We consider the problem of speeding scaling to conserve energy in a distributedsetting where there are precedence constraints between tasks, and where the performance measure is the makespan. That is, we consider an energy bounded versionof the classic problem P | prec | Cmax. We show that, without loss of generality,one need only consider constant power schedules. We then show how to reduce this problem to the problem Q | prec | Cmax to obtain a poly-log(m)-approximation algorithm.

Slowdown factors determine the extent of slowdown a computing system can experience based on functional and performance requirements. Dynamic Voltage Scaling (DVS) of a processor based on slowdown factors can lead to considerable energy savings. The problem of DVS in the presence of task synchronization has not yet been addressed. We compute slowdown factors for tasks which synchronize for access to shared resources. Tasks synchronize to enforce mutually exclusive access to these resources and can be blocked by lower priority tasks. We compute static slowdown factors for the tasks which guarantee meeting all the task deadlines. Our simulation experiments show on an average 25% energy gains over the known slowdown techniques.

Abstract—This paper studies the important interaction between parallelization and energy consumption in a parallelizable application. Given the ratio of serial and parallel portion in an application and the number of processors, we first derive the optimal frequencies allocated to the serial and parallel regions in the application to minimize the total energy consumption, while the execution time is preserved (i.e., speedup = 1). We show that dynamic energy improvement due to parallelization has a function rising faster with the increasing number of processors than the speed improvement function given by the well-known Amdahl’s Law. Furthermore, we determine the conditions under which one can obtain both energy and speed improvement, as well as the amount of improvement. The formulas we obtain capture the fundamental relationship between parallelization, speedup, and energy consumption and can be directly utilized in energy aware processor resource management. Our results form a basis for several interesting research directions in the area of power and energy aware parallel processing. Index Terms—Parallel processors, power management. 1

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An ad hoc grid is a wireless heterogeneous computing environment without a fixed infrastructure. The wireless devices have different capabilities, have limited battery capacity, support dynamic voltage scaling, and are expected to be used for eight hours at a time and then recharged. To maximize the performance of the system, it is essential to assign resources to tasks (match) and order the execution of tasks on each resource (schedule) in a manner that exploits the heterogeneity of the resources and tasks while considering the energy constraints of the devices. In the single-hop ad hoc grid heterogeneous environment considered in this study, tasks arrive unpredictably, are independent (i.e., no precedent constraints for tasks), and have priorities and deadlines. The problem is to map (match and schedule) tasks onto devices such that the number of highest priority tasks completed by their deadlines during eight hours is maximized while efficiently utilizing the overall system energy. A model for dynamically mapping tasks onto wireless devices is introduced. Seven dynamic mapping heuristics for this environment are designed and compared to each other and to a mathematical bound. 1.

Abstract—An ad hoc grid is a wireless heterogeneous computing environment without a fixed infrastructure. This study considers wireless devices that have different capabilities, have limited battery capacity, support dynamic voltage scaling, and are expected to be used for eight hours at a time and then recharged. To maximize the performance of the system, it is essential to assign resources to tasks (match) and order the execution of tasks on each resource (schedule) in a manner that exploits the heterogeneity of the resources and tasks while considering the energy constraints of the devices. In the single-hop ad hoc grid heterogeneous environment considered in this study, tasks arrive unpredictably, are independent (i.e., no precedent constraints for tasks) and have priorities and deadlines. The problem is to map (match and schedule) tasks onto devices such that the number of highest priority tasks completed by their deadlines during eight hours is maximized while efficiently utilizing the overall system energy. A model for dynamically mapping tasks onto wireless devices is introduced. Seven dynamic mapping heuristics for this environment are designed and compared to each other and to a mathematical bound. Index Terms—Ad hoc, distributed heterogeneous computing, dynamic resource allocation/management, dynamic voltage scaling, energy-aware computing, task priorities and deadlines. Ç 1

Power aware computing has become popular recently and many techniques have been proposed to manage processor energy consumption for traditional real-time applications. In this paper, we are concerned mainly with the AND/OR model of real-time applications that have different execution paths consisting of different tasks. The contribution of this paper is twofold.

Modern computing systems often adopt multiple processing elements to enhance the computing capability or reduce the power consumption, especially for embedded systems. Such configurations impose challenges on energy efficiency in hardware and software implementations. This paper targets energy-efficient task partitioning for real-time tasks on a platform with two heterogeneous processing elements (processors), in which each one has its own characteristics on power consumption and job execution. This paper proposes a general framework for different hardware configurations in energy/power consumption. The framework provides a fully polynomial-time approximation scheme (FPTAS) to derive a solution with energy consumption very close to the optimal energy consumption in tolerable time/space complexity. Experimental results reveal that the proposed framework is effective in energy efficiency. Keywords: Energy-efficient task partition, Real-time systems, DualCore systems. 1

Abstract — Autonomous robot systems have to manage their energy wisely in order to complete their missions. Typical approaches seek to conserve energy by energy-efficient motion or sensor planning. This paper puts forth a distributed systems approach to power management. Specifically, it develops and presents AutoPower, which is a model that characterizes robot software systems ’ computation and communication energy behaviors. With AutoPower, it is possible to make principled decisions about (1) where to deploy software components across the distributed computing resources of autonomous robotic systems, and (2) how the different systems involved should communicate to best meet overall mission objectives. We showcase AutoPower by using a multi-robot search-and-rescue mission as a guiding application. For this scenario, application of the model shows that there are counterintuitive energy trade-offs in configuring such application software. Further, by using AutoPower to guide deployment and interconnects at runtime, for certain configurations, overall computing system lifetimes can be increased by up to 57 % over a base-line configuration. I.

Energy consumption is increasingly affecting battery life and cooling for computer systems. Dynamic Voltage and frequency Scaling (DVS) has been shown to substantially reduce the amount of power required for uniprocessor and multiprocessor real-time systems that have independent tasks or a statically computed schedule. However, no DVS algorithm has been demonstrated for tasks with precedence constraints and using a priority-driven scheduler. Static scheduling relies an offline scheduling algorithms to determine voltage assignments. More flexible scheduling is needed for applications like target tracking, in which tasks arrive and depart the system frequently at run time. This paper describes how to schedule end-to-end tasks with DVS on priority-driven partitioned real-time multiprocessor systems. DVS scheduling can reduce energy consumption to as little as 11 % of energy consumption without DVS.