TSB: A DVS algorithm with quick response for general purpose operating systems

Seo, Euiseong; Park, Seonyeong; Lee, Joonwon

doi:10.1016/j.sysarc.2007.03.006

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…Based on this study, in [16], the event-driven DVFS scheme was proposed, which distinguishes interactive tasks with other tasks and increases the CPU frequency at the occurrence of an interactive event. In [18], the time slice based (TSB) algorithm was proposed, which adjusts the CPU frequency to meet the performance requirement of executing tasks by using their time slice. These proposed algorithms use the application's characteristics to control the CPU frequency, but do not use any response time information.…”

Section: Related Workmentioning

confidence: 99%

Response Time Constrained CPU Frequency and Priority Control Scheme for Improved Power Efficiency in Smartphones

Kyong

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYDynamic voltage and frequency scaling (DVFS) is an essential mechanism for power saving in smartphones and mobile devices. Central processing unit (CPU) load based DVFS algorithms are widely used due to their simplicity of implementation. However, such algorithms often lead to a poor response time, which is one of the most important factors of user experience, especially for interactive applications. In this paper, the response time is mathematically modeled by considering the CPU frequency and characteristics of the running applications based on the Linux kernel's completely fair scheduler (CFS), and a Response time constrained Frequency & Priority (RFP) control scheme for improved power efficiency of smartphones is proposed. In the RFP algorithm, the CPU frequency and priority of the interactive applications are adaptively adjusted by estimating the response time in real time. The experimental results show that RFP can save energy up to 24.23% compared to the ondemand governor and up to 7.74% compared to HAPPE while satisfying the predefined threshold of the response time in Android-based smartphones.

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Section: Related Workmentioning

confidence: 99%

Response Time Constrained CPU Frequency and Priority Control Scheme for Improved Power Efficiency in Smartphones

Kyong

et al. 2017

IEICE Trans. Inf. & Syst.

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“…DVFS techniques can be classified into interval-based and task-based algorithms [7,14]. Interval-based schemes monitor the CPU load at intervals and respond by changing the CPU frequency and voltage.…”

Section: Related Workmentioning

confidence: 99%

“…Another scheme is LongRun [16] which varies the frequency to suit the measured utilization. These methods are typically easy to implement but can make inaccurate predictions based on the assumption that loads are similar to recent loads [14].…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Programming Solution for Energy-Optimal Video Playback on Mobile Devices

Song

2016

Mobile Information Systems

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Due to the development of mobile technology and wide availability of smartphones, the Internet of Things (IoT) starts to handle high volumes of video data to facilitate multimedia-based services, which requires energy-efficient video playback. In video playback, frames have to be decoded and rendered at high playback rate, increasing the computation cost on the CPU. To save the CPU power, dynamic voltage and frequency scaling (DVFS) dynamically adjusts the operating voltage of the processor along with frequency, in which appropriate selection of frequency on power could achieve a balance between performance and power. We present a decoding model that allows buffering frames to let the CPU run at low frequency and then propose an algorithm that determines the CPU frequency needed to decode each frame in a video, with the aim of minimizing power consumption while meeting buffer size and deadline constraints, using a dynamic programming technique. We finally extend this algorithm to optimize CPU frequencies over a short sequence of frames, producing a practical method of reducing the energy required for video decoding. Experimental results show a system-wide reduction in energy of27%, compared with a processor running at full speed.

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“…A similar algorithm is presented in [12]. TSB [13], a time slice based DVS algorithm, which adjusts the performance level of each task at every context switching. The work in [14] presents process oriented power management.…”

Section: Task-based Dvs Algorithmsmentioning

confidence: 99%

A self-adaptive DVS scheduling algorithm for multi-core systems

Liu

et al. 2010

2010 International Conference on Computer Design and Applications

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Dynamic Voltage Scaling (DVS) is widely supported in modern general purpose systems, like desktops. Interval based DVS algorithms have been used in some operating systems (OSs), like Linux. But their load prediction models can't fit various needs of users so well, since most of them employ one prediction method only. In this paper, a self adaptive algorithm called SADVS is presented. It selects the best algorithm according to the prediction accuracy of historical records, and the selector will be used to adjust the CPU speed in the following interval. SADVS behaves well in both simulations and actual implementation. For actual implementation, we find that SADVS reduces energy consumption 0.02% .... .. . 4% without time extension compared to results of ondemand policy in Linux kernel.

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TSB: A DVS algorithm with quick response for general purpose operating systems

Cited by 16 publications

References 8 publications

Response Time Constrained CPU Frequency and Priority Control Scheme for Improved Power Efficiency in Smartphones

Response Time Constrained CPU Frequency and Priority Control Scheme for Improved Power Efficiency in Smartphones

A Dynamic Programming Solution for Energy-Optimal Video Playback on Mobile Devices

A self-adaptive DVS scheduling algorithm for multi-core systems

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