Energy Efficient Soft Real-Time Computing through Cross-Layer Predictive Control

The next decade of computing workloads is expected to be dominated by soft-real time applications such as multimedia and machine vision. Such workloads are characterized by transient spikes requiring over provisioning of compute servers, adversely affecting the cost, energy usage, and environmental impact of data centers. In many of these applications, although deadlines need to be met to provide QoS guarantees, other quality parameters of the application (for example, visual quality in video processing) can be tuned in conjunction with hardware parameters (for example, DVFS) to give acceptable performance under overload conditions. In this paper, we experimentally demonstrate a predictive control approach for improving overload capacity and energy efficiency by incorporating control variables from both the hardware and the application layer. Further, we illustrate the impact of the choice of multiprocessor real-time scheduling algorithms on the performance of the controller for heterogeneous workloads.