Online Traffic Allocation for Video Service Providers in Cloud-Edge Cooperative Systems

Currently, with the popularity of live video applications, VSPs (video service providers) begin to use cloud servers to enhance user experience and reduce operational costs. In this paper, we consider VSPs leveraging a cloud-edge cooperative model to deliver video services for cost reduction. Since bandwidth costs make up a significant portion of VSPs' operating expenses, we mainly consider bandwidth cost optimizations in traffic allocation. In addition, the QoE (quality of experience) is also very important, while the latency has a larger impact on QoE. Thus our traffic allocation approach aims to strike a fine balance between minimizing bandwidth cost and bounding the latency experienced by clients. Such a trade-off is difficult to optimize with some prevailing bandwidth billing methods such as the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$95^{th}$</tex-math></inline-formula> percentile bandwidth billing. We quantify such a trade-off by constructing a linear bandwidth cost optimization problem. We first describe the offline version of the optimization problem, and then design an online greedy algorithm that considers minimizing the current bandwidth cost at each time slot. By applying the Lyapunov optimization framework, we design another online algorithm based on the original greedy one. We prove that the time average delay achieved by our online algorithm is smaller than the upper bound we set when certain conditions are satisfied. Through extensive simulation experiments, we show that the proposed online algorithm can significantly reduce both the bandwidth cost and the time average delay of clients.