Task unloading and resource allocation method in uncertain network environment

Abstract

The invention relates to the technical field of wireless communication, in particular to a task unloading and resource allocation method in an uncertain network environment, which comprises the following steps of: modeling a task unloading process into a two-stage unloading model, and optimizing the model into a task unloading and resource allocation problem based on two-stage stochastic programming; adopting a random simulation method for converting the problem into a sample mean value approximation problem, and decoupling the problem into a local computing resource allocation sub-problem, a transmission power and edge computing resource joint allocation sub-problem and an unloading decision sub-problem; solving three sub-problems by adopting a standard Lagrangian multiplier method, a genetic algorithm and time delay estimation and energy consumption budget for analyzing local calculation and edge calculation; and enabling a user to obtain an optimal allocation strategy for task unloading by solving the three sub-problems. According to the method, the requirement of task calculation delay in a network with uncertain delay can be met, and meanwhile, the energy consumption of the system is minimized.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to a task offloading and resource allocation method in an uncertain network environment. Background technique [0002] With the rapid development of IoT technology and the popularization of new 5G / 6G applications, delay-sensitive applications have been widely envisaged, such as virtual reality, driverless driving, and face recognition, which are developing at an unprecedented speed. Mobile Edge Computing (MEC) provides inherently low-latency benefits for latency-sensitive applications by offloading tasks to network edge nodes, such as base stations and wireless access points. [0003] In mobile edge computing, task offloading and resource allocation strategies are often the key factors affecting user offloading delay and energy consumption. In order to improve user service quality (Quality of Experience, QoE) Calculate energy consumption budget, calculation delay, an...

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Problems solved by technology

[0006] Task computing delay is one of the important indicators to ensure user QoE. The above research work has considered the task computing delay, but all neglected the queuing delay of the MEC server task queue.

In the actual MEC network environment, due to the limited computing and storage resources of the MEC server, it is usually unable to quickly respond to massive and sudden computing requests. Therefore, the queuing time of tasks on the MEC server cannot be ignored.

Due to the randomness of task arrival and the time-varying nature of the MEC server task queue, it is unrealistic to obtain an accurate prediction of task queuing time. This uncertainty factor poses severe challenges to traditional task offloading and resource allocation.

In addition, most of the research work only focuses on the energy consumption of the client, and the MEC server with limited computing resources will face a large number of computing-intensive and delay-sensitive user visits, followed by the lack of computing resources and high energy Consumption and other issues

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