Cost-efficient video storage system in the cloud using neural enhancement

Abstract

Cloud storage providers offer different pricing tiers based on the access frequency of stored data. This pricing plan offers cost benefits for videos that are accessed less than once per month. However, the stringent requirement falls short in addressing the large number of ”cold” videos stored today. This paper proposes Neural Cloud Storage (NCS), a pioneering approach to address to address these issues. NCS reduces the size of video stored in the cloud by decreasing its resolution and applies neural enhancement, specifically content-aware super-resolution(SR), to achieve the restoration of the video to their original resolution. According to our preliminary cost-benefit analysis, NCS can further save an annual 14% total cost of ownership (TCO) compared to the cheapest AWS storage service for cold video. By reducing the cost, it expands the cold video coverage (from 25% to 38%) that can benefit from the multi-tiered service. As deep learning and computational resources continue to advance, we believe that neural enhancement will revolutionize the field of cloud storage.