Base station computing force resource load balancing strategy for distributed machine learning

With the emergence of terminal services such as VR, Internet of Vehicles, and autonomous driving that require enormous computing resources and network transmission resources, the computing power and network load of existing 5G base stations have been difficult to bear. Mobile edge computing technology effectively integrates the two technologies of mobile network and Internet, adding computing, storage, data processing and other functions on the mobile network side, which building an open platform to implant applications. The existing technology has shortcomings such as insufficient computing power of communication base stations, limited resources of a single edge computing node, etc., It is difficult to meet the development needs of the industry. Therefore, this paper proposes a base station computing power load balancing method based on distributed machine learning. Through distributed machine learning, the communication base station is used as a computing node, and the idle computing power of the base station is called to achieve a reasonable allocation of the computing power resources of the base station.