Cost-Efficient Cloud-Based Video Streaming Through Measuring Hotness. (25th June 2018) - Darwich, Mahmoud; Salehi, Mohsen Amini; Beyazit, Ege; Bayoumi, Magdy
Abstract: Video streaming providers generally have to store several formats of the same video and stream the appropriate format based on the characteristics of the viewer's device. This approach, called pre-transcoding, incurs a significant cost to the stream providers that rely on cloud services. Furthermore, pre-transcoding proven to be inefficient due to the long-tail access pattern to video streams. To reduce the incurred cost, we propose to pre-transcode only frequently accessed videos (called hot videos) and partially pre-transcode others, depending on their hotness degree. Therefore, we need to measure video stream hotness. Accordingly, we first provide a model to measure the hotness of video streams. Then, we develop methods that operate based on the hotness measure and determine how to pre-transcode videos to minimize the cost of stream providers. The partial pre-transcoding methods operate at different granularity levels to capture different patterns in accessing videos. Particularly, one of the methods operates faster but cannot partially pre-transcode videos with the non-long-tail access pattern. Experimental results show the efficacy of our proposed methods, specifically, when a video stream repository includes a high percentage of the Frequently Accessed Video Streams and a high percentage of videos with the non-long-tail accesses pattern.