Heuristic offloading of concurrent tasks for computation-intensive applications in mobile cloud computing

M. Jia, Jiannong Cao, Lei Yang
{"title":"Heuristic offloading of concurrent tasks for computation-intensive applications in mobile cloud computing","authors":"M. Jia, Jiannong Cao, Lei Yang","doi":"10.1109/INFCOMW.2014.6849257","DOIUrl":null,"url":null,"abstract":"Mobile applications are becoming increasingly computation-intensive, while the computing capacity of mobile devices is limited. A powerful way to reduce completion time of an application is to offload tasks to the cloud for execution. However, online offloading an application with general taskgraph is a difficult task. In this paper we present an online task offloading algorithm that minimizes the completion time of the application on the mobile device. We take cloud service time into account when making an offloading decision and we consider general taskgraphs for offloading. In our algorithm, for sequential tasks (i.e., line topology taskgraphs) we find the optimal offloading of tasks to the cloud. For concurrent tasks (i.e., general topology taskgraphs) we use a load-balancing heuristic to offload tasks to the cloud, such that the parallelism between the mobile and the cloud is maximized. Simulation results show that our algorithm has a performance of at least 85% of the optimal solution, and is significantly better than other existing algorithms.","PeriodicalId":6468,"journal":{"name":"2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"1 1","pages":"352-357"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"149","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INFCOMW.2014.6849257","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 149

Abstract

Mobile applications are becoming increasingly computation-intensive, while the computing capacity of mobile devices is limited. A powerful way to reduce completion time of an application is to offload tasks to the cloud for execution. However, online offloading an application with general taskgraph is a difficult task. In this paper we present an online task offloading algorithm that minimizes the completion time of the application on the mobile device. We take cloud service time into account when making an offloading decision and we consider general taskgraphs for offloading. In our algorithm, for sequential tasks (i.e., line topology taskgraphs) we find the optimal offloading of tasks to the cloud. For concurrent tasks (i.e., general topology taskgraphs) we use a load-balancing heuristic to offload tasks to the cloud, such that the parallelism between the mobile and the cloud is maximized. Simulation results show that our algorithm has a performance of at least 85% of the optimal solution, and is significantly better than other existing algorithms.
移动云计算中计算密集型应用并发任务的启发式卸载
移动应用的计算量越来越大,而移动设备的计算能力是有限的。减少应用程序完成时间的一种强大方法是将任务卸载到云上执行。然而,在线卸载具有通用任务图的应用程序是一项困难的任务。在本文中,我们提出了一种在线任务卸载算法,可以最大限度地减少应用程序在移动设备上的完成时间。在做出卸载决策时,我们会考虑云服务时间,并考虑卸载的一般任务图。在我们的算法中,对于顺序任务(即线拓扑任务图),我们找到了将任务卸载到云的最佳方法。对于并发任务(即一般拓扑任务图),我们使用负载平衡启发式将任务卸载到云上,这样移动设备和云之间的并行性就最大化了。仿真结果表明,该算法的性能至少达到最优解的85%,明显优于其他现有算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信