The performance and energy consumption of three embedded real-time operating systems

Baynes, K.; Collins, Chris; Fiterman, Eric; Ganesh, Brinda; Kohout, Paul; Smit, Christine; Zhang, Tiebing; Jacob, Bruce

doi:10.1145/502217.502253

Cited by 25 publications

(3 citation statements)

References 30 publications

(22 reference statements)

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“…In the past, the energy behavior of embedded and real-time operating systems has been evaluated [8,2,27]. In [9], a fullsystem energy-aware simulator is developed and the necessity of simulating OS energy is quantified.…”

Section: Figure 1 % Of Energy Dissipated By the Os On The Experimentmentioning

confidence: 99%

Run-time modeling and estimation of operating system power consumption

John

2003

Proceedings of the 2003 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

126

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The increasing constraints on power consumption in many computing systems point to the need for power modeling and estimation for all components of a system. The Operating System (OS) constitutes a major software component and dissipates a significant portion of total power in many modern application executions. Therefore, modeling OS power is imperative for accurate software power evaluation, as well as power management (e.g. dynamic thermal control and equal energy scheduling) in the light of OS-intensive workloads. This paper characterizes the power behavior of a commercial OS across a wide spectrum of applications to understand OS energy profiles and then proposes various models to cost-effectively estimate its run-time energy dissipation. The proposed models rely on a few simple parameters and have various degrees of complexity and accuracy. Experiments show that compared with cycle-accurate full-system simulation, the model can predict cumulative OS energy to within 1% accuracy for a set of benchmark programs evaluated on a high-end superscalar microprocessor. When applied to track runtime OS energy profiles, the proposed routine level OS power model offers superior accuracy than a simpler, flat OS power model, yielding per-routine estimation error of less than 6%. The most striking observation is the strong correlation between power consumption and the instructions per cycle (IPC) during OS routine executions. Since tools and methodology to measure IPC exist on modern microprocessors, the proposed models can estimate OS power for run-time dynamic thermal and energy management.

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Section: Figure 1 % Of Energy Dissipated By the Os On The Experimentmentioning

confidence: 99%

Run-time modeling and estimation of operating system power consumption

John

2003

Proceedings of the 2003 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

126

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“…Li et al [2001] presented issues concerning the implementation of a low power OS and demonstrated the energy advantage of the event-driven TinyOS over a general-purpose OS (eCos). Baynes et al [2001] compared the energy consumption of three real-time OSs on a Motorola M•CORE processor [Motorola Inc. 1998].…”

Section: Related Workmentioning

confidence: 99%

Energy macromodeling of embedded operating systems

Tan

Raghunathan

Jha

2005

ACM Trans. Embed. Comput. Syst.

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As embedded systems get more complex, deployment of embedded operating systems (OSs) as software run-time engines has become common. In particular, this trend is true even for batterypowered embedded systems, where maximizing battery life is a primary concern. In such OS-driven embedded software, the overall energy consumption depends very much on which OS is used and how the OS is used. Therefore, the energy effects of the OS need to be studied in order to design low-energy systems effectively.In this paper, we discuss the motivation for performing OS energy characterization and propose a methodology to perform the characterization systematically. The methodology consists of two parts. The first part is analysis, which is concerned with identifying a set of components that can be used to characterize the OS energy consumption, called energy characteristics. The second part is macromodeling, which is concerned with obtaining quantitative macromodels for the energy characteristics. It involves the process of experiment design, data collection, and macromodel fitting. The OS energy macromodels can be used conveniently as OS energy estimators in high-level or architectural optimization of embedded systems for low-energy consumption.As far as we know, this work is the first attempt to systematically tackle energy macromodeling of an embedded OS. To demonstrate our approach, we present experimental results for two wellknown embedded OSs, namely, µC/OS and embedded Linux OS.

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“…Recently, the energy behavior of embedded, realtime operating systems has been studied in [15,16]. In [7], a fullsystem energy simulator is developed and the necessity of simulating OS energy is quantified.…”

Section: Related Workmentioning

confidence: 99%

Routine based OS-aware microprocessor resource adaptation for run-time operating system power saving

John

2003

Proceedings of the 2003 International Symposium on Low Power Electronics and Design - ISLPED '03

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The increasingly constrained power budget of today's microprocessor has resulted in a situation where power savings of all components in a system have to be taken into consideration. Operating System (OS) is a major power consumer in many modern applications execution. This paper advocates a routine based OS-aware microprocessor resource adaptation mechanism targeting run-time OS power savings. Simulation results show that compared with the existing sampling-based adaptation schemes, this novel methodology yields more attractive power and performance trade-off on the OS execution. To our knowledge, this paper is the first step to address the power saving issue of the OS itself, an increasingly important area that has been largely overlooked in the previous studies.

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The performance and energy consumption of three embedded real-time operating systems

Cited by 25 publications

References 30 publications

Run-time modeling and estimation of operating system power consumption

Run-time modeling and estimation of operating system power consumption

Energy macromodeling of embedded operating systems

Routine based OS-aware microprocessor resource adaptation for run-time operating system power saving

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