<scp>A2Cloud‐RF</scp>: A random forest based statistical framework to guide resource selection for<scp>high‐performance</scp>scientific computing on the cloud

Samuel, David; Khan, Syeduzzaman; Balos, Cody J.; Abuelhaj, Zachariah; Dutoi, Anthony D.; Kari, Chadi; Mueller, David; Pallipuram, Vivek K.

doi:10.1002/cpe.5942

Cited by 8 publications

(2 citation statements)

References 53 publications

(80 reference statements)

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“…RF performs well on large datasets and reduces the risk of overfitting, which are the most worthy features of this algorithm 49 . The implementation of RF can be seen in References 10 and 50. Table 5 shows RF's hyperparameters using Bayesian optimizer.…”

Section: Methodsmentioning

confidence: 99%

Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities

Arif

Javaid

Aldegheishem

et al. 2021

Concurrency and Computation

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Electricity theft (ET) causes major revenue loss in power utilities. It reduces the quality of supply, raises production cost, causes legal consumers to pay the higher cost, and impacts the economy as a whole. In this article, we use the State Grid Corporation of China (SGCC) dataset, which contains electricity consumption data of 1035 days for two classes: normal and fraudulent. In this work, ET detection model is proposed that consists of four steps: interpolation, data balancing, feature extraction, and classification. First, missing values of the dataset are recovered using the interpolation method. Second, resampling technique is implemented. ET consumers are 9% in the SGCC dataset that make the model inefficient to correctly classify both classes (normal and theft). A hybrid resampling technique is proposed, named synthetic minority oversampling technique with near miss. Third, residual network extracts the latent features from the SGCC dataset. Fourth, three tree based classifiers, such as decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost) are applied to train the encoded feature vectors for classification. Besides, search for good hyperparameters is a challenging task, which is usually done manually and takes a considerable amount of time. To resolve this problem, Bayesian optimizer is used to simplify the tuning process of DT, RF, and AdaBoost. Finally, the results indicate that RF outperforms DT and AdaBoost.

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Section: Methodsmentioning

confidence: 99%

Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities

Arif

Javaid

Aldegheishem

et al. 2021

Concurrency and Computation

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“…A2Cloud‐RF 21 is very similar to A2Cloud‐cc, especially in the first stage, where the application‐instance scores are calculated. The main differences are that it is specifically targeted at HPC applications, and uses random‐forest neural networks in the second stage.…”

Section: Related Workmentioning

confidence: 99%

A lightweight performance proxy for deep‐learning model training on Amazon SageMaker

Keller Tesser,

Marques,

Borin

2024

Concurrency and Computation

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SummaryCloud computing has become popular for training deep‐learning (DL) models, avoiding the costs of acquiring and maintaining on‐premise systems. SageMaker is a cloud service that automates the execution of DL workloads. Its features include automatic hyperparameter optimization and use of spot instances. Nonetheless, it does not assist in selecting the right instance type for a workload. In public clouds, rent price depends on the configuration of the chosen instance type. Advanced and faster instances are typically more expensive, but not always the best choice. To select the optimal instance type, users must compare the workload's relative performance (and hence cost) on several candidates. Building on the execution profiles of multiple DL applications, we model the performance and cost of training DL applications on SageMaker and propose a lightweight technique to estimate these at low temporal and monetary cost. This method is a performance proxy that can be used to replace more expensive performance measurement procedures. So, it could speed up any technique that relies on such measurements. We show how it can help cloud customers seeking suitable instance types to train DL models, and that it can accurately predict the performance of different instance types when training these models on SageMaker.

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Deploying and Configuring Infrastructure

Borin

Napoli

2023

High Performance Computing in Clouds

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A2Cloud‐RF: A random forest based statistical framework to guide resource selection forhigh‐performancescientific computing on the cloud

Cited by 8 publications

References 53 publications

Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities

Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities

A lightweight performance proxy for deep‐learning model training on Amazon SageMaker

Deploying and Configuring Infrastructure

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