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Prediction Based Efficient Online Bandwidth Allocation Method
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DOI：10.1109/LCOMM.2019.2947895.png)
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Abstract
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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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