Resource allocation method for low-delay and high-reliability services in mobile edge computing

Abstract

The invention relates to a resource allocation method for a low-delay high-reliability service in mobile edge computing, and belongs to the technical field of mobile communication. According to the method, in a multi-MEC multi-user environment, a user task queue model and an MEC task queue model are described respectively, a theoretical model of mobile service provider network utility maximizationis established with the task queue overflow probability as the constraint, and joint allocation is performed on power resources, bandwidth resources and computing resources. Considering that a constraint condition in the optimization model comprises a limit constraint of a task queue overflow probability; a random optimization problem of time averaging is converted and decomposed into three sub-problems of single-time-slot solving through a Lyapunov optimization theory, including calculation resource allocation of users, bandwidth and power allocation and calculation resource allocation of MEC, and then the three sub-problems are solved respectively. According to the method, the average total income of the MSP is improved while the requirements of users for low time delay and high reliability are met.

Description

technical field [0001] The invention belongs to the technical field of communications, and relates to a resource allocation method for low-latency and high-reliability services in mobile edge computing. Background technique [0002] With the vigorous development of the Internet of Things, the number of mobile smart terminal equipment (User Equipment, UE) has increased sharply, and computing-intensive and delay-sensitive applications and services are emerging, such as augmented / virtual reality (AR / VR), facial recognition recognition, autonomous driving, etc. Such emerging services not only require abundant computing resources, but also generate high energy consumption. However, mobile smart terminal devices usually have certain limitations in computing power and battery life, making it difficult for devices to support these services. On the other hand, driven by Software Defined Network (Software Defined Network, SDN) and Network Function Virtualization (Network Function Vir...

AI Technical Summary

Problems solved by technology

[0005] First of all, the actual environment of mobile edge computing - multi-MEC and multi-user is ignored, and the scene of single MEC and multi-user is focused on. Compared with the single MEC environment, various network resources in the actual edge computing environment are performing task offloading and resource allocation. Therefore, a single MEC has not obtained a real resource allocation plan; secondly, in the research on resource allocation of MEC, general business scenarios are considered, and there are few scenarios involving 5G low latency and high reliability, while other scenarios There are high requirements for delay and reliability; finally, the research on energy consumption and delay is only focused on without considering the revenue of mobile service providers

In addition, when allocating wireless resources, the optimization of bandwidth resources is ignored, and the bandwidth is directly allocated equally, which reduces the utilization rate of network resources.

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