{"title":"Sequential Task Scheduling for Mobile Edge Computing Using Genetic Algorithm","authors":"A. Al-Habob, O. Dobre, A. G. Armada","doi":"10.1109/GCWkshps45667.2019.9024374","DOIUrl":null,"url":null,"abstract":"In this paper, we consider sequential task offloading to multiple mobile-edge computing servers to providing ultra-reliable low- latency mobile edge computing. The task consists of a set of sub-tasks, with a general dependency model among sub-tasks. Our objective is to minimize both latency and offloading failure probability by scheduling sub-tasks to servers. We formulate an optimization problem with constraints over binary scheduling decision variables. A genetic algorithm is devised to solve the formulated optimization problems. Simulation results show that the proposed algorithm provides performance close to the optimal solution, which is obtained through exhaustive search.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Globecom Workshops (GC Wkshps)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCWkshps45667.2019.9024374","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
In this paper, we consider sequential task offloading to multiple mobile-edge computing servers to providing ultra-reliable low- latency mobile edge computing. The task consists of a set of sub-tasks, with a general dependency model among sub-tasks. Our objective is to minimize both latency and offloading failure probability by scheduling sub-tasks to servers. We formulate an optimization problem with constraints over binary scheduling decision variables. A genetic algorithm is devised to solve the formulated optimization problems. Simulation results show that the proposed algorithm provides performance close to the optimal solution, which is obtained through exhaustive search.