Shu Zhang, Mingjun Xiao, Guoju Gao, Yin Xu, He Sun
{"title":"卸载任务到未知的边缘服务器:上下文多武装强盗方法","authors":"Shu Zhang, Mingjun Xiao, Guoju Gao, Yin Xu, He Sun","doi":"10.1109/INFOCOMWKSHPS57453.2023.10226047","DOIUrl":null,"url":null,"abstract":"Mobile Edge Computing (MEC), envisioned as an innovative paradigm, pushes resources from the cloud to the network edge and prompts users to offload computation-intensive and data-intensive tasks to edge servers for meeting the stringent service requirements. Prior approaches often study efficiently offloading tasks with given system information, though rigorously time-sensitive tasks offloading problems receive less attention under system uncertainty. As motivated, we propose a multi-user collaborative offloading model where users jointly decide time-sensitive task placement while considering the unknown system information and contexts. We formulate the offloading problem as a Multi-user Contextual Combinatorial Multi-armed Bandit (MCC-MAB) problem and propose a learning algorithm Context-Aware Task Offloading Decision (CATOD) to explore the system uncertainty. Since the time-sensitive task offloading problem with learned system information is still NP-hard, we present an approximation algorithm Offline Generalized Task Assignment (OGTA) to obtain an efficient offloading solution. Additionally, meticulous theoretical analysis and extensive evaluations demonstrate the significant performance on a real-world dataset.","PeriodicalId":354290,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Offloading Tasks to Unknown Edge Servers: A Contextual Multi-Armed Bandit Approach\",\"authors\":\"Shu Zhang, Mingjun Xiao, Guoju Gao, Yin Xu, He Sun\",\"doi\":\"10.1109/INFOCOMWKSHPS57453.2023.10226047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mobile Edge Computing (MEC), envisioned as an innovative paradigm, pushes resources from the cloud to the network edge and prompts users to offload computation-intensive and data-intensive tasks to edge servers for meeting the stringent service requirements. Prior approaches often study efficiently offloading tasks with given system information, though rigorously time-sensitive tasks offloading problems receive less attention under system uncertainty. As motivated, we propose a multi-user collaborative offloading model where users jointly decide time-sensitive task placement while considering the unknown system information and contexts. We formulate the offloading problem as a Multi-user Contextual Combinatorial Multi-armed Bandit (MCC-MAB) problem and propose a learning algorithm Context-Aware Task Offloading Decision (CATOD) to explore the system uncertainty. Since the time-sensitive task offloading problem with learned system information is still NP-hard, we present an approximation algorithm Offline Generalized Task Assignment (OGTA) to obtain an efficient offloading solution. Additionally, meticulous theoretical analysis and extensive evaluations demonstrate the significant performance on a real-world dataset.\",\"PeriodicalId\":354290,\"journal\":{\"name\":\"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INFOCOMWKSHPS57453.2023.10226047\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INFOCOMWKSHPS57453.2023.10226047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Offloading Tasks to Unknown Edge Servers: A Contextual Multi-Armed Bandit Approach
Mobile Edge Computing (MEC), envisioned as an innovative paradigm, pushes resources from the cloud to the network edge and prompts users to offload computation-intensive and data-intensive tasks to edge servers for meeting the stringent service requirements. Prior approaches often study efficiently offloading tasks with given system information, though rigorously time-sensitive tasks offloading problems receive less attention under system uncertainty. As motivated, we propose a multi-user collaborative offloading model where users jointly decide time-sensitive task placement while considering the unknown system information and contexts. We formulate the offloading problem as a Multi-user Contextual Combinatorial Multi-armed Bandit (MCC-MAB) problem and propose a learning algorithm Context-Aware Task Offloading Decision (CATOD) to explore the system uncertainty. Since the time-sensitive task offloading problem with learned system information is still NP-hard, we present an approximation algorithm Offline Generalized Task Assignment (OGTA) to obtain an efficient offloading solution. Additionally, meticulous theoretical analysis and extensive evaluations demonstrate the significant performance on a real-world dataset.