Hybrid Machine Learning/Analytical Models for Performance Prediction

Didona, Diego; Romano, Paolo

doi:10.1145/2668930.2688823

Cited by 8 publications

(8 citation statements)

References 19 publications

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“…Our work starts from the bootstrapped hybrid performance modeling proposed by Didona and Romano in [23], which is a combined AM/ML modeling approach that brings the strengths of AM methods to compensate the weaknesses of ML techniques, and vice versa. On the one hand, hybrid approaches use analytical modeling, which relies on a priori knowledge of the internals of the target system, known as white box approach.…”

Section: Background and Motivationsmentioning

confidence: 99%

A Hybrid Machine Learning Approach for Performance Modeling of Cloud-Based Big Data Applications

Ataie

Evangelinou

Gianniti

et al. 2021

The Computer Journal

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Nowadays, Apache Hadoop and Apache Spark are two of the most prominent distributed solutions for processing big data applications on the market. Since in many cases these frameworks are adopted to support business critical activities, it is often important to predict with fair confidence the execution time of submitted applications, for instance when service-level agreements are established with end-users. In this work, we propose and validate a hybrid approach for the performance prediction of big data applications running on clouds, which exploits both analytical modeling and machine learning (ML) techniques and it is able to achieve a good accuracy without too many time consuming and costly experiments on a real setup. The experimental results show how the proposed approach attains improvement in accuracy, number of experiments to be run on the operational system and cost over applying ML techniques without any support from analytical models. Moreover, we compare our approach with Ernest, an ML-based technique proposed in the literature by the Spark inventors. Experiments show that Ernest can accurately estimate the performance in interpolating scenarios while it fails to predict the performance when configurations with increasing number of cores are considered. Finally, a comparison with a similar hybrid approach proposed in the literature demonstrates how our approach significantly reduce prediction errors especially when few experiments on the real system are performed.

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Section: Background and Motivationsmentioning

confidence: 99%

A Hybrid Machine Learning Approach for Performance Modeling of Cloud-Based Big Data Applications

Ataie

Evangelinou

Gianniti

et al. 2021

The Computer Journal

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“…Effective model training is highly reliant on data treatment and preprocessing options. Data transformation techniques such as Principal Component Analysis (PCA) and simple correlation analysis before model training are great examples of hybrid models researchers often use unknowingly (Didona et al, 2015). These hybrid models help reduce the dimensions of input parameters related to HABs, which could reduce the runtime of HAB model development while maintaining model integrity.…”

Section: Support Vector Regressionmentioning

confidence: 99%

A Review of Machine Learning Models for Harmful Algal Bloom Monitoring in Freshwater Systems

Busari,

Sahoo,

Harmel

et al. 2023

Journal of Natural Resources and Agricultural Ecosystems

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Highlights Machine Learning (ML) models are identified, reviewed, and analyzed for HAB predictions. Data preprocessing is vital for efficient ML model development. ML models for toxin production and monitoring are limited. Abstract. Harmful algal blooms (HABs) are detrimental to livestock, humans, pets, the environment, and the global economy, which calls for a robust approach to their management. While process-based models can inform practitioners about HAB enabling conditions, they have inherent limitations in accurately predicting harmful algal blooms. To address these limitations, Machine Learning (ML) models can potentially leverage large volumes of IoT data to aid in near real-time predictions. ML models have evolved as efficient tools for understanding patterns and relationships between water quality parameters and HAB expansion. This review describes ML models currently used for predicting and forecasting HABs in freshwater ecosystems and presents model structures and their application for predicting algal parameters and related toxins. The review revealed that regression trees, random forest, Artificial Neural Network (ANN), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) are the most frequently used models for HABs monitoring. This review shows ML models' prowess in identifying significant variables influencing algal growth, HAB drivers, and multistep HAB prediction. Hybrid models also improve the prediction of algal-related parameters through improved optimization techniques and variable selection algorithms. While ML models often focus on algal biomass prediction, few studies apply ML models for toxin monitoring and prediction. This limitation can be associated with a lack of high-frequency toxin datasets for model development, and exploring this domain is encouraged. This review serves as a guide for policymakers and researchers to implement ML models for HAB prediction and reveals the potential of ML models for decision support and early prediction for HAB management. Keywords: Cyanobacteria, Freshwater, Harmful algal blooms, Machine learning, Water quality.

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“…Such approaches are able to predict average application execution times with an error of 12% on average. Machine learning and analytical modeling can be combined as discussed in [26], where different hybrid applications, such as transactional auto scaler, IRON model, and chorus, are based, respectively, on divide and conquer, bootstrapping, and ensemble techniques.…”

Section: Related Workmentioning

confidence: 99%

Multi-formalism Models for Performance Engineering

2020

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Nowadays, the necessity to predict the performance of cloud and edge computing-based architectures has become paramount, in order to respond to the pressure of data growth and more aggressive level of service agreements. In this respect, the problem can be analyzed by creating a model of a given system and studying the performance indices values generated by the model’s simulation. This process requires considering a set of paradigms, carefully balancing the benefits and the disadvantages of each one. While queuing networks are particularly suited to modeling cloud and edge computing architectures, particular occurrences—such as autoscaling—require different techniques to be analyzed. This work presents a review of paradigms designed to model specific events in different scenarios, such as timeout with quorum-based join, approximate computing with finite capacity region, MapReduce with class switch, dynamic provisioning in hybrid clouds, and batching of requests in e-Health applications. The case studies are investigated by implementing models based on the above-mentioned paradigms and analyzed with discrete event simulation techniques.

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Hybrid Machine Learning/Analytical Models for Performance Prediction

Cited by 8 publications

References 19 publications

A Hybrid Machine Learning Approach for Performance Modeling of Cloud-Based Big Data Applications

A Hybrid Machine Learning Approach for Performance Modeling of Cloud-Based Big Data Applications

A Review of Machine Learning Models for Harmful Algal Bloom Monitoring in Freshwater Systems

Multi-formalism Models for Performance Engineering

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