Hybrid computation offloading for smart home automation in mobile cloud computing

被引:0
作者
Jie Zhang
Zhili Zhou
Shu Li
Leilei Gan
Xuyun Zhang
Lianyong Qi
Xiaolong Xu
Wanchun Dou
机构
[1] Nanjing University,State Key Laboratory for Novel Software Technology
[2] Nanjing University of Information Science and Technology,Jiangsu Engineering Centre of Network Monitoring, School of Computer and Software
[3] The University of Auckland,Department of Electrical and Computer Engineering
[4] Qufu Normal University,School of Information Science and Engineering, Chinese Academy of Education Big Data
来源
Personal and Ubiquitous Computing | 2018年 / 22卷
关键词
Home automation; Mobile cloud computing; Cloudlet; Mobile service; Energy consumption;
D O I
暂无
中图分类号
学科分类号
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/∞ 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:13
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