Sparse Regression Model-Based Relearning Architecture for Shortening Learning Time in Traffic Prediction

Takahiro HIRAYAMA  Takaya MIYAZAWA  Masahiro JIBIKI  Ved P. KAFLE  

IEICE TRANSACTIONS on Information and Systems   Vol.E104-D   No.5   pp.606-616
Publication Date: 2021/05/01
Publicized: 2021/02/16
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020NTP0010
Type of Manuscript: Special Section PAPER (Special Section on the Architectures, Protocols, and Applications for the Future Internet)
network function virtualization (NFV),  service function chaining (SFC),  machine learning,  dynamic resource arbitration,  

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Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. To meet multiple quality of service (QoS) requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach to the proactive control of network systems. In this paper, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble executed in a shorter time than that of our previous work. The framework includes a reducing training data method based on sparse model regression. By making a short list of training data derived from the sparse regression model, the relearning time can be reduced to about 57% without degrading provisioning accuracy.