{"title":"基于深度q -网络的协同边缘计算系统智能任务卸载方法","authors":"J. Youn","doi":"10.1109/ICAIIC57133.2023.10067111","DOIUrl":null,"url":null,"abstract":"Recently, various applications using artificial intelligence (AI) are deployed in edge network. In particular, An intelligence applications demanded with high computation and low end-to-end latency are executed on edge computing environments. Thus, in this paper, for the optimization of the resource of edge servers in multi-edge network environments, we propose the intelligent task offloading method based on Deep Q-network that can optimize computation capability of the multi-edge computing environments. For this, first at all, we formulate the problem of multi-edge computing allocation with a Markov decision process and propose the policy for allocating edge resource adopting a deep reinforcement learning algorithm. In the simulation, the results show the proposed method gets a better performance in terms of the end-to-end latency of the offloaded task than the existing methods.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intelligent Task Offloading Method using Deep Q-Network for Collaborative Edge Computing System\",\"authors\":\"J. Youn\",\"doi\":\"10.1109/ICAIIC57133.2023.10067111\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently, various applications using artificial intelligence (AI) are deployed in edge network. In particular, An intelligence applications demanded with high computation and low end-to-end latency are executed on edge computing environments. Thus, in this paper, for the optimization of the resource of edge servers in multi-edge network environments, we propose the intelligent task offloading method based on Deep Q-network that can optimize computation capability of the multi-edge computing environments. For this, first at all, we formulate the problem of multi-edge computing allocation with a Markov decision process and propose the policy for allocating edge resource adopting a deep reinforcement learning algorithm. In the simulation, the results show the proposed method gets a better performance in terms of the end-to-end latency of the offloaded task than the existing methods.\",\"PeriodicalId\":105769,\"journal\":{\"name\":\"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAIIC57133.2023.10067111\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIIC57133.2023.10067111","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intelligent Task Offloading Method using Deep Q-Network for Collaborative Edge Computing System
Recently, various applications using artificial intelligence (AI) are deployed in edge network. In particular, An intelligence applications demanded with high computation and low end-to-end latency are executed on edge computing environments. Thus, in this paper, for the optimization of the resource of edge servers in multi-edge network environments, we propose the intelligent task offloading method based on Deep Q-network that can optimize computation capability of the multi-edge computing environments. For this, first at all, we formulate the problem of multi-edge computing allocation with a Markov decision process and propose the policy for allocating edge resource adopting a deep reinforcement learning algorithm. In the simulation, the results show the proposed method gets a better performance in terms of the end-to-end latency of the offloaded task than the existing methods.