TY - GEN
T1 - A Server Migration Method Using Q-Learning with Dimension Reduction in Edge Computing
AU - Urimoto, Ryo
AU - Fukushima, Yukinobu
AU - Tarutani, Yuya
AU - Murase, Tutomu
AU - Yokohira, Tokumi
N1 - Funding Information:
This research has been supported by the Kayamori Foundation of Informational Science Advancement and ROIS NII Open Collaborative Research 2019-20S1202.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/13
Y1 - 2021/1/13
N2 - Edge computing is a promising computing paradigm that satisfies QoS requirements of delay-sensitive applications. In edge computing, server migration control is indispensable for managing client mobility. As a server migration method for edge computing, the method based on Q-learning has been proposed. However, the method assumes that there is only one application client and the number of destination edge servers is limited to one. In this paper, we propose a server migration method using Q-learning that copes with realistic situations where there are multiple application clients and destination edge servers. The contributions of this paper are as follows: 1) we clarify that, under the situation with multiple application clients and multiple destination edge servers, a straightforward server migration method using Q-learning (RL method) does not scale due to state space explosion, and 2) we propose a server migration method using Q-learning (RL-DR method) that reduces the dimensionality of state space by abstracting the numbers of application clients at all locations into a center of the gravity (COG) of application clients. The simulation results show that 1) RL method shows up to 248% worse performance than conventional server migration methods because of state space explosion and 2) RL-DR method achieves up to 38.3% better performance than the conventional methods.
AB - Edge computing is a promising computing paradigm that satisfies QoS requirements of delay-sensitive applications. In edge computing, server migration control is indispensable for managing client mobility. As a server migration method for edge computing, the method based on Q-learning has been proposed. However, the method assumes that there is only one application client and the number of destination edge servers is limited to one. In this paper, we propose a server migration method using Q-learning that copes with realistic situations where there are multiple application clients and destination edge servers. The contributions of this paper are as follows: 1) we clarify that, under the situation with multiple application clients and multiple destination edge servers, a straightforward server migration method using Q-learning (RL method) does not scale due to state space explosion, and 2) we propose a server migration method using Q-learning (RL-DR method) that reduces the dimensionality of state space by abstracting the numbers of application clients at all locations into a center of the gravity (COG) of application clients. The simulation results show that 1) RL method shows up to 248% worse performance than conventional server migration methods because of state space explosion and 2) RL-DR method achieves up to 38.3% better performance than the conventional methods.
KW - Q-learning
KW - edge computing
KW - reinforcement learning
KW - server location decision problem
KW - server migration
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U2 - 10.1109/ICOIN50884.2021.9333965
DO - 10.1109/ICOIN50884.2021.9333965
M3 - Conference contribution
AN - SCOPUS:85100807978
T3 - International Conference on Information Networking
SP - 301
EP - 304
BT - 35th International Conference on Information Networking, ICOIN 2021
PB - IEEE Computer Society
T2 - 35th International Conference on Information Networking, ICOIN 2021
Y2 - 13 January 2021 through 16 January 2021
ER -