently, deep reinforcement learning (RL) technologies have been considered as a feasible solution for tackling combinatorial problems in various research and engineering areas. Motivated by this recent success of RL-based approaches, in this paper, we focus on how to utilize RL technologies in the...
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Autor*in:	Hyunsung Lee [verfasserIn] Jinkyu Lee [verfasserIn] Ikjun Yeom [verfasserIn] Honguk Woo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Priority assignment global fixed priority scheduling encoder-decoder neural network reinforcement learning real-time system

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 185570-185583
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:185570-185583

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3029040

Katalog-ID:	DOAJ062528912

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spelling	10.1109/ACCESS.2020.3029040 doi (DE-627)DOAJ062528912 (DE-599)DOAJ961388e6875947a59002e60fd6418040 DE-627 ger DE-627 rakwb eng TK1-9971 Hyunsung Lee verfasserin aut Panda: Reinforcement Learning-Based Priority Assignment for Multi-Processor Real-Time Scheduling 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, deep reinforcement learning (RL) technologies have been considered as a feasible solution for tackling combinatorial problems in various research and engineering areas. Motivated by this recent success of RL-based approaches, in this paper, we focus on how to utilize RL technologies in the context of real-time system research. Specifically, we first formulate the problem of fixed-priority assignments for multi-processor real-time scheduling, which has long been considered challenging in the real-time system community, as a combinatorial problem. We then propose the RL-based priority assignment model Panda that employs (i) a taskset embedding mechanism driven by attention-based encoder-decoder deep neural networks, hence enabling to efficiently extract useful features from the dynamic relation of periodic tasks. We also present two optimization schemes tailored to adopt RL for real-time task scheduling problems: (ii) the response time analysis (RTA)-based policy gradient RL and guided learning schemes, which facilitate the training processes of the Panda model. To the best of our knowledge, our approach is the first to employ RL for real-time task scheduling. Through various experiments, we show that Panda is competitive with well-known heuristic algorithms for real-time task scheduling upon a multi-processor platform, and it often outperforms them in large-scale non-trivial settings, e.g., achieving an average 7.7% enhancement in schedulability ratio for a testing system configuration of 64-sized tasksets and an 8-processor platform. Priority assignment global fixed priority scheduling encoder-decoder neural network reinforcement learning real-time system Electrical engineering. Electronics. Nuclear engineering Jinkyu Lee verfasserin aut Ikjun Yeom verfasserin aut Honguk Woo verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 185570-185583 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:185570-185583 https://doi.org/10.1109/ACCESS.2020.3029040 kostenfrei https://doaj.org/article/961388e6875947a59002e60fd6418040 kostenfrei https://ieeexplore.ieee.org/document/9214485/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 185570-185583
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author	Hyunsung Lee
spellingShingle	Hyunsung Lee misc TK1-9971 misc Priority assignment misc global fixed priority scheduling misc encoder-decoder neural network misc reinforcement learning misc real-time system misc Electrical engineering. Electronics. Nuclear engineering Panda: Reinforcement Learning-Based Priority Assignment for Multi-Processor Real-Time Scheduling
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topic_title	TK1-9971 Panda: Reinforcement Learning-Based Priority Assignment for Multi-Processor Real-Time Scheduling Priority assignment global fixed priority scheduling encoder-decoder neural network reinforcement learning real-time system
topic	misc TK1-9971 misc Priority assignment misc global fixed priority scheduling misc encoder-decoder neural network misc reinforcement learning misc real-time system misc Electrical engineering. Electronics. Nuclear engineering
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title	Panda: Reinforcement Learning-Based Priority Assignment for Multi-Processor Real-Time Scheduling
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title_sort	panda: reinforcement learning-based priority assignment for multi-processor real-time scheduling
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title_auth	Panda: Reinforcement Learning-Based Priority Assignment for Multi-Processor Real-Time Scheduling
abstract	Recently, deep reinforcement learning (RL) technologies have been considered as a feasible solution for tackling combinatorial problems in various research and engineering areas. Motivated by this recent success of RL-based approaches, in this paper, we focus on how to utilize RL technologies in the context of real-time system research. Specifically, we first formulate the problem of fixed-priority assignments for multi-processor real-time scheduling, which has long been considered challenging in the real-time system community, as a combinatorial problem. We then propose the RL-based priority assignment model Panda that employs (i) a taskset embedding mechanism driven by attention-based encoder-decoder deep neural networks, hence enabling to efficiently extract useful features from the dynamic relation of periodic tasks. We also present two optimization schemes tailored to adopt RL for real-time task scheduling problems: (ii) the response time analysis (RTA)-based policy gradient RL and guided learning schemes, which facilitate the training processes of the Panda model. To the best of our knowledge, our approach is the first to employ RL for real-time task scheduling. Through various experiments, we show that Panda is competitive with well-known heuristic algorithms for real-time task scheduling upon a multi-processor platform, and it often outperforms them in large-scale non-trivial settings, e.g., achieving an average 7.7% enhancement in schedulability ratio for a testing system configuration of 64-sized tasksets and an 8-processor platform.
abstractGer	Recently, deep reinforcement learning (RL) technologies have been considered as a feasible solution for tackling combinatorial problems in various research and engineering areas. Motivated by this recent success of RL-based approaches, in this paper, we focus on how to utilize RL technologies in the context of real-time system research. Specifically, we first formulate the problem of fixed-priority assignments for multi-processor real-time scheduling, which has long been considered challenging in the real-time system community, as a combinatorial problem. We then propose the RL-based priority assignment model Panda that employs (i) a taskset embedding mechanism driven by attention-based encoder-decoder deep neural networks, hence enabling to efficiently extract useful features from the dynamic relation of periodic tasks. We also present two optimization schemes tailored to adopt RL for real-time task scheduling problems: (ii) the response time analysis (RTA)-based policy gradient RL and guided learning schemes, which facilitate the training processes of the Panda model. To the best of our knowledge, our approach is the first to employ RL for real-time task scheduling. Through various experiments, we show that Panda is competitive with well-known heuristic algorithms for real-time task scheduling upon a multi-processor platform, and it often outperforms them in large-scale non-trivial settings, e.g., achieving an average 7.7% enhancement in schedulability ratio for a testing system configuration of 64-sized tasksets and an 8-processor platform.
abstract_unstemmed	Recently, deep reinforcement learning (RL) technologies have been considered as a feasible solution for tackling combinatorial problems in various research and engineering areas. Motivated by this recent success of RL-based approaches, in this paper, we focus on how to utilize RL technologies in the context of real-time system research. Specifically, we first formulate the problem of fixed-priority assignments for multi-processor real-time scheduling, which has long been considered challenging in the real-time system community, as a combinatorial problem. We then propose the RL-based priority assignment model Panda that employs (i) a taskset embedding mechanism driven by attention-based encoder-decoder deep neural networks, hence enabling to efficiently extract useful features from the dynamic relation of periodic tasks. We also present two optimization schemes tailored to adopt RL for real-time task scheduling problems: (ii) the response time analysis (RTA)-based policy gradient RL and guided learning schemes, which facilitate the training processes of the Panda model. To the best of our knowledge, our approach is the first to employ RL for real-time task scheduling. Through various experiments, we show that Panda is competitive with well-known heuristic algorithms for real-time task scheduling upon a multi-processor platform, and it often outperforms them in large-scale non-trivial settings, e.g., achieving an average 7.7% enhancement in schedulability ratio for a testing system configuration of 64-sized tasksets and an 8-processor platform.
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title_short	Panda: Reinforcement Learning-Based Priority Assignment for Multi-Processor Real-Time Scheduling
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