Multi-resource joint management strategy for 5 G network slicing based on POMDP

Abstract

Network slicing technology, as one of the key technologies of 5 G networks, can meet the communication needs of different scenarios by creating multiple virtual end-to-end networks on a unified infrastructure. However, how to effectively manage various resources in network slicing to improve service quality and resource utilization has become an urgent problem to be solved. Given this, to achieve joint optimization management such as computing resources and bandwidth resources, reduce network latency, and improve throughput and resource utilization, a network slicing resource management model based on partial observation Markov decision process is proposed. The model under consideration is predicated on partially observed Markov decision processes. Such processes are capable of perceiving changes in network topology and dynamically adjusting resource allocation to adapt to constantly changing network conditions. Furthermore, the model employs a hybrid heuristic value iterative algorithm to optimize computational efficiency, reduce network latency, improve throughput, and enhance resource utilization. After testing, the delay and throughput of the proposed resource management model increased with the increase in the number of service function chains. When the number of service function chains was 70, the delay was about 70 ms, lower than in other models. The throughput was about 250Mbit/s, higher than other models. The resource management model had 85 % and 81 % utilization rates of computing and bandwidth resources, respectively, which were better than other models. The above results indicate that the resource management model based on partially observed Markov decision processes can effectively reduce network latency, improve throughput and resource utilization, and has important application value for resource management of 5 G network slicing.