Evolutionary Neural Network Based Energy Consumption Forecast for Cloud Computing

Foo, Yong Wee; Goh, Cindy; Lim, Hong Chee; Zhan, Zhi-Hui; Li, Yun

doi:10.1109/icccri.2015.17

Cited by 20 publications

(11 citation statements)

References 27 publications

(22 reference statements)

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“…Apart from the fields mentioned above, EC-NNs are used in other fields as well, where they can be applied to forecast energy consumption of cloud computing [15], reconstruct the topology of gene regulatory network [16], design custom-made fractal antennas [33], optimize large scale problem [20] as well as employed in Spatio-Temporal system identification [32], and an online modeling algorithm [25].…”

Section: Othersmentioning

confidence: 99%

When Neural Network Computation Meets Evolutionary Computation: A Survey

Chen

Zhan

Shi

et al. 2016

Advances in Neural Networks – ISNN 2016

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Abstract. Neural network (NN) and evolutionary computation (EC) are two of the most popular and important techniques in computational intelligence, which can be combined together to solve the complex real world problems. This paper represents a review of the researches that combined NN and EC. There are 3 main research focuses as follows. In the first research focus, EC algorithms have been widely used to optimize the structure and parameter of the NN, including weight, structure, learning rates, and others. In the second research focus, lots literatures have witnessed that EC based NNs are widespread in the applications such as classification, automatic control, prediction, and many other fields. These two kinds of researches into combining NN and EC are mainly focuses on using EC algorithms to optimize NN, to enhance the NN performance and the NN application ability. Our survey finds that particle swarm optimization is the most popular EC algorithm that the researchers choice to optimize NN in recent year, while genetic algorithm and differential evolution are also widely used. In the third research focus, there are also researches adopted NN as a tool to enhance the performance of EC algorithms. Although the literatures in this focus are not as many as the above two focuses, the existing results show that NN has great potential in enhancing EC algorithms. The survey shows that when NN and EC meet, combining them would result in an effective way to deal with the real world application. This interesting research topic has become more and more significant in the field of computer science and still has much room for development.

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Section: Othersmentioning

confidence: 99%

When Neural Network Computation Meets Evolutionary Computation: A Survey

Chen

Zhan

Shi

et al. 2016

Advances in Neural Networks – ISNN 2016

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“…The data collected from the Hadoop cluster are used to train and calibrate the NN models. The details of the testbed setup and evolutionary NN training is described in our earlier work in [7][8].…”

Section: A Experiments Setup and Data Collectionmentioning

confidence: 99%

Machine Learning with Sensitivity Analysis to Determine Key Factors Contributing to Energy Consumption in Cloud Data Centers

Foo

Goh

2016

2016 International Conference on Cloud Computing Research and Innovations (ICCCRI)

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Abstract-Machine learning (ML) approach to modeling and predicting real-world dynamic system behaviours has received widespread research interest. While ML capability in approximating any nonlinear or complex system is promising, it is often a black-box approach, which lacks the physical meanings of the actual system structure and its parameters, as well as their impacts on the system. This paper establishes a model to provide explanation on how system parameters affect its output(s), as such knowledge would lead to potential useful, interesting and novel information. The paper builds on our previous work in ML, and also combines an evolutionary artificial neural networks with sensitivity analysis to extract and validate key factors affecting the cloud data center energy performance. This provides an opportunity for software analysts to design and develop energyaware applications and for Hadoop administrator to optimize the Hadoop infrastructure by having Big Data partitioned in bigger chunks and shortening the time to complete MapReduce jobs.

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“…El constante incremento del consumo de energía eléctrica (CEE) [1][2], estrechamente ligado al desarrollo socioeconómico [3][4], se ha convertido en uno de los temas que más ocupa la atención de políticos y científicos, tanto para determinar políticas de energía [3][4][5] como para considerar la disminución de costos en todos los puntos de su proceso de producción (generación, distribución y consumo, con énfasis en este último) [6][7] y preservar el medio ambiente [8][9]. La marcada tendencia a la desregulación del mercado de la energía eléctrica [10][11], los cambios climáticos [12][13], la expansión del uso de las energías renovables [14] y la escasez de combustibles fósiles hacen más complejo el escenario; por lo tanto, contar con pronósticos de CEE que den cuenta acabadamente de esta complejidad se vuelve un desafío.…”

Section: Introductionunclassified

Revisión sistemática de literatura sobre modelos de pronósticos de consumo de energía eléctrica

Mazzeo

Sepúlveda-Cano

Montoya

et al. 2019

rev.ing.univ.Medellin

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El creciente consumo de energía eléctrica, los cambios climáticos y el desarrollo de nuevas tecnologías exigen mejoras para la gestión eficiente de la energía. El adecuado pronóstico del consumo de energía es relevante para el desarrollo sostenible de cualquier país. En este artículo se propone una revisión sistemática de literatura seleccionada a partir de cadenas de búsqueda formada por las palabras forecasting, energyy consumptionaplicadas en las bases de datos científicas. Se comparan principalmente los modelos/técnicas utilizadas, las variables consideradas y las métricas de error usadas con el fin de obtener conocimiento de cada una de las propuestas, relevar sus características y así poder evidenciar el vacío en la literatura que podría determinar la semilla para un nuevo trabajo de investigación. Como conclusiones se observan el uso continuo de redes neuronales artificiales para el pronóstico de consumo, la importancia determinar las variables de entrada y la medición del error para evaluar la precisión de los modelos. Finalmente, como nueva línea de investigación se propone desarrollar un modelo para el pronóstico de corto plazo de CEE para un país latinoamericano en vías de desarrollo, a partir de la comparación y evaluación de diferentes técnicas/modelos, variables y herramientas ya existentes.

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Evolutionary Neural Network Based Energy Consumption Forecast for Cloud Computing

Cited by 20 publications

References 27 publications

When Neural Network Computation Meets Evolutionary Computation: A Survey

When Neural Network Computation Meets Evolutionary Computation: A Survey

Machine Learning with Sensitivity Analysis to Determine Key Factors Contributing to Energy Consumption in Cloud Data Centers

Revisión sistemática de literatura sobre modelos de pronósticos de consumo de energía eléctrica

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