Energy-Efficient Scheduling For Real-Time Tasks In Multi-Resource Uniprocessor And Multicore Systems

With the development of electricity and the embedded system, the energy consumption has come to the limit of both the power supply of batteries and the fan cooling system. Energy conservation is an important issue in the design of embedded systems.In this paper, we will discuss the energy-efficient scheduling problems on two types of embedded systems: cluster-based mulicores and uniprocessor multi-resource systems.Dynamic Voltage Scaling (DVS) and Dynamic Power Management (DPM) are two widely used techniques for saving energy in embedded systems. We address the problem of minimizing total system-wide energy consumption concerning both CPU and devices, i.e., uniprocessor multi-resource. A system is assumed to contain a fixed number of real-time tasks scheduled to run on a DVS-enabled processor, and a fixed number of off-chip devices used by the tasks during their executions. We will study the non-trivial time and energy overhead of device state transitions between active and sleep states. Our goal is to find optimal schedules providing not only the execution order and CPU frequencies of tasks, but also the time points for device state transitions. For simplicity, we adopt the frame-based realtime task model, and develop optimization algorithms based on0-1Integer Non-Linear Programming (0-1INLP) for different system configurations.While much work has addressed the energy-efficient scheduling problem for uniprocessor or multiprocessor systems, little has been done for multicore systems. The voltage DVFS patitions multicore into clusters which manage power individually, and this balances the trade-off between the hardware complexity and power efficiency. We study the multicore architecture with a fixed number of cores partitioned into clusters (or islands), i.e., cluster-based mulicores system. We develop algorithms to determine a schedule for real-time tasks to minimize the energy consumption under the timing and operating frequency constraints. As technical contributions, we first show that the optimal frequencies resulting in the minimum energy consumption for each island is not dependent on the workload mapped but the number of cores and leakage power on the island, when not considering the timing constraint. Then for systems with timing constraints, we present a polynomial algorithm which derives the minimum energy consumption for a given task partition. Finally, we develop an efficient algorithm to determine the number of active islands, task partition and frequency assignment.Our simulation results indicate that our approach can significantly outperform existing techniques in terms of energy savings, under both of the two types of embedded systems.