Efficient Computation Offloading in Edge Computing Enabled Smart Home

作     者：Yu, Bocheng Zhang, Xingjun You, Ilsun Khan, Umer Sadiq 

作者机构：Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China Soonchunhyang Univ Dept Informat Secur Engn Asan 31538 South Korea 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2021年第9卷

页      面：48631-48639页

核心收录：

基　　金：National Key Research and Development Program of China [2017YFB1001701] Soonchunhyang University Research Fund 

主　　题：Task analysis Servers Cloud computing Smart homes Edge computing Energy consumption Internet of Things Deep learning integer linear programming mobile edge computing smart home task offloading 

摘      要：Mobile edge computing which provides computing capabilities at the edge of the radio access network can help smart home reduce response time. However, the limited computing capacity of edge servers is the bottlenecks for the development of edge computing. We integrate cloud computing and edge computing in the Internet of Things to expand the capabilities. Nevertheless, the cost of leasing computing resources has been seldom considered. In this paper, we study the joint transmission power and resource allocation to minimize the users overhead which is measured by the sum of energy consumption and cost leasing servers. We formulate the problem as a Mixed Integer Linear Programming which is NP-hard and present the Branch-and-Bound to solve it. Due to high complexity, a learning method is proposed to accelerate the algorithm. The branching policy can be learned to reduce the time-cost of the Branch-and-Bound algorithm. Simulation results show our approach can improve the Branch-and-Bound efficiency and performs closely to the traditional branching scheme.