Resource Provisioning and Profit Maximization for Transcoding in Clouds: A Two-Timescale Approach

Transcoding is widely adopted for content adaptation; however, it may incur excessive resource consumption and processing delays. Taking advantage of cloud infrastructure, cloud-based transcoding can elastically allocate resources under time-varying workloads and perform multiple transcodings in parallel to reduce delays. To provide transcoding as a cloud service, cloud transcoding systems require some intelligent mechanisms to provision resources and schedule tasks to satisfy user requirements while maximizing financial profit. To this end, we propose a two-timescale stochastic optimization framework for maximizing service profit while achieving performance requirements by jointly provisioning resources and scheduling tasks under a hierarchical control architecture. Our method analytically integrates service revenue, processing delay, and resource consumption in one optimization framework. We derive the offline exact solution and design some approximate online solutions for task scheduling and resource provisioning. We implement an open source cloud transcoding system, called Morph, and evaluate the performance of our method in a real environment. Empirical studies verify that our method can reduce resource consumption and achieve a higher profit compared with baseline schemes.