Machine Learning Technique Predicting Video Streaming Views to Reduce Cost of Cloud Services

TL;DR

This paper presents a machine learning approach to predict video streaming popularity, enabling cost-effective storage management by deleting less popular videos, which reduces cloud service costs by 15%.

Contribution

It introduces a novel predictive model combined with an algorithm for dynamic video storage management to optimize cloud costs.

Findings

Cost reduction of 15% in cloud storage expenses.

Effective prediction of video popularity over time.

Improved storage efficiency by deleting low-demand videos.

Abstract

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.