A Computation and Storage Trade-off Strategy for Cost-Efficient Video Transcoding in the Cloud

Video transcoding refers to the process of converting a compressed digital video from one format to another. Since it is a compute-intensive operation, transcoding of a large number of on-demand videos requires a large scale cluster of transcoding servers. Moreover, storage of multiple transcoded versions of each source video requires a large amount of disk space. Infrastructure as a Service (IaaS) clouds provide virtual machines (VMs) for creating a dynamically scalable cluster of servers. Likewise, a cloud storage service may be used to store a large number of transcoded videos. Moreover, it may be possible to reduce the total IaaS cost by trading storage for computation, or vice versa. In this paper, we present a computation and storage trade-off strategy for cost-efficient video transcoding in the cloud called cost and popularity score based strategy. The proposed strategy estimates computation cost, storage cost, and video popularity of individual transcoded videos and then uses this information to make decisions on how long a video should be stored or how frequently it should be re-transcoded from a given source video. It is demonstrated in a discrete-event simulation and is evaluated in a series of experiments involving semi synthetic and realistic load patterns.