The Design and Analysis of Thermal-Resilient Hard-Real-Time Systems

Hettiarachchi, Pradeep M.; Fisher, Nathan; Ahmed, Masud; Wang, Le Yi; Wang, Shinan; Shi, Wei

doi:10.1109/rtas.2012.17

Cited by 16 publications

(10 citation statements)

References 23 publications

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“…The feedback thermal controller outputs the miss rate reference, and based on this reference value, the miss rate controller outputs the target frequency of CPU. On the contrary, an algorithm called Temperature Regulated Capacity Bound (TRCB) is proposed in [29], whose main goal is to design thermal-resilient hard real-time systems.…”

Section: Related Workmentioning

confidence: 99%

When thermal control meets sensor noise: analysis of noise-induced temperature error

Kim

Park

Eun

et al. 2015

21st IEEE Real-Time and Embedded Technology and Applications Symposium

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Abstract-Thermal control is critical for real-time systems as overheated processors can result in serious performance degradation or even system breakdown due to hardware throttling. The major challenges in thermal control for real-time systems are (i) the need to enforce both real-time and thermal constraints; (ii) uncertain system dynamics; and (iii) thermal sensor noise. Previous studies have resolved the first two, but the practical issue of sensor noise has not been properly addressed yet. In this paper, we introduce a novel thermal control algorithm that can appropriately handle thermal sensor noise. Our key observation is that even a small zero-mean sensor noise can induce a significant steady-state error between the target and the actual temperature of a processor. This steady-state error is contrary to our intuition that zero-mean sensor noise induces zero-mean fluctuations. We show that an intuitive attempt to resolve this unusual situation is not effective at all. By a rigorous approach, we analyze the underlying mechanism and quantify the noised-induced error in a closed form in terms of noise statistics and system parameters. Based on our analysis, we propose a simple and effective solution for eliminating the error and maintaining the desired processor temperature. Through extensive simulations, we show the advantages of our proposed algorithm, referred to as Thermal Control under Utilization Bound with Virtual Saturation (TCUB-VS).

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Section: Related Workmentioning

confidence: 99%

When thermal control meets sensor noise: analysis of noise-induced temperature error

Kim

Park

Eun

et al. 2015

21st IEEE Real-Time and Embedded Technology and Applications Symposium

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“…In recent years, several power and thermal [4,5,6,7] simulation models have been proposed for DTM techniques at architectural level. Brooks et al [8] exhaustively surveyed and analyzed the power, thermal and reliability modeling problems of the high performance microprocessors.…”

Section: Introductionmentioning

confidence: 99%

Transient power analysis to estimate the thermal time lag of a microprocessor hot spot

Chauhan

Sammakia

Ghose

2015

2015 31st Thermal Measurement, Modeling &Amp; Management Symposium (SEMI-THERM)

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The spatial and temporal non-uniform power of the microprocessor components endangers its functionality and reliability. The rapid change in dynamic power affects electric circuitry, leads to electron migration and high leakage currents; all of which contribute to localized hot spots and high thermal stresses. Though, the dynamic thermal management techniques (DTM) keep the chip temperature within the thermal budget but their efficacy depends on the accurate measurement of the emerging hot spot temperature. The time lag between the peak power input to the microprocessor components and the corresponding hot spot on its surface is therefore a critical parameter for the effective application of DTM techniques. This paper analyses the dynamic power of the microprocessor components to estimate the thermal time lag of the hot spot. The real power estimates of an Intel P4 processor are collected for selected benchmarks, analyzed and then supplied to the numerical model for thermal analysis. In all the benchmarks, the component contributing to hot spot had significantly high heat flux compared to the remaining components of the processor. The power data of the hot component showed periodic peaks of high power which raises the hot spot temperature beyond working limits. The time lapse between peak power input to the time when hot spot temperature becomes 120⁰C, is defined as thermal time lag for our analysis. The knowledge of thermal time lag can be applied by DTM techniques to prevent the power overshoot in high heat flux components. The thermal time lag can also be exploited to boost the clock frequency repeatedly in cyclic fashion to realize high performance over an extended duration. This repeated boosting can be realized by ensuring that the safe temperature limits are not exceeded in each boosting period and with a sufficient cool-down interval between boosts.

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“…In this article, first we address the two fundamental drawbacks by providing a theoretical framework and associate a tractable schedulability analysis for hard-real-time subsystems executing upon a temporally-isolated environment (i.e., in a periodic server) under both resource-and application-level mode changes. In particular, we focus on multimodal systems where the mode change may occur at periodic boundaries; processcontrol systems, such as power-or thermal-aware real-time systems with power-saving levels, satisfy, such an assumption (e.g., [Hettiarachchi et al 2012]). Then, by leveraging the developed schedulability analysis, we address the problem of minimizing a multimode real-time system with respect to resource usages over all modes.…”

Section: Introductionmentioning

confidence: 99%

Tractable schedulability analysis and resource allocation for real-time multimodal systems

Ahmed

Fisher

2014

ACM Trans. Embed. Comput. Syst.

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Real-time multimedia subsystems often require support for switching between different resource and application execution modes. To ensure that timing constraints are not violated during or after a subsystem mode change, real-time schedulability analysis is required. However, existing time-efficient multimode schedulability analysis techniques for application-only mode changes are not appropriate for subsystems that require changes in the resource execution behavior (e.g., processors with dynamic power modes). Furthermore, all existing multimode schedulability analysis that handles both resource and application mode changes is highly exponential and not scalable for subsystems with a moderate or large number of modes. As a result, the notion of resource optimality is still unaddressed for real-time multimodal systems. In this report, we first address the lack of tractable schedulability analysis for such subsystems by proposing a model for characterizing multiple resource and application modes and by deriving a sufficient schedulability test that has pseudo-polynomial time complexity. Finally, we propose an algorithm which leverages this pseudopolynomial schedulability analysis to optimize the resource usages (e.g., to minimize peak-power load) of a multimodal real-time system. Simulation results show that our proposed algorithms for schedulability analysis and resource allocation, when compared with previously-proposed approaches, require significantly less time and are just as precise. ACM Reference Format:Masud Ahmed and Nathan Fisher. 2014. Tractable schedulability analysis and resource allocation for realtime multimodal systems.

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The Design and Analysis of Thermal-Resilient Hard-Real-Time Systems

Cited by 16 publications

References 23 publications

When thermal control meets sensor noise: analysis of noise-induced temperature error

When thermal control meets sensor noise: analysis of noise-induced temperature error

Transient power analysis to estimate the thermal time lag of a microprocessor hot spot

Tractable schedulability analysis and resource allocation for real-time multimodal systems

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