Hybrid model-data driven network slice reconfiguration by exploiting prediction interval and robust optimization

Wei, F., Qin, S., Feng, G., Sun, Y. , Wang, J. and Ying-Chang, L. (2022) Hybrid model-data driven network slice reconfiguration by exploiting prediction interval and robust optimization. IEEE Transactions on Network and Service Management, 19(2), pp. 1426-1441. (doi: 10.1109/TNSM.2021.3138560)

Abstract

Proactive reconfiguration of network slices according to uncertain traffic demands is essential to improve network resource utilization while ensuring service quality in 5G-and-beyond systems. Existing researches on network slice reconfiguration are either model-driven or data-driven methods. However, model-driven methods may cause resource over-provisioning due to a lack of prediction mechanism, while data-driven methods are unrealistic in inter-slice reconfiguration that involves costly and time-consuming operations such as VNF migration. To address these issues, in this paper, we propose a Hybrid Model-Data driven (HMD) framework that intelligently performs inter-slice reconfiguration by leveraging prediction interval and robust optimization. We design a Prediction Interval-oriented Predictor (PIP) to produce a prediction interval that can bracket the future traffic demand with a prespecified probability. Based on the prediction interval, we design an inter-slice reconfiguration scheme (named box optimizer) to perform fast inter-slice reconfigurations. To tackle the over-conservativeness of the box optimizer, we further design the ellipsoid optimizer with better optimality at a cost of increased complexity. Numerical results demonstrate that the proposed framework can provide high robustness with low power consumption. Meanwhile, the trade-off between the power consumption and the realized robustness can be flexibly adjusted according to the type of slice and the level of traffic demand fluctuations.

• Item Type: Articles
• Additional Information: This work was supported by the National Science Foundation of China under Grant 62071091, the Fundamental Research Funds for the Central Universities under Grant ZYGX2020ZB044, and Huawei Cooperation Program under Grant TC20210316002.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sun, Dr Yao and Feng, Professor Gang
• Authors: Wei, F., Qin, S., Feng, G., Sun, Y., Wang, J., and Ying-Chang, L.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Network and Service Management
• Publisher: IEEE
• ISSN: 1932-4537
• ISSN (Online): 1932-4537
• Published Online: 27 December 2021
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in IEEE Transactions on Network and Service Management 19(2): 1426-1441
• Publisher Policy: Reproduced in accordance with the publisher copyright policy