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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Vydáno v:IEEE access Ročník 8; s. 185570 - 185583
Hlavní autoři: Lee, Hyunsung, Lee, Jinkyu, Yeom, Ikjun, Woo, Honguk
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3029040