论文标题
机器学习技术预测视频流视图以降低云服务的成本
Machine Learning Technique Predicting Video Streaming Views to Reduce Cost of Cloud Services
论文作者
论文摘要
视频流占据了最高的在线流量。创建了多个版本的视频，以适合用户的设备规格。在云存储中，将所有版本的经常访问的视频流留在存储库中，从而使视频流提供商支付了大量成本。通常，视频的受欢迎程度在每个时期都会发生变化，这意味着可以删除视频收到的视图数量，因此必须从存储库中删除视频。因此，在本文中，我们开发了一种方法，该方法可以预测下一个时期在存储库中每个视频流的普及。另一方面，我们提出了一种利用视频的预测流行来计算存储成本的算法，然后决定是否将视频保存或从云存储库中删除。实验结果表明，与保持所有视频流相比，云服务的成本降低了15％。
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.
