MEC Data Offloading Strategy for UPF Sinking in 5G Core Network

The explosive growth of Internet data traffic, driven by various resource-intensive and delay-sensitive smart applications supported by 5G communication networks, has posed higher requirements on latency and Quality of Service (QoS). Therefore, the decision-making of data offloading in the new type of core network has become an urgent problem. To address the stochastic optimization problem of data offloading and resource scheduling between terminals and MEC servers, this paper takes into account the randomness of terminal data generation process and dynamics of wireless channels, and constructs a stochastic optimization model for data offloading to meet user QoS requirements. This paper uses Lyapunov optimization theory and Convex optimization method to decompose the random optimization problem into three sub problems for solution, and proposes a MEC data offloading for UPF sinking of 5G core network algorithm (MOUS). Experimental results demonstrate that the MOUS algorithm effectively reduces data offloading and network scheduling costs, significantly improving the performance of the existing network.