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.
Highlights
With the rapid development of the Internet of things (IoT) [1], the increasing number of smart devices are emerging, such as eldercare and childcare application [2], [3], for the intelligent living environment to bring more convenience to people’s lives
We study the cost and energy efficiency of task offloading in a hierarchical network architecture that consists of edge computing and cloud computing
To the best of our knowledge, it is the first time to study the problem of cost and energy efficiency in a multi-layer network for smart home
Summary
With the rapid development of the Internet of things (IoT) [1], the increasing number of smart devices are emerging, such as eldercare and childcare application [2], [3], for the intelligent living environment to bring more convenience to people’s lives. Users need to consider the lease computing resources (from edge server or cloud server) to meet the offload demand and the energy consumption of a smart home. We study the computation offloading among multiple mobile devices in an integrative network of MEC and MCC, in which we minimize the mobile devices’ overhead of cost and energy. We model the overhead of each mobile user as the weighted-sum of the computation cost and energy consumption; We formulate the problem of joint transmission power and resource allocation for minimizing the overhead while complying with the Signal-to-Interference-plus-Noise Ratio (SINR) and task deadline. 2) Because the formulated problem is MILP, the branch and bound is the most effective solution to solve it, while the approach high a time overhead.
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