Algorithms for power savings

1995

The energy usage of computer systems is becoming an important consideration, especially for battery-operated systems. Various methods for reducing energy consumption have been investigated, both at the circuit level and at the operating systems level. In this paper, we propose a simple model of job scheduling aimed at capturing some key aspects of energy minimization. In this model, each job is to be executed between its arrival time and deadline by a single processor with variable speed, under the assumption that energy usage per unit time, P, is a convex function, of the processor speed s. We give an off-line algorithm that computes, for any set of jobs, a minimum-energy schedule. We then consider some on-line algorithms and their competitive performance for the power function P(s)=sp where p⩾2. It is shown that one natural heuristic, called the Average Rate heuristic, uses at most a constant times the minimum energy required. The analysis involves bounding the largest eigenvalue in matrices of a special type

ACM Transactions on Embedded Computing Systems, 2003

Portable embedded computing systems require energy autonomy. This is achieved by batteries serving as a dedicated energy source. The requirement of portability places severe restrictions on size and weight, which in turn limits the amount of energy that is continuously available to maintain system operability. For these reasons, efficient energy utilization has become one of the key challenges to the designer of battery-powered embedded computing systems. In this paper, we first present a novel analytical battery model, which can be used for the battery lifetime estimation. The high quality of the proposed model is demonstrated with measurements and simulations. Using this battery model, we introduce a new "battery-aware" cost function, which will be used for optimizing the lifetime of the battery. This cost function generalizes the traditional minimization metric, namely the energy consumption of the system. We formulate the problem of battery-aware task scheduling on a single processor with multiple voltages. Then, we prove several important mathematical properties of the cost function. Based on these properties, we propose several algorithms for task ordering and voltage assignment, including optimal idle period insertion to exercise charge recovery. This paper presents the first effort toward a formal treatment of battery-aware task scheduling and voltage scaling, based on an accurate analytical model of the battery behavior.

IEEE Transactions on Industrial Informatics, 2010

The use of mobile devices is often limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries or battery cells over the load to exploit the recovery properties of the batteries helps to extend the overall systems lifetime. Straightforward scheduling schemes, like round robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries. In this paper we compare these scheduling schemes with the optimal scheduling scheme produced with two different modeling approaches: an approach based on a priced-timed automaton model (implemented and evaluated in Uppaal Cora), as well as an analytical approach (partly formulated as non-linear optimization problem) for a slightly adapted scheduling problem. We show that in some cases the results of the simple scheduling schemes (round robin, and best-first) are close to optimal. However, the optimal schedules, computed according to both methods, also clearly show that in a variety of scenarios, the simple schedules are far from optimal.

2004

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. vi

ACM Transactions on Design Automation of Electronic Systems, 2007

This paper proposes a new online voltage scaling (VS) technique for battery-powered embedded systems with real-time constraints. The VS technique takes into account the execution times and discharge currents of tasks to further reduce the battery charge consumption when compared to the recently reported slack forwarding technique , whilst maintaining low online complexity of O(1). Furthermore, we investigate the impact of online rescheduling and remapping on the battery charge consumption for tasks with data dependency which has not been explicitly addressed in the literature and propose a novel rescheduling/remapping technique. Finally, we take leakage power into consideration and extend the proposed online techniques to include adaptive body biasing (ABB) which is used to reduce the leakage power. We demonstrate and compare the efficiency of the presented techniques using seven real-life benchmarks and numerous automatically generated examples.

2010

Abstract The use of mobile devices is often limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries or battery cells over the load to exploit the recovery properties of the batteries helps to extend the overall systems lifetime. Straightforward scheduling schemes, like round-robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries.

2004

Energy consumption is becoming a crucial issue in the design of digital systems especially when considering portable and embedded systems due to their operational dependency on batteries. Since the processor is a major source of energy consumption, energy-aware scheduling strategies that decrease the CPU speed when possible enable to achieve significant energy savings.

2005

In this work we consider battery powered portable systems which either have Field Programmable Gate Arrays (FPGA) or voltage and frequency scalable processors as their main processing element. An application is modeled in the form of a precedence task graph at a coarse level of granularity. We assume that for each task in the task graph several unique design-points are available which correspond to different hardware implementations for FPGAs and different voltagefrequency combinations for processors. It is assumed that performance and total power consumption estimates for each design-point are available for any given portable platfrom, including the peripheral components such as memory and display power usage. We present an iterative heuristic algorithm which finds a sequence of tasks along with an appropriate design-point for each task, such that a deadline is met and the amount of battery energy used is as small as possible. A detailed illustrative example along with a case study of a real-world application of a robotic arm controller which demonstrates the usefulness of our algorithm is also presented.

28th IEEE International Real-Time Systems Symposium (RTSS 2007), 2007

Recent technological advances have opened up several distributed real-time applications involving battery-driven embedded devices with local processing and wireless communication capabilities. Energy management is the key issue in the design and operation of such systems. In this paper, we consider a single-hop networked real-time embedded system where each node supports both dynamic voltage scaling (DVS) and dynamic modulation scaling (DMS) power management techniques to tradeoff time for energy savings. In this model, we address the problem of scheduling periodic complex tasks where each task consists of several precedence constrained message passing sub-tasks. Our contributions towards this problem are two fold. First, we analyze the system level energy-time tradeoffs considering both the computation and communication workloads by defining a novel energy gain metric. We then present static (centralized) and dynamic (distributed) energy gain based slack allocation algorithms which reduce the total energy consumption, while guaranteeing the ready time, deadline and precedence constraints. We compare the performance of the proposed algorithms with several baseline algorithms through simulation studies. Our results show that the proposed algorithms perform significantly better than the baseline algorithms for the simulated conditions. Finally, we identify several interesting energy-aware research problems in the area of networked real-time embedded systems.

Many portable devices rely on batteries for their power supply. The capacity of the batteries is finite, and the duration with which one can use the device is limited by the battery lifetime. Accordingly, to increase the efficiency of these systems, energy consumption and also managing the use of the batteries are too important. Given the characteristics of the nonlinear behaviour of the battery, for maximizing battery life, which is related to the discharge pattern of batteries, is one of np-hard problems. This paper to extending the system lifetime and maximizing the efficiency of the battery, presents a greedy algorithm for dynamic voltage scaling according to battery and power consumption characteristics of the tasks. These tasks have deadline and should be done on the specific time. In order to test the proposed algorithm offered in this paper, we test it with three algorithms to compare the results. Simulation results show that the proposed method (gjtbs) in different conditions (with different workload of the system) maximized systems lifetime