# Improving Hierarchy Storage for Video Streaming in Cloud

arXiv: https://arxiv.org/abs/2104.11317 · HTML: https://mlsanity.com/paper/2104.11317/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2021.

## Built on by (most cited first, 1 of 1)

- [Machine Learning Technique Predicting Video Streaming Views to Reduce Cost of Cloud Services](https://mlsanity.com/paper/2210.09078/) (2022)

## Abstract (arXiv)

Frequently accessed video streams are pre-transcoded into several formats to satisfy the characteristics of all display devices. Storing several video stream formats imposes a high cost on video stream providers using the old classical way. Alternatively, cloud providers offer a high flexibility of using their services and at a low cost relatively. Therefore, video stream companies adopted cloud technology to store their video streams. Generally, having all video streams stored in one type of cloud storage, the cost rises gradually. More importantly, the variation of the access pattern to frequently accessed video streams impacts negatively the storage cost and increases it significantly. To optimize storage usage and lower its cost, we propose a method that manages the cloud hierarchy storage. Particularly, we develop an algorithm that operates on parts of different videos that are frequently accessed and stores them in their suitable storage type cloud. Experiments came up with promising results on reducing the cost of using cloud storage by 18.75 %.