Energy efficiency has become a key concern in modern computing. Major processor vendors now offer single-ISA heterogeneous processors that combine powerful and energy-efficient cores, such as Arm's big.LITTLE CPUs, Apple's M-series chips, and Intel P/E systems. However, today's OS schedulers, relying on simple cost-based thread allocation strategies, fail to fully exploit their potential.

Rising energy consumption is of growing concern for cloud data center providers. Modern processors try to counteract this problem through low-power idle states that save energy in phases with little demand for compute resources. Making proper use of this feature, however, requires knowledge about the properties of these states for the very processors used in a specific setup; most importantly, the energy consumed in each idle state and the latency for resuming normal operation. Unfortunately, hardware vendors usually do not provide this critical information.

The ever-increasing demand for scalable database systems is limited by their energy consumption, which is one of the major challenges in research today. While existing approaches mainly focused on transaction-oriented disk-based database systems, we are investigating and optimizing the energy consumption and performance of data-oriented scale-up in-memory database systems that make heavy use of the main power consumers, which are processors and main memory. In this demo, we present energy-utility functions as an approach for enabling the operating system to improve the energy efficiency of scalable in-memory database systems. Our highly interactive demo setup mainly allows attendees to switch between multiple DBMS workloads and watch in detail how the system responds by adapting the hardware configuration appropriately.

For embedded system software, it is common to use static mappings of tasks to cores. This becomes considerably more challenging in multi-application scenarios. In this paper, we propose TETRiS, a multi-application run-time system for static mappings for heterogeneous system-on-chip architectures. It leverages compile-time information to map and migrate tasks in a fashion that preserves the predictable performance of using static mappings, allowing the system to accommodate multiple applications. TETRiS runs on off-the-shelf embedded systems and is Linux-compatible. We embed our approach in a state-of-the-art compiler for multicore systems and evaluate the proposed run-time system in a modern heterogeneous platform using realistic benchmarks. We present two experiments whose execution time and energy consumptions are comparable to those obtained by the highly-optimized Linux scheduler CFS, and where execution time variance is reduced by a factor of 510, and energy consumption variance by a factor of 83.