Dynamic service function chain arrangement method and system based on deep reinforcement learning

Abstract

The invention discloses a dynamic service function chain arrangement method and system based on deep reinforcement learning. The method comprises the following steps: acquiring a historical network state according to an SDN controller; the network state comprises service function chain request stream information generated in the Internet of Things supporting mobile edge computing and corresponding network resource state information; setting deep reinforcement learning parameters and initializing the weight of the neural network; training a neural network according to an experience sample generated by interaction of the intelligent agent and the environment; for the service function chain request flow obtained in real time, the trained neural network is utilized, a heuristic algorithm is adopted, the placement and routing path of the virtualized network function meeting the requirement of the service function chain request flow is determined and deployed, and network resource state information is comprehensively considered; the load balancing of the network is realized while the resource consumption cost and the time delay of the request stream of the Internet of Things are reduced, and the network flow receiving rate is improved.

Description

technical field [0001] The invention belongs to the technical field of edge computing, and relates to a method and system for arranging dynamic service function chains based on deep reinforcement learning, which are used to solve the problem of virtual network function placement and routing under the background of edge computing. Background technique [0002] With the rapid development of the Internet of Things, the number of Internet of Things terminals is continuously increasing, resulting in massive computing-intensive and delay-sensitive Internet of Things request flows, such as driverless driving, augmented reality, and face recognition. Although traditional cloud computing can provide high computing power for these IoT data, it cannot meet the low latency and low energy consumption requirements of the data. Transmitting data to a remote cloud data center will cause a large network delay and consume a huge amount of bandwidth and transmission resources. In order to sol...

AI Technical Summary

Problems solved by technology

However, in an IoT network supporting mobile edge computing, since edge cloud resources are limited and each type of virtualized network function has more than one instance, the same type of virtualized network function can be deployed on different micro-clouds, This makes it difficult to optimize the placement of virtual network functions and the selection of routing paths to allocate and utilize resources reasonably

However, the existing service function chain deployment methods all have some deficiencies.

Although the traditional accurate method for solving the combinatorial problem can obtain the global optimal solution, it is not suitable for the situation where the network scale and the number of Internet of Things request flows are large; although the heuristic algorithm can obtain a feasible solution within a certain period of time, it is easy to fall into the Local optimal solution and difficult to adapt to dynamic network changes in most cases

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