Joint Computation And Communication Resource Allocation Strategy In Mobile-Edge Colud Computing Networks

With the development of mobile Internet, mobile devices have become an indispensable devices in people's lives. The function of mobile applications is more and more powerful, to meet the demand of traveling,shopping, entertainment and other aspects of people's life. However, the computational resources, storage resources and battery capacity of mobile devices are limited, which can not meet the needs of various applications.It is a significant challenge to resolve the contradiction between resource-hungry applications and resource-constrained mobile devices.Computation offloading is envisioned as a promising approach to address such a challenge. By offloading the computation to the nearby resource-rich cloud infrastructure, computation task only runs on the resource-rich cloud infrastructure, without taking the local computing and storage resources, so computation offloading can effectively resolve the contradiction between resource-hungry applications and resource-constrained mobile devices. In addition, computation offloading can enhance the endurance of mobile terminal. The mobile edge computing network can provide strong edge computing and storage services and computation offloading in mobile edge computing network has the advantages of low service delay. However, how to jointly consider the status of the wireless communication resources and the cloud computing resources to select an appropriate MEC service node for computation offloading, is a valuable research issue.The main work of this paper is as follows:First, based on the computation and communication resources, a joint decision making strategy is proposed. Firstly, this paper established MEC service node selection model, which aims at minimize the communication cost between user and MEC service node. Secondly, an algorithm based on computing and communication resources is proposed.Finally, simulation results show that the proposed algorithm is effective.And the algorithm can achieve a lower communication cost and improve the fairness of users.Second, considering the resource constraints of MEC service node, a multi user and multi MEC service node matching scheme is proposed. In this paper, we first describe the network scenarios of multi user and multi MEC service nodes. Secondly, a resource constrained MEC service node matching model is established. The model is based on auction theory,which aims at optimize the revenue of MEC service nodes, and make full use of the resource of MEC service node. Thirdly, to find an appropriate MEC service node, a user service node matching algorithm based on two-dimensional resource auction is proposed. Finally, simulation results show that the proposed algorithm is effective, and the algorithm can make the MEC service nodes obtain higher profits while making full use of the resources.