SLOOP: QoS-Supervised Loop Execution to Reduce Energy on Heterogeneous Architectures

Most systems allocate computational resources to each executing task without any actual knowledge of the application’s Quality-of-Service (QoS) requirements. Such best-effort policies lead to overprovisioning of the resources and increase energy loss. This work assumes applications with soft QoS requirements and exploits the inherent timing slack to minimize the allocated computational resources to reduce energy consumption. We propose a lightweight progress-tracking methodology based on the outer loops of application kernels. It builds on online history and uses it to estimate the total execution time. The prediction of the execution time and the QoS requirements are then used to schedule the application on a heterogeneous architecture with big out-of-order cores and small (LITTLE) in-order cores and select the minimum operating frequency, using DVFS, that meets the deadline. Our scheme is effective in exploiting the timing slack of each application. We show that it can reduce the energy consumption by more than 20% without missing any computational deadlines