“The goal is to have databases in the Cloud run autonomously.  The Cloud should be about scale, elasticity, statelessness and ease of operation and interoperability.  Cloud infrastructures are about moving processes into microservices and the agile deployment of business services.  Deep Learning has the potential to give databases innovative and powerful level autonomy in a multitenant environment, allowing DBAs the freedom to offer expertise in system architecture and design…”.

Introduction

This article details initial research performed using deep learning algorithms to detect anomalies in Oracle performance.  It does not serve as a “deep” dive into deep learning and machine learning algorithms.  Currently, there are many really good resources available from experts on the subject matter and I strongly recommend those who are interested in learning more about these topics to check out the list of references at the end of this article.  Mathematical terminology is used throughout this article (it’s almost impossible to avoid), but I attempted to keep the descriptions brief, as it’s best that people interested in these topics seek to out the rich resources available online to get a better breadth of information on individual subjects.

 

In this final article on Oracle performance tuning and machine learning, I will discuss the application of deep learning models in predicting performance and detecting anomalies in Oracle.  Deep Learning is a branch of Machine Learning method that uses intensive Artificial Intelligence (AI) techniques with data to learn iteratively; while deploying optimization and minimization functions. Applications for these techniques include natural language processing, image recognition, self-driving cars, anomaly and fraud detection.  With the number of applications for deep learning models growing substantially in the last few years, it was only a matter of time that it would find its way into relational databases.   Relational databases have sort of become the workhorses of the IT industry and still generate massive amounts of revenue.  Many data-driven applications still use some type of relational database; even with the growth of Hadoop and NoSQL databases.  It’s been a business goal of Oracle Corporation, one of the largest relational database software companies in the world, to create database services that are easier manage, secure and operate.

As I mentioned in my previous article, Oracle Enterprise Edition has a workload data repository that it already uses to produce great analysis for performance and workload.  Microsoft SQL-Server also has a warehouse that can store performance data, but I’ve decided to devote my research into Oracle.

For this analysis, the focus was specifically on the Oracle Program Global Area (PGA).

Oracle Program Global Area

 

The Program Global Area (PGA) is a private memory in the database that contains information for server processes.  Each user session gets a private memory region within the PGA.  Oracle will read and write information to the PGA based on requests from server processes.  The PGA performance metrics accessed for this article are based on Oracle Automatic Shared Memory Management (ASMM).

As a DBA, when troubleshooting PGA performance, I typically look at the PGA advisor, which are a series of modules that collects monitoring and performance data from PGA.  It recommends how large the PGA should be in order to fulfill process requests for private memory and is based on the Cache Hit Percentage value.

 

Methodology

 

The database was staged in a Microsoft Azure virtual machine processing large scale data from a data generator.  Other data was compiled from public portals such as EAI (Energy Administration Institute) and PJM Interconnection, an eastern regional transmission organization.

Tools used to perform the analysis include SAS Enterprise Miner, Azure Machine Learning studio and the SciKit Learn with TensorFlow machine learning libraries.  I’ve focused my research on a few popular techniques for which I continuously do research.  These include

• Recurrent Neural Networks
• Autoencoders
• K-Nearest Neighbors
• Naїve Bayes
• Principal Component Analysis
• Decision Trees
• Support Vector Machines
• Convolutional Neural Network
• Random Forest

For this research into databases, I focused primarily on SVM, PCA and CNN. The first step was to look at the variable worth (the variables that had the greatest weight on the model) for data points per sample.

 

 

 

The analysis of Oracle Performance data on Process Memory within dedicated process memory in Oracle in the program global area of the database.

Once the data was collected, cleaned, imputed and partitioned, Azure ML studio was used to build two types of classifiers for anomaly detection.

 

Support Vector Machine (SVM):  Implements a binary classifier where the training data consists of examples of only one class (normal data).  The model attempts to separate the collection of training data from the origin using maximum margin.

 

Principal Component Analysis (PCA): Create subspace spanned by orthonormal eigenvectors associated with the top eigenvalues of the data covariance matrix for approximation of classifiers.

 

For prediction, I compared Artificial Neural Networks and Regression models.  For Deep Learning, I researched the use of CNN specifically for anomaly detection.

 

Deep Learning and Oracle Database Performance Tuning

My article Using Machine Learning and Data Science for Performance Tuning in Oracle  discusses the use of Oracle’s automated workflow repository, a data warehouse which stores snapshots of views for SQL, O/S and system state and active session history among many other areas of system performance.  Standard data science methods require having a strong understanding of business processes through qualitative and quantitative methods, cleaning data to find outliers and missing values, and applying data partitioning strategies to get better data validation and scoring of models.  As a final step, a review of the results would be required to determine its hypothetical testing accuracy.

