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

Abstract

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.

Research on the standard operation of processing process based on the cluster analysis

“…On this basis, they applied genetic algorithm to the performance improvement module to improve the adaptability of the system. The research results show that the model in this study is superior to other existing models in terms of prediction [13]. Sr A et al applied the K-means clustering algorithm to the segmentation of brain tissue images.…”

Evaluation of Teacher Allocation in Regional Compulsory Education Industry Based on K-means clustering and GIS Spatial analysis: A Case Study of teacher-student ratio

“…K-means (k-means clustering algorithm) algorithm is a widely used unsupervised clustering algorithm [18,19], which is simple to implement and has a good and stable clustering effect. To master the allocation characteristics of teacher-student ratio in 30 districts and counties, this paper takes the deviation allocation standard of the teacher-student ratio of the 3 learning stages of each district/county as the deviation degree to construct a 3dimensional feature attribute, and uses K-means algorithm to cluster samples from 30 districts and counties and find the classification characteristics.…”

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

“…AC technologies are good at providing solutions for performance prediction and workload behavior monitoring. A CBR based approach is presented in [17] which predict the change is workload behavior. Various studies presented work on providing predictions related to workload performance such as query arrival times [17] [18].…”

“…A CBR based approach is presented in [17] which predict the change is workload behavior. Various studies presented work on providing predictions related to workload performance such as query arrival times [17] [18]. However, more performance parameters can be used to reflect the workload behavior during execution for better workload management and resource utilization.…”