On optimal batching policies for video-on-demand storage servers

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 first-come-first-served (FCFS) and maximum queue length (MOL). Neither of these policies lead to entirely satisfactory results. MQL tends to be too aggressive in scheduling popular videos by only considering the queue length to maximize batch size, while FCFS has the opposite effect. 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 MFQ 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 the best weighting factors for the various videos. A simulation is developed to compare the proposed MFQ policy with FCFS and MQL. Our study shows that MFQ yields excellent empirical results in terms of standard performance measures such as average latency time, defection rates and fairness