Autonomic workload performance tuning in large-scale data repositories

Raza, Basit; Sher, Arshad; Afzal, Sana; Malik, Ahmad Kamran; Anjum, Adeel; Kumar, Yogan Jaya; Faheem, Muhammad

doi:10.1007/s10115-018-1272-0

Cited by 9 publications

(5 citation statements)

References 118 publications

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“…Many existing studies have presented different approaches, including different machine learning and deep learning techniques for prediction in general [19], [20] and particularly to diagnose and predict heart disease correctly [6], [7]. Many techniques including support vector machine (SVM), artificial neural network (ANN), fuzzy logic, deep neural networks (DNN), decision trees, and long short-term memory have been applied for identifying heart disease symptoms in patients.…”

Section: Related Workmentioning

confidence: 99%

An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

et al. 2020

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A rapid increase in heart disease has occurred in recent years, which might be the result of unhealthy food, mental stress, genetic issues, and a sedentary lifestyle. There are many advanced automated diagnosis systems for heart disease prediction proposed in recent studies, but most of them focus only on feature preprocessing, some focus on feature selection, and some only on improving the predictive accuracy. In this study, we focus on every aspect that may have an influence on the final performance of the system, i.e., to avoid overfitting and underfitting problems or to solve network configuration issues and optimization problems. We introduce an optimally configured and improved deep belief network named OCI-DBN to solve these problems and improve the performance of the system. We used the Ruzzo-Tompa approach to remove those features that are not contributing enough to improve system performance. To find an optimal network configuration, we proposed a stacked genetic algorithm that stacks two genetic algorithms to give an optimally configured DBN. An analysis of a RBM and DBN trained is performed to give an insight how the system works. Six metrics were used to evaluate the proposed method, including accuracy, sensitivity, specificity, precision, F1 score, and Matthew's correlation coefficient. The experimental results are compared with other state-of-the-art methods, and OCI-DBN shows a better performance. The validation results assure that the proposed method can provide reliable recommendations to heart disease patients by improving the accuracy of heart disease predictions by up to 94.61%.

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Section: Related Workmentioning

confidence: 99%

An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

et al. 2020

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“…Since decades, a large number of researches have been carried out to improve the DBMS and DWH performance to make systems autonomic. The study [20] provides the literature survey for autonomic workload management in all databases dealing with large volume of data such as DBMS and DWH. This study discusses the importance of autonomic systems aspects including classification, adaptation and performance prediction.…”

Section: Database and Dwh Multi-class Prediction And Classificationmentioning

confidence: 99%

A Novel Optimized Case-Based Reasoning Approach With K-Means Clustering and Genetic Algorithm for Predicting Multi-Class Workload Characterization in Autonomic Database and Data Warehouse System

2020

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Data management systems are essential elements for any organization which is dealing with large volume of data now a days. Due to increase in data volume, and its complexities, it has become more challenging job for workload management system to maintain its performance. So, there is a need of such a system that can autonomically deal with such complexities with less or without human involvement. Performance of these systems can be improved by making the systems well-aware about the workload entering into the system. The workload of a prevalent typical database and data warehouse system can be characterized into three types that is Online Transaction Processing (OLTP), Decision Support Systems (DSS) and Mixed type of workload. Currently, autonomic characterization of workload into a binary class such as OLTP and DSS is being carried out as reported in the literature, however, characterizing the workload into three types that refers to a multi-class classification problem is relatively a more challenging task. In this study, we propose a novel optimized Case-based Reasoning (CBR) approach based on clustering for autonomically characterizing the workload into multi-class types before entering into the system. We implement four phases of CBR along with case-base generation and map it to the elements of autonomic MAPE-K model. In Retrieve phase, k-means clustering is used for enhancing retrieval efficiency and workload types predictions are made in Reuse phase. Genetic Algorithm is used in Revise and Adapt phase of CBR. Few autonomic self_* characteristics are incorporated to make it autonomic. We performed various experiments and results show that the proposed model outperforms in prediction as compared to existing approaches. We performed posthoc test for the validation of results in comparison with other machine learning classifiers using the Friedman test that show that the proposed model stands out as the best classifier. INDEX TERMS Workload management system, workload characterization, case-based reasoning, autonomic systems, Genetic Algorithm.

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“…These algorithms arrange data according to an expected workload and contribute to the automated performance tuning of database applications. Most of the existing research outcomes show that performance tuning with the predicted database workloads improves performance during processing time and reduces database administration time [10].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Query Processing on Multi-tiered Persistent Storage

Noon¹,

Getta²,

Xia³

2022

Adv. sci. technol. eng. syst. j.

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The efficient processing of database applications on computing systems with multi-tiered persistent storage devices needs specialized algorithms to create optimal persistent storage management plans. A correct allocation and deallocation of multi-tiered persistent storage may significantly improve the overall performance of data processing. This paper describes the new algorithms that create allocation and deallocation plans for computing systems with multi-tiered persistent storage devices. One of the main contributions of this paper is an extension and application of a notation of Petri nets to describe the data flows in multi-tiered persistent storage. This work assumes a pipelined data processing model and uses a formalism of extended Petri nets to describe the data flows between the tiers of persistent storage. The algorithms presented in the paper perform linearization of the extended Petri nets to generate the optimal persistent storage allocation/deallocation plans. The paper describes the experiments that validate the data allocation/deallocation plans for multitiered persistent storage and shows the improvements in performance compared with the random data allocation/deallocation plans.

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Autonomic workload performance tuning in large-scale data repositories

Cited by 9 publications

References 118 publications

An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

A Novel Optimized Case-Based Reasoning Approach With K-Means Clustering and Genetic Algorithm for Predicting Multi-Class Workload Characterization in Autonomic Database and Data Warehouse System

Optimization of Query Processing on Multi-tiered Persistent Storage

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