 

Deep Learning has changed these methodologies a bit by applying artificial intelligence into building models.  These models learn from iteratively training as data moves from hidden layers with activation functions from input to output.  The hidden layers in this article are convolutional and are specific to spatial approximations such as convolution, pooling and fully connected layers (FCL).  This has opened many opportunities to automate a lot of the steps typically used in typical data science models.  If there is data generated which would require interpretation by a human operator, this can now be interpreted using deep neural networks at much higher rates that can possibly be done by a human operator.

 

Deep Learning is a subset of Machine Learning which is loosely based on how neurons learn in in the brain.  Neural networks have been around for decades but have just recently gained popularity in the information technology for its ability to identify and classify images.  Image data has exploded with the increase in social media platforms, digital images and image data storage.  Imaging data, along with text data how a multitude of applications in the real world, so there is no shortage of work being done in this area.  The latest popularity of neural networks can be attributed to Alexnet, a deep neural network that on the ImageNet classification challenge for achieving low error rates on the ImageNet dataset.

 

With anomaly detection, the idea is to train a deep learning models to detect anomalies without overfitting data.  As the model iterates through the layers of a deep neural network, cost functions help to determine how close it is classifying real-world data.  The model should have no prior knowledge of the processes and should be iteratively trained in the data for the cost functions from input arrays and activation functions of other previous layers [7].

 

Anomaly detection is the process of detecting outliers in the data streams such as financial transactions and network traffic. It can also be applied to deviations in system performance for the purpose of this article.

 

Predictive Analysis versus Anomaly Detection

Using predictive analytics to model targets through supervised learning techniques is most useful in planning for capacity and performing aggregated analysis of resource consumption and database performance.  For the model, we analyzed regression and neural network models to determine how well each one scored based on inputs from PGA metrics.

Predictive analysis requires cleansing of data, supervised and non-supervised classification, imputation and variable worth selection to create model. Most applications can be scored best with linear or logistic regression.  In the analysis on PGA performance, I found a logistic regression model scored better than an artificial neural network for predictive ability.

 

 

In my previous article, I mentioned the role that machine learning and data science can play in Oracle performance data.

1. Capacity Planning and IT Asset Planning.
2. Performance Management
3. Business Process Analysis

The fourth application for data science and machine learning in Oracle is anomaly detection.  Which specifically means applying artificial intelligence to the training of algorithms mostly used in image recognition and language processing and credit fraud detection.  It’s also a possibly less efficient way of detecting performance problems in Oracle performance.  To attempt to obtain accuracy in the algorithm presents a risk itself, since such models could result in overfitting and high dimensionality that you want to avoid in deep neural networks.  Getting accuracy that is comparable to what I human operator can do, works better because basically you don’t want the process to overthink things.  The result of an overfitting model is a lot of false positives.  You want the most accurate signs of an anomaly, not a model that is oversensitive.  Deep Learning techniques also perform intense resource consumption to generate output in a neural network.  Most business scale applications require GPUs to build them efficiently.

Convolutional Neural Networks

 

Convolutional Neural Networks (CNN) are designed for high dimensional data such as images and signals.  It’s used for computer vision as well as network intrusion detection and anomaly detection.  Oracle performance data is designed as normal text (ASCII) data and contains many different ranges of metrics like seconds versus bytes of memory. Using a mathematical normalization formula, text data can be converted in vector arrays that can be mapped, pooled and compressed.  Convolutional Neural Networks are good distinguishing features in an image matrix.  Computationally, it is efficient to represent images as multi-dimensional arrays.

 

The first step is to normalize the PGA data, which contains multiple scales and features.  Below is a sample of the data.

 

Normalizing the data can be done with the following formula[8]:

 

The second step is to convert this data into image format.  This would require building a dimensional array of all the features.  Filtering the array can be done by removing small variances and nonlinear features to generate an overall neutral vector.  The goal is to normalize and create a multidimensional array of the data.

 

CNN is often used to identify the NMIST data, which is a set of handwritten numbers.  It contains 60,000 training images and 10,000 testing images.  Researchers have used CNN to get an error rate on the NMIST data of less than 1%.

 

Convolution Neural Networks have five basic components, input layer, convolution layer, pooling layer, fully connected layer and output layer.  Below is a visual of how CNN works to recognize an image of a bird versus and image of a cat.

