Resource allocation method for computing power network service function chain

Abstract

The invention relates to a computing power network service function chain resource allocation method, which belongs to the field of edge computing and resource allocation, and comprises the following steps: S1, establishing a user uncertainty service function chain demand model; s2, based on the user uncertainty service function chain demand model, service node computing resources and transmission link network bandwidth resource allocation are jointly considered, and a computing power network service cost optimization problem model is established; s3, converting the optimization problem into a convex problem by using a variable relaxation method, solving by using a dual decomposition method and a subgradient method to obtain an optimal user node association and service link selection result, and obtaining a corresponding resource allocation scheme; s4, restoring the linear solution in the step S3 into a discrete solution by using a heuristic rounding algorithm; and S5, solving the residual traffic engineering problem based on the discrete solution, updating the continuous solution, and obtaining feasible solutions of all variables of the problem. According to the invention, the joint resource cost of the service node and the transmission link can be reduced.

Description

technical field [0001] The invention belongs to the technical field of edge computing and resource allocation, and relates to a computing power network service function chain resource allocation method. Background technique [0002] With the development of 5G and next-generation mobile communication technologies, more and more mobile devices use diverse services in the network. The computing power and transmission resource demands of mobile users for various application services are increasing exponentially. In order to solve the congestion problem of a large amount of data in cloud computing centers, as well as the low latency and differentiated requirements of new applications, edge computing technology has been widely used. Edge computing that can be deployed flexibly shortens the distance between computing resources and mobile users, thereby providing users with low-latency and traffic-localized services, which can effectively reduce network overhead and improve network...

AI Technical Summary

Problems solved by technology

However, there is still little work to apply robust optimization techniques for uncertain demand to the resource allocation optimization of service function chains, thus ignoring the potential of this technology for service function chain applications in edge computing networks

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