Computing resource allocation and task unloading method for edge computing of super-dense network

Abstract

A computing resource allocation and task unloading method for super-dense network edge computing comprises the following steps: step 1, establishing a system model based on a super-dense network edgecomputing network of an SDN, and obtaining network parameters; step 2, obtaining parameters required by edge calculation: unloading the parameters to an edge server of a macro base station and an edgeserver connected with a small base station s through local calculation in sequence to obtain an uplink data rate of a transmission calculation task; step 3, adopting a Q-learning scheme to obtain anoptimal computing resource allocation and task unloading strategy; and step 4, adopting a DQN scheme to obtain an optimal computing resource allocation and task unloading strategy. The method is suitable for a dynamic system by stimulating an intelligent agent to find an optimal solution on the basis of learning variables. In a reinforcement learning (RL) algorithm, Q-Learning has good performancein some time-varying networks. A deep learning technology is combined with Q-learning, a learning scheme based on a deep Q network (DQN) is provided, the benefits of mobile equipment and an operatorare optimized at the same time in a time-varying environment, and compared with a method based on Q-learning, the method is shorter in learning time and faster in convergence. The method realizes simultaneous optimization of benefits of mobile devices (MDs) and operators in a time-varying environment based on the DQN.

Description

technical field [0001] The invention belongs to the technical field of intelligent computers, and in particular relates to a computing resource allocation and task offloading method for ultra-dense network edge computing. Background technique [0002] In today's society, the ever-increasing mobile devices (MDs) with innovative applications place unprecedented demands on user experience and network capacity expansion. Ultra-Dense Networks (UDN) can provide sufficient baseband resources and ubiquitous connectivity for widely distributed mobile devices, and Mobile Edge Computing (MEC) can well meet the high computing resource demands of various new IoT applications and low latency requirements. Therefore, the combination of ultra-dense network and mobile edge computing is considered as a promising future technology, which can significantly increase the capacity of the system and extend the cloud computing capacity to the nearest edge server to meet the ever-increasing computin...

AI Technical Summary

Problems solved by technology

However, most previous optimization schemes focus on one-time optimization goals in certain scenarios and situations, and it is difficult to achieve long-term offload performance in changing, real-world environments.

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