Energy efficient scheduling techniques for real-time embedded systems

Battery-powered portable embedded systems have been widely used in many applications. These embedded systems have to concurrently perform a multitude of complex tasks under stringent time constraints. As these systems become more complex and incorporate more functionality, they became more power-hungry. Thus, reducing power consumption and extending battery lifespan while guaranteeing the timing constraints has became a critical aspect in designing such systems. This gives rise to three aspects of research: (i) Guaranteeing the execution of the hard real-time tasks by their deadlines, (ii) Determining the minimum voltage under which each task can be executed, and (iii) Techniques to take advantage of run-time variations in the execution times of tasks. In this research, we present techniques that address the above aspects in single and multi processor embedded systems. We study the performance of the proposed techniques on various benchmarks in terms of energy savings

Scheduling of real time embedded systems for resource and energy minimization by voltage scaling

The aspects of real-time embedded computing are explored with the focus on novel real-time scheduling policies, which would be appropriate for low-power devices. To consider real-time deadlines with pre-emptive scheduling policies will require the investigation of intelligent scheduling heuristics. These aspects for various other RTES models like Multiple processor system, Dynamic Voltage Scaling and Dynamic scheduling are the focus of this thesis. Deadline based scheduling of task graphs representative of real time systems is performed on a multiprocessor system; A set of aperiodic, dependent tasks in the form of a task graph are taken as the input and all the required task parameters are calculated. All the tasks are then partitioned into two or more clusters allowing them to be run at different voltages. Each cluster, thus voltage scaled results in the overall minimization of the power utilized by the system. With the mapping of each task to a particular voltage done, the tasks are scheduled on a multiprocessor system consisting of processors that can run at different voltages and frequencies, in such a way that all the timing constraints are satisfied

Voltage Set-up Problem on Embedded Systems with Multiple Voltages

Dynamic voltage scaling (DVS), arguably the most effective energy reduction technique, can be enabled by having multiple voltages physically implemented on the chip and allowing the operating system to decide which voltage to use at run-time. Indeed, this is predicted as the future low-power system by International Technology Roadmap for Semiconductors (ITRS). There still exist many important unsolved problems on how to reduce the system's dynamic and/or total power by DVS. One of such problems, which we refer to as the voltage set-up problem, is "how many levels and at which values should voltages be implemented for the system to achieve the maximum energy saving". It challenges whether DVS technique's full potential in energy saving can be reached on multiple-voltage systems. In this paper, (1) we derive analytical solutions for dual-voltage system. (2) For the general case that does not have analytic solutions, we develop efficient numerical methods that can take the overhead of voltage switch and leakage into account. (3) We demonstrate how to apply the proposed algorithms on system design. (4) Interestingly, the experimental results, on both real life DSP applications and random created applications, suggest that multiple-voltage DVS systems with only a couple levels of voltages, when set up properly, can be very close to DVS technique's full potential in energy saving. Parts of this report were published in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 13, No. 7, pp. 869-872, July 2005

A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow

Energy-Centric Scheduling for Real-Time Systems

Energy consumption is today an important design issue for all kinds of digital systems, and essential for the battery operated ones. An important fraction of this energy is dissipated on the processors running the application software. To reduce this energy consumption, one may, for instance, lower the processor clock frequency and supply voltage. This, however, might lead to a performance degradation of the whole system. In real-time systems, the crucial issue is timing, which is directly dependent on the system speed. Real-time scheduling and energy efficiency are therefore tightly connected issues, being addressed together in this work. Several scheduling approaches for low energy are described in the thesis, most targeting variable speed processor architectures. At task level, a novel speed scheduling algorithm for tasks with probabilistic execution pattern is introduced and compared to an already existing compile-time approach. For task graphs, a list-scheduling based algorithm with an energy-sensitive priority is proposed. For task sets, off-line methods for computing the task maximum required speeds are described, both for rate-monotonic and earliest deadline first scheduling. Also, a run-time speed optimization policy based on slack re-distribution is proposed for rate-monotonic scheduling. Next, an energy-efficient extension of the earliest deadline first priority assignment policy is proposed, aimed at tasks with probabilistic execution time. Finally, scheduling is examined in conjunction with assignment of tasks to processors, as parts of various low energy design flows. For some of the algorithms given in the thesis, energy measurements were carried out on a real hardware platform containing a variable speed processor. The results confirm the validity of the initial assumptions and models used throughout the thesis. These experiments also show the efficiency of the newly introduced scheduling methods

Experiences in Implementing an Energy-Driven Task Scheduler in RT-Linux

Dynamic voltage scaling (DVS) is being increasingly used for power management in embedded systems. Energy is a scarce resource in embedded real-time systems and energy consumption must be carefully balanced against realtime responsiveness. We describe our experiences in implementing an energy driven task scheduler in RT-Linux. We attempt to minimize the energy consumed by a taskset while guaranteeing that all task deadlines are met. Our algorithm, which we call LEDF, follows a greedy approach and schedules as many tasks as possible at a low CPU speed in a power-aware manner. We present simulation results on energy savings using LEDF, and we validate our approach using the RT-Linux testbed on the AMD Athlon 4 processor. Power measurements taken on the testbed closely match the power estimates obtained using simulation. Our results show that DVS results in significant energy savings for practical real-life task sets. We also show that when CPU speeds are restricted to only a few discrete values, this approach saves more energy than currently existing methods

DYNAMIC VOLTAGE SCALING FOR PRIORITY-DRIVEN SCHEDULED DISTRIBUTED REAL-TIME SYSTEMS

Energy consumption is increasingly affecting battery life and cooling for real- time systems. Dynamic Voltage and frequency Scaling (DVS) has been shown to substantially reduce the energy consumption of uniprocessor real-time systems. It is worthwhile to extend the efficient DVS scheduling algorithms to distributed system with dependent tasks. The dissertation describes how to extend several effective uniprocessor DVS schedul- ing algorithms to distributed system with dependent task set. Task assignment and deadline assignment heuristics are proposed and compared with existing heuristics concerning energy-conserving performance. An admission test and a deadline com- putation algorithm are presented in the dissertation for dynamic task set to accept the arriving task in a DVS scheduled real-time system. Simulations show that an effective distributed DVS scheduling is capable of saving as much as 89% of energy that would be consumed without using DVS scheduling. It is also shown that task assignment and deadline assignment affect the energy- conserving performance of DVS scheduling algorithms. For some aggressive DVS scheduling algorithms, however, the effect of task assignment is negligible. The ad- mission test accept over 80% of tasks that can be accepted by a non-DVS scheduler to a DVS scheduled real-time system