{"title":"卸载(只)正确的工作:使用马尔可夫决策过程进行稳健卸载","authors":"Esa Hyytiä, T. Spyropoulos, J. Ott","doi":"10.1109/WoWMoM.2015.7158127","DOIUrl":null,"url":null,"abstract":"We consider a dynamic offloading problem arising in the context of mobile cloud computing (MCC). In MCC, three types of tasks can be identified: (i) those which can be processed only locally in a mobile device, (ii) those which are processed in the cloud, and (iii) those which can be processed either in the mobile or in the cloud. For type (iii) tasks, it is of interest to consider when they should be processed locally and when in the cloud. Furthermore, for both type (ii) and (iii) tasks, there is typically two ways to access the cloud: via a (costly) cellular connection or via intermittently available WLAN hotspots. The optimal strategy involves multi-dimensional considerations such as the availability of WLAN hotspots, energy consumption, communication costs and the expected delays. We approach this challenging problem in the framework of Markov decision processes and derive a near-optimal offloading policy.","PeriodicalId":221796,"journal":{"name":"2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"36","resultStr":"{\"title\":\"Offload (only) the right jobs: Robust offloading using the Markov decision processes\",\"authors\":\"Esa Hyytiä, T. Spyropoulos, J. Ott\",\"doi\":\"10.1109/WoWMoM.2015.7158127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider a dynamic offloading problem arising in the context of mobile cloud computing (MCC). In MCC, three types of tasks can be identified: (i) those which can be processed only locally in a mobile device, (ii) those which are processed in the cloud, and (iii) those which can be processed either in the mobile or in the cloud. For type (iii) tasks, it is of interest to consider when they should be processed locally and when in the cloud. Furthermore, for both type (ii) and (iii) tasks, there is typically two ways to access the cloud: via a (costly) cellular connection or via intermittently available WLAN hotspots. The optimal strategy involves multi-dimensional considerations such as the availability of WLAN hotspots, energy consumption, communication costs and the expected delays. We approach this challenging problem in the framework of Markov decision processes and derive a near-optimal offloading policy.\",\"PeriodicalId\":221796,\"journal\":{\"name\":\"2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"36\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WoWMoM.2015.7158127\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WoWMoM.2015.7158127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Offload (only) the right jobs: Robust offloading using the Markov decision processes
We consider a dynamic offloading problem arising in the context of mobile cloud computing (MCC). In MCC, three types of tasks can be identified: (i) those which can be processed only locally in a mobile device, (ii) those which are processed in the cloud, and (iii) those which can be processed either in the mobile or in the cloud. For type (iii) tasks, it is of interest to consider when they should be processed locally and when in the cloud. Furthermore, for both type (ii) and (iii) tasks, there is typically two ways to access the cloud: via a (costly) cellular connection or via intermittently available WLAN hotspots. The optimal strategy involves multi-dimensional considerations such as the availability of WLAN hotspots, energy consumption, communication costs and the expected delays. We approach this challenging problem in the framework of Markov decision processes and derive a near-optimal offloading policy.