Optimizing Video Streaming Costs in the Cloud: A CNN Approach

As demand for high-quality, on-demand content skyrockets, cost optimization of video streaming in cloud en-vironments has become critical. Addressing this issue, our research introduces Deep Stream Cost (DSC), an innovative deep learning architecture for optimizing cloud-based video streaming costs. DSC employs convolutional neural networks (CNNs), dynamic resource scaling, and task offloading strategies, achieving considerable cost reduction while ensuring optimal video analytics performance. Our results are promising: system efficiency improved by up to 40%, resource usage was curtailed by 30%, and latency was reduced by 25%, all of which are substantial gains over traditional methods. Beyond cost reduction, DSC enhances scalability and availability, thanks to its real-time monitoring, predictive resource provisioning, and cost-awareness feedback loop. DSC's contribution to the development of cost-effective and scalable video delivery systems is highly relevant in today's era of cloud-based services and increasing demand for efficient resource utilization.