Adaptive Bandwidth Allocation Based on Sample Path Prediction With Gaussian Process Regression

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Traffic prediction facilitates intelligent networking maintenance by enabling efficient network resource allocation. With the development of machine learning algorithms, traffic prediction has attracted increasing attention and has been widely used in resource allocation and traffic management. In this paper, we consider an input traffic with a quality of service (QoS) requirement, such as the overflow probability, and propose an adaptive bandwidth allocation method based on the Gaussian process regression (GPR) to satisfy the required QoS. In the proposed method with GPR, we consider the stochastic property of each sample path individually and compute the required bandwidth adaptively for each sample path by estimating its overflow probability. Thus, it is more beneficial than the bandwidth allocation method based on the average overflow probability over all sample paths that are widely used in many previous works. We investigate the computational complexity and performance of the proposed method through simulation with real-world traffic as well as computer-generated traffic and show that the proposed method allocates the bandwidth adaptively and efficiently to satisfy the required QoS.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2019-10
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, v.18, no.10, pp.4983 - 4996

ISSN
1536-1276
DOI
10.1109/TWC.2019.2931570
URI
http://hdl.handle.net/10203/268333
Appears in Collection
MA-Journal Papers(저널논문)
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