Massive increases in production and consumption of digital media are motivating cloud "video encoding as a service." In this paper, we explore efficient platforms for supporting such services. Specifically, we explore transcoding performance on a software encoder and two hardware-accelerated encoders representing widely different points in the hardware acceleration design space, as well as interference effects when heterogenous encoders are run concurrently. Using results from our exploratory study, we next propose a framework for providing video encoding as a service on a single server, and implement a prototype of the framework in a system called Catalyst. Catalyst accepts transcoding tasks and intelligently uses all available resources, including software and hardware-accelerated encoders, to maximize throughput while respecting task deadlines, and provides a foundational building block for a cluster-based cloud video encoding service. We evaluate Catalyst using a synthetic transcoding workload designed to emulate an IP-TV/Cloud-DVR workload. Evaluation results show that Catalyst can significantly increase throughput while meeting task deadlines compared to naive use of hardware-accelerated encoding.

Several companies have recently announced plans to build "green" datacenters, i.e. datacenters partially or completely powered by renewable energy. These datacenters will either generate their own renewable energy or draw it directly from an existing nearby plant. Besides reducing carbon footprints, renewable energy can potentially reduce energy costs, reduce peak power costs, or both. However, certain renewable fuels are intermittent, which requires approaches for tackling the energy supply variability. One approach is to use batteries and/or the electrical grid as a backup for the renewable energy. It may also be possible to adapt the workload to match the renewable energy supply. For highest benefits, green datacenter operators must intelligently manage their workloads and the sources of energy at their disposal.