{"title":"计算卸载决策算法中的代价求解模型","authors":"Mi Swe Zar Thu, Ei Chaw Htoon","doi":"10.1109/IEMCON.2018.8615089","DOIUrl":null,"url":null,"abstract":"The integration of cloud computing and mobile devices, known as Mobile Cloud Computing (MCC), allows the adoption of offloading techniques for improving compute intensive applications' performance and minimize the energy consumption. Deciding to offload some computing tasks or not is a way to solve the limitations of battery life and computing capability of mobile devices. In this paper, to alleviate the computational burden of mobile devices, we present a cost estimation weight factor for computation offloading in mobile devices. To make the right decisions as to whether or not to perform task offloading, based on the energy cost of the methods. The experimental results will demonstrate that the proposed cost estimator can significantly reduce energy consumption of mobile device as well as execution time of application.","PeriodicalId":368939,"journal":{"name":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Cost Solving Model in Computation Offloading Decision Algorithm\",\"authors\":\"Mi Swe Zar Thu, Ei Chaw Htoon\",\"doi\":\"10.1109/IEMCON.2018.8615089\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The integration of cloud computing and mobile devices, known as Mobile Cloud Computing (MCC), allows the adoption of offloading techniques for improving compute intensive applications' performance and minimize the energy consumption. Deciding to offload some computing tasks or not is a way to solve the limitations of battery life and computing capability of mobile devices. In this paper, to alleviate the computational burden of mobile devices, we present a cost estimation weight factor for computation offloading in mobile devices. To make the right decisions as to whether or not to perform task offloading, based on the energy cost of the methods. The experimental results will demonstrate that the proposed cost estimator can significantly reduce energy consumption of mobile device as well as execution time of application.\",\"PeriodicalId\":368939,\"journal\":{\"name\":\"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEMCON.2018.8615089\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMCON.2018.8615089","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cost Solving Model in Computation Offloading Decision Algorithm
The integration of cloud computing and mobile devices, known as Mobile Cloud Computing (MCC), allows the adoption of offloading techniques for improving compute intensive applications' performance and minimize the energy consumption. Deciding to offload some computing tasks or not is a way to solve the limitations of battery life and computing capability of mobile devices. In this paper, to alleviate the computational burden of mobile devices, we present a cost estimation weight factor for computation offloading in mobile devices. To make the right decisions as to whether or not to perform task offloading, based on the energy cost of the methods. The experimental results will demonstrate that the proposed cost estimator can significantly reduce energy consumption of mobile device as well as execution time of application.