QoE-Aware Video Storage Power Management Based on Hot and Cold Data Classification.

Dynamically adaptive streaming over HTTP (DASH), the most common streaming technique, requires a video server to store all the transcoded versions, resulting in a lot of storage space, thereby consuming a significant disk power. A disk array can be divided into hot and cold zones to allow cold disks to be spun down, but this poses several questions such as (1) which video segments can be stored on the hot disks, (2) how to allocate video segments among the hot disks, and (3) how to handle requests to the cold disks. To address this, we propose three new algorithms; (1) a hot data classification algorithm to determine which segments should be stored on the hot disks, by taking segment popularity and quality-of-experience (QoE) into account, (2) a video segment allocation algorithm to balance workloads among the hot disks, and (3) a disk bandwidth allocation algorithm which determines the bit-rate of each segment with the aim of maximizing overall QoE. Experimental results show that our scheme can reduce the power consumption between 29% and 46% compared with the method of storing all the transcoded versions at the cost of 1.5% QoE degradation.