A CBR Model for Workload Characterization in Autonomic Database Management System

Shaheen, Nusrat; Raza, Basit; Malik, Ahmad Kamran

doi:10.1109/icet.2018.8603615

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…This framework predicts performance on binary classification of workload but doesn't discuss the Mixed type workload. An autonomic performance prediction framework in DWH is presented in [16] using traditional CBR approach and for characterization considered only binary workload types. A CBR based workload characterization model is presented in [17], which considered the classification as binary classification and characterized the workload into OLTP and DSS.…”

Section: Database and Dwh Multi-class Prediction And Classificationmentioning

confidence: 99%

“…Studies are also incorporating the clustering techniques for large scale data repositories for better handling the large volume of data. The study [16] presented clustering in their proposed performance prediction framework in DWH. For optimizing the solutions of searching problems, implementation of evolutionary algorithms can help in a better way.…”

Section: Database and Dwh Multi-class Prediction And Classificationmentioning

confidence: 99%

“…Machine learning Lazy learning approaches are being used in building of autonomic data management systems either by characterizing the workload or by predicting the performance of the system [15] [16] [17] [18]. Evolutionary algorithms are playing important part in optimizing the searching problems.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Database and Dwh Multi-class Prediction And Classificationmentioning

confidence: 99%

Section: Database and Dwh Multi-class Prediction And Classificationmentioning

confidence: 99%

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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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An approach to design and develop generic integrated architecture for autonomic software system

Dehraj

Sharma

2020

Int J Syst Assur Eng Manag

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Autonomic Workload Performance Modeling for Large-Scale Databases and Data Warehouses Through Deep Belief Network With Data Augmentation Using Conditional Generative Adversarial Networks

2021

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Databases and warehouses are experiencing workload of different types such as Decision Support System (DSS), Online Transaction Processing (OLTP) and Mixed workloads. Handling variety of workload in autonomic systems is a critical task. After self-configuring the workload, the next challenge is workload performance tuning that motives towards self-predictive systems. Existing studies provide performance modeling solutions on small-scale data repositories of Database Management System (DBMS) and Data Warehouse (DWH) using either classical eager or lazy learning approaches. However, in realworld problems, we normally have to deal with large-scale data repositories. Therefore, there is a need to develop performance models that provide data augmentation to solve large-scale data repositories that are not publicly available. In this study, deep learning approaches have been investigated for performance tuning of large-scale data repositories. We propose a performance prediction model called Optimized GANbased Deep Learning (OGDL) model. For data augmentation, Conditional Generative Adversarial Networks (CGAN) is applied. For autonomic perspective, we incorporated MAPE-K model to manage the workload autonomically. Different deep learning models are applied, and it was observed that Deep Belief Network (DBN) performed better as compared to other deep learning models such as Deep Neural Network (DNN). We performed a number of experiments and from results it is observed that deep learning models performed the best in comparison with classical machine learning and lazy learning and a 6 − 8% increase in accuracy is recorded in our experiments using DBN. The proposed OGDL model performed the best in workload performance predictions in an optimized way.

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A CBR Model for Workload Characterization in Autonomic Database Management System

Cited by 3 publications

References 19 publications

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

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

An approach to design and develop generic integrated architecture for autonomic software system

Autonomic Workload Performance Modeling for Large-Scale Databases and Data Warehouses Through Deep Belief Network With Data Augmentation Using Conditional Generative Adversarial Networks

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