 

The activation function uses a popular rectified linear unit ReLU, which is typical used for CNN.  Popular activation functions include logistic sigmoid and hyperbolic tangents.  ReLU is defined as a linear y=x for positive values and linear y=0 for negative values.  It’s great as an activation function for CNN, due to it’s simplicity and because it helps the time it takes to iterate in the neural network.

 

 

 

Comparing Support Vector Machines (SVM) and Principal Component Analysis (PCA)

Support Vector Machines or SVM are good for finding large margin classifications and identifying vectors of data that are related.  The nice thing about SVM is that it has features to deal with outliers built into it. Support Vector Machines is a feature-rich supervised machine learning technique used for classification of observations by their coordinates.  I compared the SVM with principal component analysis (PCA) to approximate.  PCA creates subspaces spanned by orthonormal eigenvectors associated with the top eigenvalues of the data covariance matrix.  PCA based methods help to remove redundancy and reduce dimensionality that is persistent in performance data.   Once data was split into training and testing, we used SVM and PCA to optimize multiple dimensions in the data.

 

 

Evaluation of Machine Learning Models for Oracle Workloads

For this test, we compared neural networking regression models and ANN.  Deep Learning of patterns concerned with anomalies within a database require AI style learning techniques.  Finding the correct classifier for performance metrics to improve the accuracy of an Oracle anomaly detection system can include ANN, naive Bayes, k-nearest neighbors and general algorithms.

 

There are several classification methods that can be used when evaluating anomaly detection models

 

• RoC Curve
• Area under RoC
• Precision-Recall Curve
• Mean average precision (mAP)
• Accuracy of classification

 

Below is a RoC chart used to score PCA and SVM models.  RoC charts plot false positive rates against true positive rates.   When comparing the PCA and the SVM model, PCA had a higher true positive rate.

Summary:  The Future of Autonomous Databases

Oracle has released its first deep learning database, marketed as “The world’s first self-driving database”.  Oracle has announced 18c as a new autonomous database that requires no human labor for daily operational task, can provide more security, and automate most database processes.  The database will self-tune, self-upgrade and self-patch – all while maintaining %99.995 availability with machine learning.  For many companies, especially those working on cloud and PaaS infrastructures, this will mean lower costs.  With Exadata, this would include compression techniques that would add further benefits to very large and enterprise level workloads.

 

Will there be more databases that will be completely run by Artificial Intelligence and Deep Learning algorithms?  As a DBA, maintaining a database can be arduous, but many of my DBA colleagues enjoy the respect and prestige of database management and database tuning.  With the role of a DBA evolving rapidly, autonomous database may provide the freedom for DBAs to provide database design and development to corporate teams.

 

It remains to be seen if databases as a service (DBaaS) will reach the reality of full autonomy.  It’s bound to happen before automobiles become level 5 autonomous.  Selecting the service on this platform could provide opportunities of minimal configurations – and you’re done.  Everything else is taken care of.  There would be no operator, either in the hosted environment or on premise, nor would anyone ever touch the database for any reason except for application and software development.

 

In summary, this is a very high-level article on techniques for using deep learning and machine learning on Oracle performance data.  I hope that this cursory introduction will inspire DBAs and operators to do their own research and apply it to their toolbox.

 

References

 

¹http://deeplearning.net/reading-list/

 

²https://www.analyticsvidhya.com/

 

³http://www.kdnuggets.com/

 

⁴http://www.ieee.org/

 

⁵https://www.computer.org/

 

⁶https://www.udacity.com/course/deep-learning-nanodegree-nd101se/deep-learning-nanodegree–nd101

 

⁷https://www.fast.ai

 

⁸“A Novel Intrusion Detection Model for Massive Network Using Convolutional Neural Networks” Kehe Wu; Zuge Chen; Wei Li.  IEEE Access. Received July 29, 2018.

 

⁹“Enhanced Network Anomaly Detection Based on Deep Neural Networks”.  Naseer, Sheraz; Saleem, Yasir; Khalid, Shezad, Bashir, Muhammad Khawar; Jihun Han, Iqbal, Muhammad Munwar; Kijun Han.  IEEE Acwwwcess Received June 3, 2018.  Accepted July 16, 2018.

 

¹⁰https://www.pyimagesearch.com Dr. Adrian Rosebrock

 

¹¹U.S. Energy Information Administration. https://www.eia.gov/.

 

¹²PJM Interconnection.  https://www.pjm.com/markets-and-operations.aspx

 

¹³Oracle Corporation.  https://www.oracle.com/index.html