Hybrid computation offloading for smart home automation in mobile cloud computing

被引:37
作者
Zhang, Jie [1 ]
Zhou, Zhili [2 ]
Li, Shu [1 ]
Gan, Leilei [1 ]
Zhang, Xuyun [3 ]
Qi, Lianyong [4 ]
Xu, Xiaolong [2 ]
Dou, Wanchun [1 ]
机构
[1] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210023, Jiangsu, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Jiangsu Engn Ctr Network Monitoring, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China
[3] Univ Auckland, Dept Elect & Comp Engn, Auckland, New Zealand
[4] Chinese Acad Educ Big Data, Qufu Normal Univ, Sch Informat Sci & Engn, Rizhao 276826, Peoples R China
基金
国家重点研发计划; 美国国家科学基金会;
关键词
Home automation; Mobile cloud computing; Cloudlet; Mobile service; Energy consumption; GENETIC ALGORITHM; OPTIMIZATION;
D O I
10.1007/s00779-017-1095-0
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Smart home automation enables the users to realize the access control of the in-home appliances by the mobile devices. With the rapid development of mobile cloud computing, offloading computation workloads of the home automation applications to nearby cloudlets has been treated as a promising approach to overcoming inherent flaws of portable devices, such as low battery capacity. The computing capacity of cloudlet is limited compared with the distant public cloud whose elastic computation resources are almost infinite. Therefore, some mobile services should wait for the occupied computation resources in the cloudlet to get released, which is less energy-efficient. In view of this challenge, we model the waiting time spending in the cloudlet as a M/M/m/infinity queue and propose a hybrid computation offloading algorithm for home automation applications to minimize the total energy consumption of the mobile devices within a given constant deadline. The proposed algorithm combines cloudlet with public clouds, providing a more energy-efficient offloading strategy for home automation applications. Technically, a particle swarm optimization (PSO)-based heuristic algorithm is implemented to schedule mobile services. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of our proposed algorithm.
引用
收藏
页码:121 / 134
页数:14
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