Multiobjective Model and Improved Artificial Raindrop Algorithm for Virtual Network Mapping

Abstract

Network virtualization (NV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of 5G communication. Resource scheduling in virtual network mapping is an important problem faced by NV technology. To enhance the performance of the network, optimal resource scheduling schemes should be determined. In general, maximum index of frequency slots used, energy consumption, and the ratio of used frequency slots are three very important indicators for the network. Based on the previous research studies, we first take these three indicators into the objective of the mathematical model and define a novel multiobjective optimization model. Then, the three objectives are integrated into one objective to be minimized by using the sum weighted strategy. Finally, an efficient algorithm, which integrates the advantages of artificial raindrop algorithm (ARA), particle swarm optimization, and differential evolution, is proposed and denoted as IARA. Moreover, simulation experiments have been conducted in several experimental scenes with other compared algorithms. The experimental results show that the proposed algorithm IARA can obtain better scheduling schemes than the compared algorithms.