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Prediction Based Efficient Online Bandwidth Allocation Method

delete2019-12-01
delete5
PRE
AI
J
Jeong-Seop Kim
G
Ganguk Hwang *
DOI：10.1109/LCOMM.2019.2947895delete
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Abstract

Abstract

En 中文
In this letter, we propose an efficient online bandwidth allocation method based on Gaussian process regression (GPR) in order to guarantee given quality of service (QoS) requirement and to prevent a waste of resources. To this end, we analyze large-buffer asymptotics for Gaussian queues including non-stationary input processes, and derive the necessary bandwidth to meet the requirement. Our experimental results demonstrate the proposed method satisfies the QoS requirement for both synthetic traffic and real-world traffic. We also show that the proposed algorithm performs better than the existing algorithm in terms of resource efficiency.
Keywords:
Bandwidth
Quality of service
Channel allocation
Ground penetrating radar
Queueing analysis
Prediction algorithms
Gaussian processes
Traffic prediction
adaptive bandwidth allocation
Gaussian process regression
large-buffer asymptotics
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

IEEE Communications Letters cover
IEEE Communications Letters
IF:
4.4
Papers:
1.3W
Citations:
2.2W

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