Dynamic Embedding and Quality of Service-Driven Adjustment for Cloud Networks

• Published in: IEEE transactions on industrial informatics 2020-02, Vol.16 (2), p.1406-1416
• Main Authors: Cao, Haotong, Wu, Shengchen, Aujla, Gagangeet Singh, Wang, Qin, Yang, Longxiang, Zhu, Hongbo
• Format: Article
• Language: English
• Subjects: Algorithms; Bandwidth; Certain virtual elements; Cloud computing; Computer simulation; dynamic embedding; Embedding; End users; Heuristic; Heuristic algorithms; Heuristic methods; Internet service providers; Machine learning; Machine learning algorithms; Mapping; Measurement; Optimization; Production scheduling; Quality of service; quality of service (QoS) demand; Quality of service architectures; reinforcement learning; virtual network; Virtual networks; Virtualization
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2019.2936074

Cloud computing built on virtualization technologies can provide Internet service providers (SPs) with elastic virtualized node and link resources. SPs can outsource their virtualized resources as customized virtual networks (VNs) to end users. Hence, how to efficiently embed these VNs is the core issue in virtualization research. This technical issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied, including the reinforcement learning (RL) approach of machine learning. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. Optimizing the VN QoS performance is beneficial to guaranteeing service quality in cloud computing environment. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed in order to be evaluated in continuous time. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the reembedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be adjusted. The dynamic embedding algorithm ensures flexible VN assignment and fulfills customized QoS demands. Finally, simulation results are illustrated in order to validate the strength of our dynamic algorithm. We perform the comparison with multiple existing dynamic algorithms. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13%.