{"title":"带计算接入点的移动云卸载半定松弛方法","authors":"Meng-Hsi Chen, B. Liang, Min Dong","doi":"10.1109/SPAWC.2015.7227025","DOIUrl":null,"url":null,"abstract":"We consider a mobile cloud computing scenario consisting of one user with multiple independent tasks, one computing access point (CAP), and one remote cloud server. The CAP can either process the received tasks from the mobile user or offload them to the cloud, providing additional computation capability over traditional mobile cloud computing systems. We aim to optimize the offloading decision of the user to minimize the overall cost of energy, computation, and delay. It is shown that the problem can be formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. We propose an efficient offloading decision algorithm by semidefinite relaxation and a novel randomization mapping method. Our simulation results show that the proposed algorithm gives nearly optimal performance with only a small number of randomization iterations, and adding CAPs to the traditional dichotomy of mobile devices and remote cloud servers can drastically improve mobile cloud computing performance.","PeriodicalId":211324,"journal":{"name":"2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"75","resultStr":"{\"title\":\"A semidefinite relaxation approach to mobile cloud offloading with computing access point\",\"authors\":\"Meng-Hsi Chen, B. Liang, Min Dong\",\"doi\":\"10.1109/SPAWC.2015.7227025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider a mobile cloud computing scenario consisting of one user with multiple independent tasks, one computing access point (CAP), and one remote cloud server. The CAP can either process the received tasks from the mobile user or offload them to the cloud, providing additional computation capability over traditional mobile cloud computing systems. We aim to optimize the offloading decision of the user to minimize the overall cost of energy, computation, and delay. It is shown that the problem can be formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. We propose an efficient offloading decision algorithm by semidefinite relaxation and a novel randomization mapping method. Our simulation results show that the proposed algorithm gives nearly optimal performance with only a small number of randomization iterations, and adding CAPs to the traditional dichotomy of mobile devices and remote cloud servers can drastically improve mobile cloud computing performance.\",\"PeriodicalId\":211324,\"journal\":{\"name\":\"2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"75\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPAWC.2015.7227025\",\"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 Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPAWC.2015.7227025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A semidefinite relaxation approach to mobile cloud offloading with computing access point
We consider a mobile cloud computing scenario consisting of one user with multiple independent tasks, one computing access point (CAP), and one remote cloud server. The CAP can either process the received tasks from the mobile user or offload them to the cloud, providing additional computation capability over traditional mobile cloud computing systems. We aim to optimize the offloading decision of the user to minimize the overall cost of energy, computation, and delay. It is shown that the problem can be formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. We propose an efficient offloading decision algorithm by semidefinite relaxation and a novel randomization mapping method. Our simulation results show that the proposed algorithm gives nearly optimal performance with only a small number of randomization iterations, and adding CAPs to the traditional dichotomy of mobile devices and remote cloud servers can drastically improve mobile cloud computing performance.