An Embedded Software Power Model Based on Algorithm Complexity Using Back-Propagation Neural Networks

Li, Qi; Guo, Bing; Shen, Yan; Wang, Jihe; Wu, Yuansheng; Yun-ben, Liu

doi:10.1109/greencom-cpscom.2010.25

Cited by 10 publications

(9 citation statements)

References 8 publications

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“…We focus on the second type as our goal is to establish real-time power monitoring on cloud servers without any extra metering devices. Li et al [26] built a software/program power consumption model using BPNN. The model takes as input the target program's time complexity, space complexity and data size, and was experimentally proved accurate.…”

Section: Modeling Time Series Of Power With Annmentioning

confidence: 99%

A Power Consumption Model for Cloud Servers Based on Elman Neural Network

Lin

et al. 2021

IEEE Trans. Cloud Comput.

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Section: Modeling Time Series Of Power With Annmentioning

confidence: 99%

A Power Consumption Model for Cloud Servers Based on Elman Neural Network

Lin

et al. 2021

IEEE Trans. Cloud Comput.

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“…It will take more time and computing power to train the model to the convergence stage as the number of parameters to be optimized rises. The server energy consumption was forecasted via Q-learning, B-ANN, MLP and other reinforcement learning techniques (Shen et al, 2013;Li et al, 2010;Islam et al, 2012;Moreno and Xu, 2012;Caglar and Gokhale, 2014;et al, Tesauro et al, 2017). However, before the training effect may truly improve, reinforcement learning necessitates experience accumulation to a significant level.…”

Section: Figurementioning

confidence: 99%

Research on identification of server energy consumption characteristics via dirichlet max-margin factor analysis similarity preservation model

Chen

Liu²,

Shen³

et al. 2023

Front. Energy Res.

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Growing server energy consumption is a significant environmental issue, and mitigating it is a key technological challenge. Application-level energy minimization strategies depend on accurate modeling of energy consumption during an application’s execution. This paper presents a theoretical and experimental study of the dpMMSPFA model in the field of server energy consumption identification. The dpMMSPFA for classification of hidden spaces uses latent variable support vector machines (LVSVM) to learn discriminative subspaces with maximal marginal constraints. The factor analysis (FA) model, the similarity preservation (SP) item, the Dirichlet process mixture (DPM) model, and the maximal marginal classifier are jointly learned beneath a unified Bayesian architecture to advance classification of predictive power. The parameters of the proposed model can be inferred by the simple and efficient Gibbs sampling in terms of the conditional conjugate property. Empirical results on various datasets demonstrate that 1) max-margin joint learning can significantly improve the prediction performance of the model implemented by feature extraction and classification separately and meanwhile retain the generative ability; 2) dpMMSPFA is superior to MMFA when employing SP item and Dirichlet process mixture as prior knowledge; 3) the classification of dpMMSPFA model can often achieve better results on benchmark and measured energy server consumption datasets; 4) and the recognition rate can reach as high as 95.79% at 10 components, far better than other models on measured energy server consumption datasets.

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“…It follows, = ( ) * (8) where G(L) is the equivalent data size mapped from the ATC complexity and L is actual input data size of the application. G(L) takes the following expressions according to the complexity of the application, 1, , , × log , , [24] [25] (e.g., for the voice recognition algorithm, G(L)= 2 ). X is a Gamma distributed random variable [23], and its probability distribution function (PDF) is given by,…”

Section: Optimal Computation Energy In Me Modelmentioning

confidence: 99%

Optimization of Energy Consumption in the Mobile Cloud Systems

S¹,

Wang²,

Zhou³

et al. 2016

KSII TIIS

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We investigate the optimization of energy consumption in Mobile Cloud environment in this paper. In order to optimize the energy consumed by the CPUs in mobile devices, we put forward using the asymptotic time complexity (ATC) method to distinguish the computational complexities of the applications when they are executed in mobile devices. We propose a multi-scale scheme to quantize the channel gain and provide an improved dynamic transmission scheduling algorithm when offloading the applications to the cloud center, which has been proved to be helpful for reducing the mobile devices energy consumption. We give the energy estimation methods in both mobile execution model and cloud execution model. The numerical results suggest that energy consumed by the mobile devices can be remarkably saved with our proposed multi-scale scheme. Moreover, the results can be used as a guideline for the mobile devices to choose whether executing the application locally or offloading it to the cloud center.

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An Embedded Software Power Model Based on Algorithm Complexity Using Back-Propagation Neural Networks

Cited by 10 publications

References 8 publications

A Power Consumption Model for Cloud Servers Based on Elman Neural Network

A Power Consumption Model for Cloud Servers Based on Elman Neural Network

Research on identification of server energy consumption characteristics via dirichlet max-margin factor analysis similarity preservation model

Optimization of Energy Consumption in the Mobile Cloud Systems

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