{"title":"Planning Computation Offloading on Shared Edge Infrastructure for Multiple Drones","authors":"Giorgos Polychronis, S. Lalis","doi":"10.1109/ICDCSW56584.2022.00063","DOIUrl":null,"url":null,"abstract":"Drones are used in a wide range of applications, which may involve computationally-demanding data processing tasks during the missions. While such heavy tasks can be offloaded to nearby edge-servers, this may not always be feasible due to capacity limitations and contention. In this case, it is important to have a fair allocation of server resources to drones. We propose a heuristic for this problem, and evaluate it though simulation experiments using realistic performance parameters. We show that the mission time can be greatly reduced, by up to 33% (16 min) compared to the default where drones perform all computations onboard, while evenly balancing the benefits of offloading among drones with different missions.","PeriodicalId":357138,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDCSW56584.2022.00063","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Drones are used in a wide range of applications, which may involve computationally-demanding data processing tasks during the missions. While such heavy tasks can be offloaded to nearby edge-servers, this may not always be feasible due to capacity limitations and contention. In this case, it is important to have a fair allocation of server resources to drones. We propose a heuristic for this problem, and evaluate it though simulation experiments using realistic performance parameters. We show that the mission time can be greatly reduced, by up to 33% (16 min) compared to the default where drones perform all computations onboard, while evenly balancing the benefits of offloading among drones with different missions.