{"id":3771,"date":"2019-04-29T07:41:17","date_gmt":"2019-04-29T07:41:17","guid":{"rendered":"http:\/\/network.ee.tsinghua.edu.cn\/niulab\/?p=3771"},"modified":"2020-09-04T07:05:08","modified_gmt":"2020-09-04T07:05:08","slug":"adaptive-learning-based-task-offloading-for-vehicular-edge-computing-systems","status":"publish","type":"post","link":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/?p=3771","title":{"rendered":"Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems"},"content":{"rendered":"<p><span class=\"paper_subtitle\">LANGUAGE English <\/span><\/p>\n<p><span class=\"paper_subtitle\"><span class=\"paper_subtitle\">SOURCE<\/span>\u00a0 <strong><em>IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY<\/em><\/strong>, Vol: 68 No: 4 pp: 3061-3074, APR 2019<\/span><\/p>\n<p><span class=\"paper_subtitle\"><span class=\"paper_subtitle\">Published Date<\/span>:\u00a0APR 2019<\/span><\/p>\n<p><span class=\"paper_subtitle\">ABSTRACT<\/span><\/p>\n<p>The vehicular edge computing system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states, and computing workloads. These uncertainties bring extra challenges to task offloading. In this paper, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning based task offloading (ALTO) algorithm based on the multi-armed bandit theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with inputawareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to 30% compared with the existing upper confidence bound based learning algorithm.<\/p>\n","protected":false},"excerpt":{"rendered":"<p><a href=\"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/wp-content\/uploads\/2019\/12\/Adaptive-Learning-Based-Task-Offloading-for-Vehicular-Edge-Computing-Systems.pdf\" target=\"_blank\"><img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-117\" title=\"pdf\" src=\"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/wp-content\/uploads\/2010\/08\/pdf.gif\"alt=\"\" width=\"95\" height=\"50\" \/><\/a>\tYuxuan Sun, Xueying Guo, Jinhui Song, Sheng Zhou, Zhiyuan Jiang, Xin Liu, Zhisheng Niu, Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems, <span class=\"papersource\">IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, Vol: 68 No: 4 pp: 3061-3074, APR 2019 <\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[7],"tags":[100],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts\/3771"}],"collection":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3771"}],"version-history":[{"count":1,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts\/3771\/revisions"}],"predecessor-version":[{"id":3806,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts\/3771\/revisions\/3806"}],"wp:attachment":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3771"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3771"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3771"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}