Fine-Grained Energy Estimation and Optimization of Embedded Operating Systems

Zhao, Xuetong; Guo, Yao; Wang, Hua; Chen, Xiangqun

doi:10.1109/icess.symposia.2008.58

Cited by 10 publications

(9 citation statements)

References 9 publications

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“…They operate at different abstraction levels and have different estimation techniques. Zhao et al [2] propose a new approach to estimate and optimize the energy consumption of the embedded operating system and the applications at a finegrained level. The work is based on power model and a new estimation model for operating system energy consumption.…”

Section: Related Workmentioning

confidence: 99%

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Energy Characterization and Classification of Embedded Operating System Services

Ouni

Belleudy

Senn³

2012

2012 15th Euromicro Conference on Digital System Design

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This paper presents an energy estimation approach for embedded low power operating systems (OS). The methodology consists in characterizing and estimating energy and power overheads of a set of embedded OS basic services : scheduling, context switch and inter-process communication. We analyze the impact of hardware and software parameters like processor frequency and scheduling policy on the energy consumption. Then, energy and power models are extracted and the OS energy overhead of H264 application is estimated using the STORM tool. For the measurement setup, the hardware platform used is OMAP35x EVM board running Linux omap as operating system.

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Section: Related Workmentioning

confidence: 99%

“…The function describes the variation of with , . We compute the energy consumption of the service following this equation (2).…”

Section: Energy Characterization and Estimation Flowmentioning

confidence: 99%

Energy Characterization and Classification of Embedded Operating System Services

Ouni

Belleudy

Senn³

2012

2012 15th Euromicro Conference on Digital System Design

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“…Another simulator‐based approach has been reported in the work of Zhao et al that considered per cycle energy modeling finding instruction's energy cost to track power consumed by function calls and execution paths. However, the authors overlooked effects of cache hit‐rate and miss‐ratio to total energy estimation accuracy.…”

Section: Code Analysis–based Energy Estimation and Modelingmentioning

confidence: 99%

“…Base cost energy of an instruction is estimated either using cycle‐accurate simulators or external measurement setup as presented in Figure 2 . However, accuracy of energy estimation using code analysis–based methodology highly depends on (a) execution path estimation, (b) instructions storage location prediction, (c) circuit state overhead, and (d) loops bound estimation …”

Section: Introductionmentioning

confidence: 99%

A survey on energy estimation and power modeling schemes for smartphone applications

Ahmad

Gani

Hamid

et al. 2016

Int J Communication

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In the last decade, the rising trend in the popularity of smartphones motivated software developers to increase application functionality. However, increasing application functionality demands extra power budget that as a result, decreases smartphone battery lifetime. Optimizing energy critical sections of an application creates an opportunity to increase battery lifetime. Smartphone application energy estimation helps investigate energy consumption behavior of an application at diversified granularity (eg, coarse and fine granular) for optimal battery resource use. This study explores energy estimation and modeling schemes to highlight their advantages and shortcomings. It classifies existing smartphone application energy estimation and modeling schemes into 2 categories, ie, code analysis and mobile components power model-based estimation owing to their architectural designs. Moreover, it further classifies code analysis-based modeling and estimation schemes in simulation-based and profiling-based categories. It compares existing energy estimation and modeling schemes based on a set of parameters common in most literature to highlight the commonalities and differences among reported literature. Existing application energy estimation schemes are low-accurate, resource expensive, or non-scalable, as they consider marginally accurate smart battery's voltage/ current sensors, low-rate power capturing tools, and labor-driven lab-setting environment to propose power models for smartphone application energy estimation. Besides, the energy estimation overhead of the components power model-based estimation schemes is very high as they physically run the application on a smartphone for energy profiling. To optimize smartphone application energy estimation, we have highlighted several research issues to help researchers of this domain to understand the problem clearly. KEYWORDS application energy, energy estimation, energy profiling, profiling overhead 1 | INTRODUCTION Nowadays, with the proliferation of portable devices, the energy-efficient system design has become a must-to-meet requirement for recent resource-constrained smartphone devices. The ever growing smartphone user's demands encourage application developers to enrich the legacy applications that as a result, increase smartphone's computational and communication cost. Among all smartphone-based applications, on-demand video, 1 multi-agent-based distributed games, 2 spatial locality-based social applications, 3 pedestrian tracking, 4,5 and context-based advertisement services 6 are the most energy-hungry services. The inherent features of these services considerably increase the energy demands of processors when executed on smartphones. 7-9 It is estimated that in last 2 decades, processor power budget has increased significantly because of smartphone application's high resource use. To efficiently exploit smartphone's power budget, software optimization minimizes inter-component interaction among hardware modules that as a result, increases battery's lifetime. ...

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“…This work does not consider energy macromodeling and RTOS services. Zhao et al [8] propose a new approach to estimate and optimize the energy consumption of the embedded OS and the applications at a fine-grained level. The work is based on power model and a new estimation model for OS energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Accurate energy characterization of OS services in embedded systems

Ouni

Belleudy

Senn³

2012

J Embedded Systems

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As technology scales for increased circuit density and performance, the management of power consumption in embedded systems is becoming critical. Because the operating system (OS) is a basic component of the embedded system, the reduction and characterization of its energy consumption is a main challenge for the designers. In this work, a flow of low power OS energy characterization is introduced. The variation of the energy and power consumption of the embedded OS services is studied. The remainder of this article details the methods used to determine energy and power overheads of a set of basic services of the embedded OS: scheduling, context switch and inter-process communication. The impacts of hardware and software parameters like processor frequency and scheduling policy on the energy consumption are analyzed. Also, models and laws of the power and energy are extracted. Then, to quantify the low power OS energetic overhead, the obtained models are integrated in the system level design. Our method allows estimating the energy consumption of the low power OS services when running an application on a specific hardware platform.

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Fine-Grained Energy Estimation and Optimization of Embedded Operating Systems

Cited by 10 publications

References 9 publications

Energy Characterization and Classification of Embedded Operating System Services

Energy Characterization and Classification of Embedded Operating System Services

A survey on energy estimation and power modeling schemes for smartphone applications

Accurate energy characterization of OS services in embedded systems

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