Presentation 2023-01-19
A Time Series Analysis of Queueing Systems by Using Machine Learning
Tomoyasu Kudo, Takashi Okuda,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) The digital transformation's (DX) market has been expanding rapidly in recent years, and data processing systems that analyze time-series events are important to realize DX. Transient performance evaluation of queueing systems is useful for designing data processing systems. Theoretical analysis is a method to evaluate transient performance, but it is difficult to obtain a solution due to the difficulty of identifying the distribution, arrival rate, and processing rate, as well as the complexity of the theory. In this study, we use machine learning to evaluate the transient performance of queueing systems (M/G/1 and GI/M/1 systems) and analyze time series of events.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Queueing Systems / Machine Learning / Time Series Analysis
Paper # IN2022-54
Date of Issue 2023-01-12 (IN)

Conference Information
Committee IN
Conference Date 2023/1/19(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Aichi Industry & Labor Center
Topics (in Japanese) (See Japanese page)
Topics (in English) Contents Distribution, Social Networking Services, Data Analytics and Processing Platform, Big data, etc.
Chair Kunio Hato(Internet Multifeed)
Vice Chair Tsutomu Murase(Nagoya Univ.)
Secretary Tsutomu Murase(KDDI Research)
Assistant

Paper Information
Registration To Technical Committee on Information Networks
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Time Series Analysis of Queueing Systems by Using Machine Learning
Sub Title (in English)
Keyword(1) Queueing Systems
Keyword(2) Machine Learning
Keyword(3) Time Series Analysis
1st Author's Name Tomoyasu Kudo
1st Author's Affiliation Aichi Prefectural University(Aichi Prefectural Univ.)
2nd Author's Name Takashi Okuda
2nd Author's Affiliation Aichi Prefectural University(Aichi Prefectural Univ.)
Date 2023-01-19
Paper # IN2022-54
Volume (vol) vol.122
Number (no) IN-342
Page pp.pp.13-18(IN),
#Pages 6
Date of Issue 2023-01-12 (IN)