An adaptive heuristic algorithm to solve the network slicing resource management problem

Artificial intelligence-based (AI-based) network slicing bandwidth allocation enables the 5G/6G service providers to create multiple virtual networks atop a shared physical infrastructure while fulfilling varying end-user demands. Some researchers argue that AI-enable network may run the danger of having private information compromised. We still need a backup rule-based methodology to allocate bandwidth resource to each slice, if the AI-based method suddenly encounters security issues. To design such a rule-based methodology, this study attempts to answer two questions: (1) Is the network slicing bandwidth allocation problem the nondeterministic polynomial-time completeness (NP-completeness)? (2) Is there a heuristic methodology without any training process, which has equivalent performance compared to the AI-based methodology? This study first proves the classical network slicing bandwidth allocation problems is NP-completeness. This shows that the designed heuristic method is inescapably suboptimal to the network slicing bandwidth allocation problem. Secondly, this study proposes the Adaptive Hungarian Algorithm (AHA), which outperforms previous AI-empowered method and does not need any training process. The experiments demonstrate that AHA reached 93%-97% of the maximal system throughput by brute-and-force algorithm, compared to other methodologies only having at most 93% of the maximal system throughput. This also indicates that AHA is capable to solve the network slicing bandwidth allocation problem, if the telecommunication operators do not have sufficient sample complexity to train an AI model.