Machine Learning Technique Predicting Video Streaming Views to Reduce Cost of Cloud Services
This addresses cost reduction for video streaming providers, but it is incremental as it applies existing prediction methods to a specific domain.
The paper tackles the problem of high cloud storage costs for video streaming providers by predicting video popularity and deciding which versions to delete, achieving a 15% cost reduction compared to keeping all videos.
Video streams tremendously occupied the highest portion of online traffic. Multiple versions of a video are created to fit the user's device specifications. In cloud storage, Keeping all versions of frequently accessed video streams in the repository for the long term imposes a significant cost paid by video streaming providers. Generally, the popularity of a video changes each period of time, which means the number of views received by a video could be dropped, thus, the video must be deleted from the repository. Therefore, in this paper, we develop a method that predicts the popularity of each video stream in the repository in the next period. On the other hand, we propose an algorithm that utilizes the predicted popularity of a video to compute the storage cost, and then it decides whether the video will be kept or deleted from the cloud repository. The experiment results show a cost reduction of the cloud services by 15% compared to keeping all video streams.