Efficient Computation Offloading in Edge Computing Enabled Smart Home

Abstract

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.

Keywords

• Deep learning
• integer linear programming
• mobile edge computing
• smart home
• task offloading