Forecast-Assisted Service Function Chain Dynamic Deployment for SDN/NFV-Enabled Cloud Management Systems

• Published in: IEEE systems journal Vol. 17; no. 3; pp. 4371 - 4382
• Main Authors: Zhang, Junning, Liu, Yicen, Li, Zhiwei, Lu, Yu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Delays; Dynamic scheduling; Graph neural networks; Graph neural networks (GNNs); Internet of Things; Machine learning; Management systems; multitask regression; Network function virtualization; network function virtualization (NFV); Nonlinear programming; Prediction models; Predictive models; Resource management; service function chain (SFC); software-defined network; Software-defined networking; Virtual links; Viterbi algorithm detectors
• ISSN: 1932-8184; 1937-9234
• DOI: 10.1109/JSYST.2023.3263865

Software-defined network (SDN) and network function virtualization (NFV) are acknowledged as the most promising technologies to effectively allocate resource for network service. A service function chain (SFC), which can deploy virtualized network functions (VNFs) and chain them with associated flows allocation, can be used to represent each network service owing to the introduction of the SDN/NFV technology. Co-hosted applications on multiple Internet of Things terminals have dynamic and time-varying service requirements, in order to allocate network resources optimally and meet the end-to-end delay requirements of services, sufficient strategies are required to satisfy the continuously changing service demands. In this article, a deep learning model that combines the multitask regression layer above the graph neural networks is first presented to predict the future resource requirements of each VNF instance. The SFC deployment problem is then solved using the integer nonlinear programming (INLP) approach, and a novel prediction-assisted Viterbi algorithm is presented to overcome the scalability problem of the INLP approach. According to the simulation findings, the proposed deep model provides a minimum of a 6.2% improvement in prediction accuracy over baseline prediction models, and the proposed SFC deployment strategy has been demonstrated to deliver better performance in terms of acceptance ratio and revenue, compared to the current passive deployment algorithms.