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2021 | OriginalPaper | Buchkapitel

An Online Planning Agent to Optimize the Policy of Resources Management

verfasst von : Aditya Shrivastava, Aksha Thakkar, Vipul Chudasama

Erschienen in: Proceedings of Second International Conference on Computing, Communications, and Cyber-Security

Verlag: Springer Singapore

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Abstract

Reinforcement learning-based systems have received a lot of attention in various domains in recent years. In such domains, an autonomous agent learns from environment to provide a solution. Resource scheduling is considered as research challenge where such autonomous agent optimizes the solutions. This work is presented as an investigation on the effectiveness of various algorithms which drives actions associated with autonomous agent. We give a detailed contention between three differing algorithms—Q-learning, Dyna-Q, and deep-Q-network, given the task of effectively allocating the resources in an online basis. Among the mentioned algorithms, the Q-learning and deep-Q-network, which are model free algorithms, have remained in wide use for planning. However, this paper focuses on highlighting the effectiveness of lesser-known model-based algorithm, Dyna-Q. The experiment results show agent-based policy derived by the Dyna-Q algorithm which provides optimized resource scheduling for the current environment.

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Literatur
1.
Zurück zum Zitat Arel I, Liu C, Urbanik T, Kohls AG (2010) Reinforcement learning-based multi-agent system for network traffic signal control. IET Intell Transp Syst 4(2):128–135 Arel I, Liu C, Urbanik T, Kohls AG (2010) Reinforcement learning-based multi-agent system for network traffic signal control. IET Intell Transp Syst 4(2):128–135
3.
Zurück zum Zitat Dutreilh X, Kirgizov S, Melekhova O, Malenfant J, Rivierre N, Truck I (2011) Using reinforcement learning for autonomic resource allocation in clouds: towards a fully automated workflow Dutreilh X, Kirgizov S, Melekhova O, Malenfant J, Rivierre N, Truck I (2011) Using reinforcement learning for autonomic resource allocation in clouds: towards a fully automated workflow
7.
Zurück zum Zitat Hameed K, Ali A, Jabbar M, Junaid M, Haider A, Naqvi M (2016) Resource management in operating system—a survey of scheduling algorithms Hameed K, Ali A, Jabbar M, Junaid M, Haider A, Naqvi M (2016) Resource management in operating system—a survey of scheduling algorithms
8.
Zurück zum Zitat Heller B, Seetharaman S, Mahadevan P, Yiakoumis Y, Sharma P, Banerjee S, McKeown N (2010) Elastictree: Saving energy in data center networks, pp 249–264 Heller B, Seetharaman S, Mahadevan P, Yiakoumis Y, Sharma P, Banerjee S, McKeown N (2010) Elastictree: Saving energy in data center networks, pp 249–264
10.
Zurück zum Zitat Jiang J, Das R, Ananthanarayanan G, Chou PA, Padmanabhan V, Sekar V, Dominique E, GoliszewskiM,Kukoleca D,Vafin R et al (2016) Via: Improving internet telephony call quality using predictive relay selection. In: Proceedings of the 2016 ACM SIGCOMM conference, pp 286–299 Jiang J, Das R, Ananthanarayanan G, Chou PA, Padmanabhan V, Sekar V, Dominique E, GoliszewskiM,Kukoleca D,Vafin R et al (2016) Via: Improving internet telephony call quality using predictive relay selection. In: Proceedings of the 2016 ACM SIGCOMM conference, pp 286–299
11.
Zurück zum Zitat Karthiban K, Raj JS (2020) An efficient green computing fair resource allocation in cloud computing using modified deep reinforcement learning algorithm Karthiban K, Raj JS (2020) An efficient green computing fair resource allocation in cloud computing using modified deep reinforcement learning algorithm
12.
Zurück zum Zitat Kober J, Bagnell JA, Peters J (2012) Reinforcement learning in robotics: a survey. Int J Robot Res 32:1238–1274 Kober J, Bagnell JA, Peters J (2012) Reinforcement learning in robotics: a survey. Int J Robot Res 32:1238–1274
14.
Zurück zum Zitat Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I,Wierstra D, Riedmiller M (2013) Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013) Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I,Wierstra D, Riedmiller M (2013) Playing atari with deep reinforcement learning. arXiv preprint arXiv:​1312.​5602 (2013)
16.
Zurück zum Zitat Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489. https://doi.org/10.1038/nature16961 Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489. https://​doi.​org/​10.​1038/​nature16961
18.
Zurück zum Zitat Tesauro G, Jong NK, Das R, Bennani MN (2006) A hybrid reinforcement learning approach to autonomic resource allocation. In: 2006 IEEE international conference on autonomic computing, pp 65–73 Tesauro G, Jong NK, Das R, Bennani MN (2006) A hybrid reinforcement learning approach to autonomic resource allocation. In: 2006 IEEE international conference on autonomic computing, pp 65–73
19.
Zurück zum Zitat Viet H, An S, Chung T (2011) Extended dyna-q algorithm for path planning of mobile robots. J Meas Sci Instrum 2(3):283–287 Viet H, An S, Chung T (2011) Extended dyna-q algorithm for path planning of mobile robots. J Meas Sci Instrum 2(3):283–287
20.
Zurück zum Zitat Zhang W, Dietterich TG (1995) A reinforcement learning approach to job-shop scheduling. In: IJCAI, vol 95. Citeseer, pp 1114–1120 Zhang W, Dietterich TG (1995) A reinforcement learning approach to job-shop scheduling. In: IJCAI, vol 95. Citeseer, pp 1114–1120
Metadaten
Titel
An Online Planning Agent to Optimize the Policy of Resources Management
verfasst von
Aditya Shrivastava
Aksha Thakkar
Vipul Chudasama
Copyright-Jahr
2021
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-16-0733-2_33

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