Energy Predictive Models of Computing: Theory, Practical Implications and Experimental Analysis on Multicore Processors

Shahid, Arsalan; Fahad, Muhammad; Manumachu, Ravi Reddy; Lastovetsky, Alexey

doi:10.1109/access.2021.3075139

Cited by 10 publications

(10 citation statements)

References 57 publications

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“…They always outperform energy modeling methods based on system utilization due to their fine-grained characteristic. A. Shahid et al pointed out that any nonlinear energy model using only PMC (such as RF and NN models) is inconsistent and inaccurate [ 39 ] and proposed a theoretical framework for computing energy prediction models [ 40 ] because of the current state-of-the-art multicore CPU energy prediction models based on linear regression.The basic practical implications of the theory include selection criteria for model variables, model intercepts, and model coefficients. The model theory follows the physical laws of the conservation of computing energy.…”

Section: Energy Consumption Prediction Frameworkmentioning

confidence: 99%

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Prediction of Overall Energy Consumption of Data Centers in Different Locations

Zhang

Liu

2022

Sensors

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The use of big data leads to higher demands for hyperscale data centers (HDCs) in terms of the scale and quantity required for data storage and processing. Before the construction of an HDC, it is necessary to comprehensively analyze the economic budget according to the energy requirements and potential energy cost. We propose a global energy consumption prediction framework based on the power usage effectiveness (PUE) calculation that considers all heat sources and power consumption. The framework integrates physical models and a statistical framework that combines IT equipment energy consumption and data center energy consuming predictions. Furthermore, the framework provides a method to calculate the carbon emissions and electricity cost of the data center. Using hourly meteorological data as climate parameters, combined with a limited range of energy parameters, the annual PUE values of 60 regions were estimated, and a further analysis of the Carbon Usage Effectiveness (CUE) and electricity costs in China was conducted as an example. Based on experimental validation and an evaluation of real-time data, our framework can predict the overall energy consumption of HDCs effectively, filling a gap in HDC research in the Asia-Pacific region and providing a basis for HDC feasibility analysis.

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Section: Energy Consumption Prediction Frameworkmentioning

confidence: 99%

“…Details and proofs can be found in [ 40 ]. Experiments on two modern Intel multicore servers improved the prediction accuracy of state-of-the-art linear regression models with significant energy saving.…”

Section: Energy Consumption Prediction Frameworkmentioning

confidence: 99%

Prediction of Overall Energy Consumption of Data Centers in Different Locations

Zhang

Liu

2022

Sensors

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“…Dynamic Power Consumption: This occurs by the CMOS shifting to high frequency. Dynamic power consumption P dynamic denotes the power consumption of capacitive-load P cl , the sum of transient power consumption P t and overall power consumption, as given in Equation ( 2) [43][44][45].…”

Section: Static and Dynamic Energy Modelmentioning

confidence: 99%

A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

Jayaprakash¹,

Nagarajan

Prado

et al. 2021

Energies

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Nowadays, many organizations and individual users are employing cloud services extensively due to their efficiency, reliability and low cost. A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact, which is critical in the deployment of leading-edge technologies today such as blockchain and digital finances, IoT, online gaming and video streaming. In this review, various clustering, optimization, and machine learning methods used in cloud resource allocation to increase the energy efficiency and performance are analyzed, compared and classified. Specifically, on the one hand, we discuss how clustering methods and optimization techniques are widely applied in energy management due to their capacity to provide solutions for energy consumption reduction. On the other hand, we study how multi-objective optimization methods focus on reducing energy consumption as well as service level agreement (SLA) violation, and improving quality of services (QoS) simultaneously. Also, we discuss how optimization methods such as the firefly algorithm, whale optimization algorithm (WOA), particle swarm optimization (PSO) and genetic algorithm (GA) provide the highest performance in the field. Moreover, we analyze how machine learning methods such as deep neural network (DNN), random forest, and support vector machine (SVM) are applied to the prediction of energy consumption in the cloud, showing an accurate performance in this prediction. Nevertheless, we study how the existing methods still have limitations of low convergence, trap into local optima and overfitting.

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“…11 . Researchers have focused on new MLI topologies with a lower number of power components to overcome the shortcomings of traditional topologies 12,13,14,15,16,17,18,19 .…”

Section: Introductionmentioning

confidence: 99%

Design of a 21-level multilevel inverter with minimum number of devices count

Hataş

Karakılıç

Almalı

2023

Preprint

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Multilevel inverters (MLIs) have attracted the attention of researchers for their needs in industrial applications, renewable energy systems, and electric vehicles. MLIs require a large number of power electronic components to synthesize higher levels at the output voltage. However, overuse of power electronic devices increases the complexity, losses, and cost of MLIs. In this study, a new MLI has been proposed with a reduced number of power switches. The basic unit of the proposed MLI comprises only three independent DC sources and ten switches (eight unidirectional, and two bidirectional) to produce 21 levels at the output voltage waveform. The nearest level control (NLC) modulation method has been used to produce gate pulses. Furthermore, three extension topologies have been proposed to generate a higher number of levels and the extension parameters have been compared with recently introduced and conventional topologies. The comparative study shows that the proposed MLI topology requires fewer components in terms of power electronics parameters than the others. On the other hand, the presented first extension study can be used for all non-extendable basic units is one of the prominent values of the study. Simulation studies showing modulation methods, switching patterns, and signal outputs were performed with Matlab/Simulink. A prototype of the proposed main module has been realized and tested in the laboratory with an FPGA processing board. Experimental results have been verified with simulation results, and the performance of the proposed topology has been proven.

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Energy Predictive Models of Computing: Theory, Practical Implications and Experimental Analysis on Multicore Processors

Cited by 10 publications

References 57 publications

Prediction of Overall Energy Consumption of Data Centers in Different Locations

Prediction of Overall Energy Consumption of Data Centers in Different Locations

A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

Design of a 21-level multilevel inverter with minimum number of devices count

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