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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Veröffentlicht in:	IEEE access Jg. 8; S. 185570 - 185583
Hauptverfasser:	Lee, Hyunsung, Lee, Jinkyu, Yeom, Ikjun, Woo, Honguk
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial neural networks Coders Combinatorial analysis encoder-decoder neural network Encoders-Decoders Feature extraction global fixed priority scheduling Heuristic algorithms Heuristic task scheduling Machine learning Microprocessors Optimization Priority assignment Processor scheduling Program processors Real time real-time system Real-time systems reinforcement learning Response time Scheduling Task analysis
ISSN:	2169-3536, 2169-3536
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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.
AbstractList	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.
Author	Lee, Hyunsung Lee, Jinkyu Woo, Honguk Yeom, Ikjun
Author_xml	– sequence: 1 givenname: Hyunsung orcidid: 0000-0001-9814-9548 surname: Lee fullname: Lee, Hyunsung organization: Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea – sequence: 2 givenname: Jinkyu orcidid: 0000-0002-2332-1996 surname: Lee fullname: Lee, Jinkyu organization: Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, South Korea – sequence: 3 givenname: Ikjun surname: Yeom fullname: Yeom, Ikjun organization: Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, South Korea – sequence: 4 givenname: Honguk orcidid: 0000-0001-6948-3440 surname: Woo fullname: Woo, Honguk email: hwoo@skku.edu organization: Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, South Korea
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SubjectTerms	Algorithms Artificial neural networks Coders Combinatorial analysis encoder-decoder neural network Encoders-Decoders Feature extraction global fixed priority scheduling Heuristic algorithms Heuristic task scheduling Machine learning Microprocessors Optimization Priority assignment Processor scheduling Program processors Real time real-time system Real-time systems reinforcement learning Response time Scheduling Task analysis
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Title	Panda: Reinforcement Learning-Based Priority Assignment for Multi-Processor Real-Time Scheduling
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