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Introducing new AIOps innovations in Oracle 19c - San Jose AICUG
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Introducing new AIOps innovations in Oracle 19c - San Jose AICUG
1.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Introducing New AI Ops Innovations in Oracle Autonomous Database Service AICUG Meetup – San Jose – ActionSpot Sandesh Rao VP Autonomous Health and Machine Learning
2.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation.
3.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | whoami Real Application Clusters - HA DataGuard- DR Machine Learning- AIOps Enterprise Management Sharding Big Data Operational Management Home Automation Geek @sandeshr https://www.linkedin.com/in/raosandesh/
4.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Agenda • What motivated us to go into Machine Learning ? • Which algorithms, tools & technologies are used? • Oracle use cases for Applied Machine Learning in the Cloud - AIOps • Oracle & Machine Learning initiatives and tools • Questions and Open Talk
5.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | 5 Why Machine Learning for us and why now? • Lots of Data generated as exhaust from systems – Cloud , different formats and interfaces , frameworks • Machine Learning has become accessible – Anyone can be a Data Scientist – Algorithms are accessible as libraries aka scikit , keras , tensorflow .. – Sandbox to get started as easy as a docker init • Business use cases • How to find value from the data , fewer guesses to make decisions
6.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | ML Project Workflow • Set Business Objectives • Gather , Prepare and Cleanse Data • Model Data – Feature Extraction , Test , Train , Optimizer – Loss Function , effectiveness – Framework and Library to use • Apply the Model as an inference engine – Decision making using the Model’s output – Tune Model till outcome is closer to Business Objective 6 Set Business Objectives Understand Use case Create Pseudo Code Synthetic Data Generation Pick Tools and Frameworks Train Test Model Deploy Model Measure Results and Feedback
7.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Types of Machine Learning Supervised Learning Predict future outcomes with the help of training data provided by human experts Semi-Supervised Learning Discover patterns within raw data and make predictions, which are then reviewed by human experts, who provide feedback which is used to improve the model accuracy Unsupervised Learning Find patterns without any external input other than the raw data Reinforcement Learning Take decisions based on past rewards for this type of action 7
8.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | • Hierarchical k-means, Orthogonal Partitioning Clustering, Expectation- Maximization Clustering Feature Extraction/Attribute Importance / Component Analysis • Decision Tree, Naive Bayes, Random Forest, Logistic Regression, Support Vector Machine Classification 8 Machine Learning Algorithms • Multiple Regression, Support Vector Machine, Linear Model, LASSO, Random Forest, Ridge Regression, Generalized Linear Model, Stepwise Linear Regression Regression Association & Collaborative Filtering Reinforcement Learning - brute force, Monte Carlo, temporal difference.... • Many different use cases Neural network & deep Learning with Deep Neural Network
9.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Modeling Phase – AutoML2 to the rescue Provide Dataset to AutoML2 Configuration parameters for model picked Dataset is divided into training set & testing set Actual Training Evaluate performance of trained model Tweak model parameters, change predictors change test/train data splits and change algorithms Pick model plus parameters depending on outcome and measure , F1 , Precision , Recall , MSE Document all runs and apply A/B testing to see what the variations produce 9
10.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | 10 Tools & Libraries Assisting ML projects
11.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | What is Oracle Doing Around Machine Learning? • Big Data Appliance • Big Data Discovery , Big Data Preparation Data Visualization Cloud • Analytics Cloud – Sales, Marketing, HCM on top of SaaS • DaaS – Oracle Data Cloud , Eloqua .. • Oracle Labs (labs.oracle.com) – Machine Learning Research Group • Autonomous Database – Zeppelin Notebooks preloaded with use cases – Applied Machine Learning used for Implementing AIOps 11
12.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Oracle AI Platform Cloud Service – Coming Soon… • Collaborative end-to-end machine learning in the cloud • Enables data science teams to – Organize their work – Access data and computing resources – Build , Train , Deploy – Manage models • Collaborative , Self-Service , Integrated • https://cloud.oracle.com/en_US/ai-platform 12
13.
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Oracle and/or its affiliates. All rights reserved. | Oracle Autonomous Data Warehouse Cloud Key Features Highly Elastic Independently scale compute and storage, without having to overpay for fixed blocks of resources Built-in Web-Based SQL ML Tool Apache Zeppelin Oracle Machine Learning notebooks ready to run ML from browser Database migration utility Dedicated cloud-ready migration tools for easy migration from Amazon Redshift, SQL Server and other databases Enterprise Grade Security Data is encrypted by default in the cloud, as well as in transit and at rest High-Performance Queries and Concurrent Workloads Optimized query performance with preconfigured resource profiles for different types of users Oracle SQL Autonomous DW Cloud is compatible with all business analytics tools that support Oracle Database Self Driving Fully automated database for self-tuning patching and upgrading itself while the system is running Cloud-Based Data Loading Fast, scalable data-loading from Oracle Object Store, AWS S3, or on-premises 13 Oracle Machine Learning
14.
