Parametric Design and Performance Analysis of a Decoupled Service-Oriented Prediction Framework Based on Embedded Numerical Software

Kousiouris, George; Menychtas, Andreas; Kyriazis, Dimosthenis; Konstanteli, Kleopatra; Gogouvitis, Spyridon V.; Katsaros, Georgios; Varvarigou, Theodora

doi:10.1109/tsc.2012.21

Cited by 15 publications

(22 citation statements)

References 26 publications

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“…The model parameters (types and number of neurons, training functions etc.) were optimized based on a Genetic Algorithm, following the defined methodology in (Kousiouris et al, 2012), adapted in the given dataset. A number of training functions were investigated from the 56 PANAGOULIA et al…”

Section: Setmentioning

confidence: 99%

“…The inputs selection for each stage (2 nd to 4 th ) is based on a set of crucial statistical indices which is able to exploit nonlinear input variables and through an appropriate process can also optimize the parameters of the same network. The approach is compared against two alternatives, an auto-regressive model with only linear characteristics but very fast creation time, and a GA-based optimized ANN architecture (Kousiouris et al, 2012), investigating a number of different parameters and tradeoffs in network design (training functions, different types of neuron activation functions, size and number of layers etc. ), but depending only on the previous values of the forecasted metric, in order to showcase the trade-off of not selecting multiple hydrological criteria but dealing only with the data as a time series.…”

Section: Introductionmentioning

confidence: 99%

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A multi-stage methodology for selecting input variables in ANN forecasting of river flows

2017

Global NEST Journal

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The scientific community has recognized the necessity for more efficiently selected inputs in artificial neural network models (ANNs) in river flows and has worked on this despite some shortcomings. Moreover, there is none or limited inclusion of ANN inputs coupled with atmospheric circulation under various patterns arising from the need of data downscaling for climate change predictions in hydrology domain. This paper presents the results of a novel multi-stage methodology for selecting input variables used in artificial neural network (ANN) models for river flow forecasting. The proposed methodology makes use of data correlations together with a set of crucial statistical indices for optimizing model performance, both in terms of ANN structure (e.g. neurons, momentum rate, learning rate, activation functions, etc), but also in terms of inputs selection. The latter include various previous time steps of daily areal precipitation and temperature data coupled with atmospheric circulation in the form of circulation patterns, observed river flow data and time expressed via functions of sine and cosine. Additionally, the non-linear behavior between river flow and the respective inputs is investigated by the ANN configuration itself and not only by correlation indices (or other equivalent contingency tools). The proposed methodology revealed the river flow of past four days, the precipitation of past three days and the seasonality as robust input variables. However, the temperature of three past days should be considered as an alternative against the seasonality. The produced models forecasting ability was validated by comparing its one-step ahead flow prediction ability to two other approaches (an auto regressive model and a genetic algorithm (GA)-optimized single input ANN).

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Section: Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi-stage methodology for selecting input variables in ANN forecasting of river flows

2017

Global NEST Journal

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“…This aspect is noticeable at the higher CC layers as changes in users' requests [17] and at the lower CC layers as variable resource utilization [18], [19]. To summarize, cloud usage patterns are heterogeneous due to the varying user needs.…”

Section: A Classic Resource Management Conceptsmentioning

confidence: 99%

“…One of the emerging objectives is the minimization of the environmental impact, driven by the policies of governments and companies, the technical challenges in reliable provisioning of high cloud BrokErinG [27], [28], [29], [30], [ [38], [39], [40], [16], [14], [41], [42], [43] [42] [44] workflow schEdulinG [45], [46], [47], [48], [15], [49], [50] [15] [47], [51], [48], [15] [37] [21] [52], [53] dynaMic caPaciTy PlanninG [54], [18] [17], [18] [55] [19] sErvEr farM load BalancinG [56] [55] [57] [58], [59] [ 56] power, and the cost of electricity for largescale IT systems. Environmental objectives include the minimization of electrical energy use, the peak power draw of the systems, cooling costs, and CO 2 emissions.…”

Section: Objectivesmentioning

confidence: 99%

A Survey of Evolutionary Computation for Resource Management of Processing in Cloud Computing [Review Article]

Guzek

Bouvry

Talbi

2015

IEEE Comput. Intell. Mag.

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C loud computing is significantly reshaping the computing industry. Individuals and small organizations can benefit from using state-of-the-art services and infrastructure, while large companies are attracted by the flexibility and the speed with which they can obtain the services. Service providers compete to offer the most attractive conditions at the lowest prices. However, the environmental impact and legal aspects of cloud solutions pose additional challenges. Indeed, the new cloud-related techniques for resource virtualization and sharing and the corresponding service level agreements call for new optimization models and solutions. It is important for computational intelligence researchers to understand the novelties introduced by cloud computing. The current survey highlights and classifies key research questions, the current state of the art, and open problems.

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“…Goals can be hard (must be satisfied) and soft (should be satisfied). To satisfy the request, each layer enables the data access according to one or more modes, each of them having different impacts in terms of performance, data quality, energy, and security [8]. For instance, compressed data involve more computation than uncompressed one, but it reduces the time for transmission.…”

Section: The Cross-layer Optimization Frameworkmentioning

confidence: 99%

Data Movement in the Internet of Things Domain

D'Andria

Field

Kopaneli

et al. 2015

Service Oriented and Cloud Computing

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Parametric Design and Performance Analysis of a Decoupled Service-Oriented Prediction Framework Based on Embedded Numerical Software

Cited by 15 publications

References 26 publications

A multi-stage methodology for selecting input variables in ANN forecasting of river flows

A multi-stage methodology for selecting input variables in ANN forecasting of river flows

A Survey of Evolutionary Computation for Resource Management of Processing in Cloud Computing [Review Article]

Data Movement in the Internet of Things Domain

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