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

arXiv: https://arxiv.org/abs/2210.09078 · HTML: https://mlsanity.com/paper/2210.09078/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Cost-Efficient Storage for On-Demand Video Streaming on Cloud](https://mlsanity.com/paper/2007.03410/) (2020)
- [Cost Efficient Repository Management for Cloud-Based On-Demand Video Streaming](https://mlsanity.com/paper/2012.00597/) (2020)
- [Improving Hierarchy Storage for Video Streaming in Cloud](https://mlsanity.com/paper/2104.11317/) (2021)
- [Cost Minimization of Cloud Services for On-Demand Video Streaming](https://mlsanity.com/paper/2204.09423/) (2022)

## Abstract (arXiv)

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.