{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00213187","sets":["1164:1579:10482:10719"]},"path":["10719"],"owner":"44499","recid":"213187","title":["エッジコンピューティングにおける RL ベースの効率的なタスクオフロードと割り当てに向けたメカニズムの開発"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-10-04"},"_buckets":{"deposit":"481ec87a-0964-4c60-baef-eb7d9229ae7a"},"_deposit":{"id":"213187","pid":{"type":"depid","value":"213187","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"エッジコンピューティングにおける RL ベースの効率的なタスクオフロードと割り当てに向けたメカニズムの開発","author_link":["544995","544993","544994","544992"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"エッジコンピューティングにおける RL ベースの効率的なタスクオフロードと割り当てに向けたメカニズムの開発"},{"subitem_title":"Towards an Efficient RL-based Task Offloading and Allocation Mechanism in Edge Computing","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"タスク分割","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-10-04","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"九州産業大学情報科学研究科"},{"subitem_text_value":"九州産業大学情報科学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Shintaro Ide","subitem_text_language":"en"},{"subitem_text_value":"Bernady Apduhan","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"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/213187/files/IPSJ-ARC21246006.pdf","label":"IPSJ-ARC21246006.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-ARC21246006.pdf","filesize":[{"value":"1.3 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"16"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"72d64706-54ff-4a54-8735-fe0fb4cadd49","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"井手, 慎太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"アプドゥハン, ベーナディ"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Shintaro, Ide","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Bernady, Apdeuhan","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10096105","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8574","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，Internet of Things (IoT) の普及によりインターネット技術は急速な発展を遂げている．IoT を用いた新たなアプリケーションとして，自律運転システム，モバイルヘルス，スマートホーム，VR/AR 技術など多岐に渡っている．IoT アプリケーションは生活を豊かにする役割を持つ一方で，従来のモバイルデバイスと比較して個々の生成されるタスクサイズは増大化されており，従来のクラウドコンピューティングでは高遅延を引き起こす場合 があることが分かっている．この問題を解決するための手法としてエッジコンピューティングが登場した．エッジコンピューティングはユーザ機器の付近でタスクを処理するため，従来手法よりも低遅延なサービスを提 供できる一方で，分散配置されたサーバの 1 箇所にタスクが集中した場合，処理能力の低さがボトルネックとなり，高遅延が発生してしまう場合がある．本論文では，近くにあるエッジサーバのパフォーマンスと可用性に基づいて タスクを動的にオフロードする，エッジコンピューティング環境における深層強化学習ベースのオフロードメカニ ズムを提案した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In recent years, the Internet of Things (IoT) has been growing rapidly, and new applications using the IoT include autonomous driving systems, mobile health, smart homes, VR/AR technologies, and many more. While IoT applications play a role in enriching our lives, the size of each generated task is increasing compared to traditional mobile devices, which can cause high latency in traditional cloud computing. Edge computing has emerged as a method to solve this problem. Edge computing can provide services with lower latency than conventional methods because the tasks are processed in the vicinity of the user's device. However, when the tasks are concentrated in one of the distributed servers, the low processing power becomes a bottleneck and high latency may occur. In this paper, we proposed a deep reinforcement learning based offloading mechanism in an edge computing environment which can dynamically offload a task based on the performance and availability of nearby edge servers. Preliminary experiment results is promising and offered insights on related issues. ","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告システム・アーキテクチャ（ARC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-10-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"6","bibliographicVolumeNumber":"2021-ARC-246"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":213187,"updated":"2025-01-19T17:14:30.275998+00:00","links":{},"created":"2025-01-19T01:14:05.636164+00:00"}