A Linux-governor based Dynamic Reliability Manager for android mobile devices

Mercati, Pietro; Bartolini, Andrea; Paterna, Francesco; Rosing, Tajana; Benini, Luca

doi:10.7873/date2014.117

Cited by 12 publications

(19 citation statements)

References 7 publications

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“…Task scheduling decisions are controlled at longer intervals and the voltage/frequency scaling is performed at a shorter interval. This approach is extended in [Mercati et al 2014] as a governor for the Android Operating System. Another control approach is proposed in [Sironi et al 2013] to manage the temperature of applications running on multiprocessor systems.…”

Section: Related Workmentioning

confidence: 99%

Adaptive and Hierarchical Runtime Manager for Energy-Aware Thermal Management of Embedded Systems

Das

Al-Hashimi

Merrett

2016

ACM Trans. Embed. Comput. Syst.

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Modern embedded systems execute applications, which interacts with the operating system and hardware differently depending on type of workload. These cross-layer interactions result in wide variations of chipwide thermal profile. In this paper, a reinforcement learning-based run-time manager is proposed that guarantees application-specific performance requirements and controls the POSIX thread allocation and voltage/frequency scaling for energy-efficient thermal management. This controls three thermal aspectspeak temperature, average temperature and thermal cycling. Contrary to existing learning-based run-time approaches that optimize energy and temperature individually, the proposed run-time manager is the first approach to combine the two objectives, simultaneously addressing all three thermal aspects. However, determining thread allocation and core frequencies to optimize energy and temperature is an NP-hard problem. This leads to an exponential growth in the learning table (significant memory overhead) and a corresponding increase in the exploration time to learn the most appropriate thread allocation and core frequency for a particular application workload. To confine the learning space and to minimize the learning cost, the proposed run-time manager is implemented in a two-stage hierarchy: a heuristic-based thread allocation at a longer time interval to improve thermal cycling, followed by a learning-based hardware frequency selection at a much finer interval to improve average temperature, peak temperature and energy consumption. This enables finer control on temperature in an energy-efficient manner, while simultaneously addressing scalability, which is a crucial aspect for multi-/many-core embedded systems. The proposed hierarchical run-time manager is implemented for Linux running on nVidia's Tegra SoC, featuring four ARM Cortex-A15 cores. Experiments conducted with a range of embedded and cpu intensive applications demonstrate that the proposed run-time manager not only reduces energy consumption by an average 15% with respect to Linux, but also improves all the thermal aspects -average temperature by 14 • C, peak temperature by 16 • C and thermal cycling by 54%.

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Section: Related Workmentioning

confidence: 99%

Adaptive and Hierarchical Runtime Manager for Energy-Aware Thermal Management of Embedded Systems

Das

Al-Hashimi

Merrett

2016

ACM Trans. Embed. Comput. Syst.

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“…Here accuracy describes the systematic bias of the data values compared to the real values of the measured quantity 3 , and precision denotes the random errors in repeated measurements under the same conditions 4 . Although the concept of measurement device precision and propagation of inaccuracies into the system through calculations is well established, more attention by CPS engineers is due in system design 5 .…”

Section: ) Potential Solutionsmentioning

confidence: 99%

“…Self-awareness has been applied to both hardware [1] and software [2]. Some of the applications which have been explored for the implementation of self-aware concepts (under this term or other terms such as adaptivity, autonomy, goal-oriented and so on), are mobile applications [3], cloud computing [4], networks [5], operating systems [6], web [7], multi-core resource managers [8] and adaptive and dynamic compilation environment [9], (cyberphysical) system-on-chip [1], and health monitoring [10]. 1 www.gartner.com/newsroom/id/2636073…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Observation and its Role in Self-Awareness; An Emotion Recognition System Example

TaheriNejad

Jantsch

Pollreisz

2016

Annals of Computer Science and Information Systems

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Abstract-Observation plays a crucial role in self-awareness. In many scenarios, such as the Observe-Decide-Act (ODA) loops, self-awareness is founded upon observations of the system. In other words, observation generates the understanding of the system from the status and behavior of its self and its environment. Although recently more focus has been put on comprehensive and competent observations, we believe that further attention and work is due, especially in the field of cyberphysical systems. Hence, in this paper, we discuss our position on various aspects of observation methods. In a short list, the major aspects are Abstraction, Disambiguation, Desirability, Relevance, Data Reliability, Confidence, Attention, and History. We elaborate and anticipate the potential of these factors in improving the quality of the observation of the system, decreasing the processing load of higher layers, increasing the reliability of decisions, and consequently the overall performance of the system. To put these aspects into perspective, we elaborate them in the context of their potentials in our emotion recognition system under development.

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“…However, HotSpot has a known limitation on accuracy and simulation time [6], making this approach difficult to use for real-time applications. A control-theoretic approach is proposed in [24] to optimize the lifetime reliability of a multiprocessor system. Task scheduling decisions are controlled at longer intervals and the voltage/frequency scaling is performed at a shorter interval.…”

Section: Related Workmentioning

confidence: 99%

Workload Change Point Detection for Runtime Thermal Management of Embedded Systems

Das

Merrett

Tribastone

et al. 2016

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-Applications executed on multicore embedded systems interact with system software (such as the OS) and hardware, leading to widely varying thermal profiles which accelerate some aging mechanisms, reducing the lifetime reliability. Effectively managing the temperature therefore requires (1) autonomous detection of changes in application workload and (2) appropriate selection of control levers to manage thermal profiles of these workloads. In this paper we propose a technique for workload change detection using density ratio-based statistical divergence between overlapping sliding windows of CPU performance statistics. This is integrated in a run-time approach for thermal management, which uses reinforcement learning to select workload-specific thermal control levers by sampling on-board thermal sensors. Identified control levers override the OS's native thread allocation decision and scale hardware voltage-frequency to improve average temperature, peak temperature and thermal cycling. The proposed approach is validated through its implementation as a hierarchical run-time manager for Linux, with heuristic-based thread affinity selected from the upper hierarchy to reduce thermal cycling and learningbased voltage-frequency selected from the lower hierarchy to reduce average and peak temperatures. Experiments conducted with mobile, embedded and high performance applications on ARM-based embedded systems demonstrate that the proposed approach increases workload change detection accuracy by an average 3.4x, reducing the average temperature by 4-25• C, peak temperature by 6-24• C and thermal cycling by 7-35% over stateof-the-art approaches.

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A Linux-governor based Dynamic Reliability Manager for android mobile devices

Cited by 12 publications

References 7 publications

Adaptive and Hierarchical Runtime Manager for Energy-Aware Thermal Management of Embedded Systems

Adaptive and Hierarchical Runtime Manager for Energy-Aware Thermal Management of Embedded Systems

Comprehensive Observation and its Role in Self-Awareness; An Emotion Recognition System Example

Workload Change Point Detection for Runtime Thermal Management of Embedded Systems

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