{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00240712","sets":["581:11492:11504"]},"path":["11504"],"owner":"44499","recid":"240712","title":["Container Auto-scaling System Using Sliding-Window Regression with Fuzzy Entropy"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-11-15"},"_buckets":{"deposit":"b915b2f6-a8cb-450d-a898-18616080dd05"},"_deposit":{"id":"240712","pid":{"type":"depid","value":"240712","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Container Auto-scaling System Using Sliding-Window Regression with Fuzzy Entropy","author_link":["660835","660836","660834","660833"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Container Auto-scaling System Using Sliding-Window Regression with Fuzzy Entropy"},{"subitem_title":"Container Auto-scaling System Using Sliding-Window Regression with Fuzzy Entropy","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[一般論文（推薦論文）] Cloud computing (IaaS, PaaS, SaaS), Regression, Outlier and anomaly detection, Load distribution and scheduling","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2024-11-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Japan Advanced Institute of Science and Technology"},{"subitem_text_value":"Japan Advanced Institute of Science and Technology"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Japan Advanced Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Japan Advanced Institute of Science and Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/240712/files/IPSJ-JNL6511004.pdf","label":"IPSJ-JNL6511004.pdf"},"date":[{"dateType":"Available","dateValue":"2026-11-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6511004.pdf","filesize":[{"value":"1.8 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"f50f95df-e506-4cb6-ba72-058bd80966d7","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Naoya, Yokoyama"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kiyofumi, Tanaka"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Naoya, Yokoyama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kiyofumi, Tanaka","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_publisher_15":{"attribute_name":"公開者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"When offering services such as e-commerce on the cloud, there is a necessity to adjust the amount of server resources provided in response to the irregularly increasing and decreasing traffic. This need arises when there is a desire to maintain a constant level of service while, at the same time, minimizing costs as much as possible. There is an abundance of prior research regarding the prediction of future loads from past time-series data. Many of these approaches rely on traditional time-series forecasting, which necessitates that the data used for learning adhere to stationary or unit root processes, or they use deep learning approaches that include using a vast amount of data and parameters. In this study, we propose a traffic forecasting method that includes dynamic window size changes that can follow even slight variation in trends. This method incorporates a fuzzy-entropy-based burst traffic detection in the regression estimation with sliding-window learning. In the evaluation, we conduct four comparative experiments using actual traffic rather than simulations. As a result, compared to the baseline, the proposed reduced the number of request failures and improved the Mean Squared Error between the ideal and the actual container count by 26.4 points on average.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.32(2024) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.32.916\n------------------------------","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"When offering services such as e-commerce on the cloud, there is a necessity to adjust the amount of server resources provided in response to the irregularly increasing and decreasing traffic. This need arises when there is a desire to maintain a constant level of service while, at the same time, minimizing costs as much as possible. There is an abundance of prior research regarding the prediction of future loads from past time-series data. Many of these approaches rely on traditional time-series forecasting, which necessitates that the data used for learning adhere to stationary or unit root processes, or they use deep learning approaches that include using a vast amount of data and parameters. In this study, we propose a traffic forecasting method that includes dynamic window size changes that can follow even slight variation in trends. This method incorporates a fuzzy-entropy-based burst traffic detection in the regression estimation with sliding-window learning. In the evaluation, we conduct four comparative experiments using actual traffic rather than simulations. As a result, compared to the baseline, the proposed reduced the number of request failures and improved the Mean Squared Error between the ideal and the actual container count by 26.4 points on average.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.32(2024) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.32.916\n------------------------------","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2024-11-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"11","bibliographicVolumeNumber":"65"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":240712,"updated":"2025-01-19T07:53:36.908017+00:00","links":{},"created":"2025-01-19T01:45:03.276155+00:00"}