Gaussian Process Regression-Based Traffic Load Balancing for Multimedia Multipath Systems

A Multimedia multipath (MMMP) system aims at offering higher reliability and stability utilizing a variety of communication media and paths. To satisfy quality of service (QoS) requirements, an associated router should estimate the path status and allocate traffic load to each path properly. In this paper, we propose an adaptive load balancing algorithm which does not require any information about the system and the necessary information is estimated through online learning with Gaussian Process Regression (GPR). To this end, we introduce a probing period to collect training data on delay for GPR and estimate the timeout probability of each path using the predictive distributions from GPR. We then analyze and minimize the cost function, the weighted sum of the timeout probabilities of the paths. In the analysis, we propose two approximation methods of the timeout probability without direct calculations, which allows the proposed algorithm to operate in online manner. Through extensive simulations under various scenarios with real-world traffic traces, we demonstrate that the proposed algorithm balances the traffic load properly according to dynamic system conditions. Furthermore, we analyze the impact of probing packets on the network and design the proposed algorithm to reduce the communication overhead due to probing packets.