Research on dynamic allocation of network slicing resources based on OS-MBRL

Abstract

With the growth of business needs of network users, how to achieve dynamic and accurate resource allocation of network slicing is a problem that must be solved in the current network. Considering that traditional modelless reinforcement learning methods require a longer model training time, a dynamic resource allocation method based on OS-MBRL was proposed. The online support vector machines algorithm was utilized to construct a system model that could handle dynamically changing data streams and continuously update the model to adapt to new data, ensuring a lower number of SLA violations when allocating fewer resources. Simulation experiment results show that compared with NAF algorithm, DQN algorithm, and TD3 algorithm, the proposed method can reduce SLA violations by up to 80% and resource allocation by 9%.

Keywords

• network slicing
• resource allocation
• reinforcement learning
• online SVM algorithm