The maximum factor queue length batching scheme for video-on-demand systems

Abstract

In a video-on-demand environment, batching of video requests is often used to reduce I/O demand and improve throughput. Since viewers may defect if they experience long waits, a good video scheduling policy needs to consider not only the batch size but also the viewer defection probabilities and wait times. Two conventional scheduling policies for batching are the first-come-first-served (FCFS) policy, which schedules the video with the longest waiting request, and the maximum queue length (MQL) policy, which selects the video with the maximum number of waiting requests. Neither of these policies leads to entirely satisfactory results. MQL tends to be too aggressive in scheduling popular videos by considering only the queue length to maximize batch size, while FCFS has the opposite effect by completely ignoring the queue length and focusing on arrival time to reduce defections. In this paper, we introduce the notion of factored queue length and propose a batching policy that schedules the video with the maximum factored queue length. We refer to this as the MFQL policy. The factored queue length is obtained by weighting each video queue length with a factor which is biased against the more popular videos. An optimization problem is formulated to solve for the best weighting factors for the various videos. We also consider MFQL implementation issues. A simulation is developed to compare the proposed MFQL variants with FCFS and MQL. Our study shows that MFQL yields excellent empirical results in terms of standard performance measures such as average latency time, defection rates, and fairness.