Network Traffic Prediction for Load Balancing in Cloud Access Point Controller

In cluster cloud access controller (AC) solutions, load balancing algorithms typically consider the number of access points (APs), the number of users, the network traffic at the ACs, as well as central processing unit (CPU) and memory usage. However, because the network traffic has bursts and the user traffic on APs is unbalanced, it is not enough to consider only these factors. We report on the development of new traffic prediction models and their use in load balancing algorithms. The methods are evaluated with simulation experiments using MATLAB and CLOUDSIM. The methods utilize phase space reconstruction sequencing of the user network traffic. The result is improved load balancing efficiency when compared with alternative existing approaches. Keywordscloud controller; wireless access point; load balancing; network traffic prediction.

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