On the Cost-QoE Tradeoff for Cloud-Based Video Streaming Under Amazon EC2's Pricing Models

The emergence of cloud computing provides a costeffective approach to deliver video streams to a large number of end users with the desired user quality-of-experience (QoE). Under such a paradigm, a video service provider (VSP) can launch its own video streaming services virtually, by renting the distribution infrastructure from one or more cloud service providers (CSPs). However, CSPs like Amazon EC2 normally offer multiple pricing options for virtual machine (VM) instances they can provide, such as on-demand instances, reserved instances, and spot instances. Such diverse pricing models make it challenging for a VSP to determine how to optimally procure the required number of VM instances in different types to satisfy dynamic user demands. Given the limited budget, a VSP needs to carefully balance the procurement cost and the achieved QoE for end users. In this paper, we investigate the trade-off between the cost incurred by VM instance procurement and the achieved QoE of end users under Amazon EC2’s pricing models, and formulate the VM instance provisioning and procurement problem into a constrained stochastic optimization problem. By applying the Lyapunov optimization framework, we design an online procurement algorithm, which approaches the optimal solution with explicitly provable upper bounds. We also conduct extensive trace-driven simulations and our results show that our proposed algorithm (OPT-ORS) achieves a good balance between the procurement cost and the user QoE for cloud-based VSPs. In the achieved near-optimal situation, our algorithm guarantees that reserved VM instances are fully utilized to satisfy the baseline user demand, on-demand VM instances are only rent to handle flash crowds, while spot VM instances are rent more frequently than on-demand VM instances to serve user demand over the baseline due to their low prices.