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Oracle and/or its affiliates. All rights reserved. | Oracle Machine Learning and Advanced Analytics • Support multiple data platforms, analytical engines, languages, UIs and deployment strategies Strategy and Road Map Big Data / Big Data Cloud Relational ML Algorithms Common core, parallel, distributed SQL R, Python, etc.GUI Data Miner, RStudio Notebooks Advanced Analytics Oracle Database Cloud DWCS
15.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | CLASSIFICATION – Naïve Bayes – Logistic Regression (GLM) – Decision Tree – Random Forest – Neural Network – Support Vector Machine – Explicit Semantic Analysis CLUSTERING – Hierarchical K-Means – Hierarchical O-Cluster – Expectation Maximization (EM) ANOMALY DETECTION – One-Class SVM TIME SERIES – Holt-Winters, Regular & Irregular, with and w/o trends & seasonal – Single, Double Exp Smoothing REGRESSION – Linear Model – Generalized Linear Model – Support Vector Machine (SVM) – Stepwise Linear regression – Neural Network – LASSO ATTRIBUTE IMPORTANCE – Minimum Description Length – Principal Comp Analysis (PCA) – Unsupervised Pair-wise KL Div – CUR decomposition for row & AI ASSOCIATION RULES – A priori/ market basket PREDICTIVE QUERIES – Predict, cluster, detect, features SQL ANALYTICS – SQL Windows, SQL Patterns, SQL Aggregates A1 A2 A3 A4 A5 A6 A7 • OAA (Oracle Data Mining + Oracle R Enterprise) and ORAAH combined • OAA includes support for Partitioned Models, Transactional, Unstructured, Geo-spatial, Graph data. etc, Oracle’s Machine Learning & Adv. Analytics Algorithms FEATURE EXTRACTION – Principal Comp Analysis (PCA) – Non-negative Matrix Factorization – Singular Value Decomposition (SVD) – Explicit Semantic Analysis (ESA) TEXT MINING SUPPORT – Algorithms support text type – Tokenization and theme extraction – Explicit Semantic Analysis (ESA) for document similarity STATISTICAL FUNCTIONS – Basic statistics: min, max, median, stdev, t-test, F-test, Pearson’s, Chi-Sq, ANOVA, etc. R PACKAGES – CRAN R Algorithm Packages through Embedded R Execution – Spark MLlib algorithm integration EXPORTABLE ML MODELS – C and Java code for deployment
16.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Oracle Machine Learning Key Features • Collaborative UI for data scientists – Packaged with Autonomous Data Warehouse Cloud (V1) – Easy access to shared notebooks, templates, permissions, scheduler, etc. – SQL ML algorithms API (V1) – Supports deployment of ML analytics Machine Learning Notebook for Autonomous Data Warehouse Cloud
17.
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Oracle and/or its affiliates. All rights reserved. | Oracle Machine Learning UI in ADW
18.
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Oracle and/or its affiliates. All rights reserved. |
19.
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Oracle and/or its affiliates. All rights reserved. |
20.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | PaaS Resource Lifecycle Management Bare-Metal thru Installation Upgrade Patching Dependency Resolution Prerequisites Resolution Required Capabilities Automatable Scalable Online (if possible) PaaS Application Lifecycle Management Installation Upgrade Patching Dependency Resolution Prerequisites Resolution Workload Profile Identification Placement determination SLA management Required Capabilities Automatable Provider Interoperable Cloud Operations Early Warning Response System Detect degradations and faults Pinpoint root cause & component Push warnings and alerts Push targeted corrective actions SLA – based resource management Real-time Health Dashboard Required Capabilities Continuous and frequent Autonomous Action Enabled OSS Integration Enabled Management Interoperable AIOps Cloud Operations – 3 Strategic Pillars Resource Lifecycle Management Database Lifecycle Management Database Autonomous Self-Repair
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Oracle and/or its affiliates. All rights reserved. | Database Operations Runtime Management • Humans are slow and don’t scale • Manual triage and floods of notifications do not scale • Applied Machine learning techniques effectively respond in real-time and without huge impact to operations – Prevent problems and optimize solutions in real-time – Recover from failures and identify root cause quickly with minimal intervention
22.
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Oracle and/or its affiliates. All rights reserved. | Applied Machine Learning for Cloud Operations • Generic ML-extracted Data Clusters are insufficient for diagnostics • Operational data correlation does not determine root cause • Trusted root cause determination critical to swift corrective actions • Algorithms selected and models built require domain expertise • Models refined via field feedback Subject Matter ExpertLog ASH Metrics ML Knowledge Extraction Model Generation Human Supervision Application Optimized Models Feedback Scrub Data
23.
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Oracle and/or its affiliates. All rights reserved. | Autonomous Health Cloud Platform MachinesSmart Collectors SRs Expert Input Feedback & Improvement Bugs 1 SRs Logs Model Generation Model Knowledge Extraction Applied Machine Learning Cloud Ops Object Store Admin UI in Control Plane Oracle Support Bug DB SE UI in Support Tenant (CNS) Cleansing, metadata creation & clustering 5 Model generation with expert scrubbing 6 Deployed as part of cloud image, running from the start 1 Proactive regular health checking, real- time fault detection, automatic incident analysis, diagnostic collection & masking of sensitive data 2 Use real-time health dashboards for anomaly detection, root cause analysis & push of proactive, preventative & corrective actions. Auto bug search & auto bug & SR creation. 3 Auto SR analysis, diagnosis assistance via automatic anomaly detection, collaboration and one click bug creation 4 Message Broker
24.
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Oracle and/or its affiliates. All rights reserved. |
25.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Autonomous Health - Anomaly Timeline Remove clutter from log files to find the most important events to enable root cause analysis
26.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Anomaly Detection – High Level Known normal log entry (discard) Probable anomalous Line (collect) Log Collection File Type 1 File Type 2 File Type n.. Log File Anomaly Timeline Probable Anomalies
27.
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Oracle and/or its affiliates. All rights reserved. | Trace File Analyzer – High Level Anomaly Detection Flow Log Cleansing 1 2 3 4 5 6 Entry Feature Creation Entry Clustering Model Generation Expert Input Knowledge Base Creation Knowledge Base Indexing Feedback Training Real-time Log File Processing Timestamp Correlation & Ranking 8 9 7 Batch Feedback
28.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Knowledge Base Indexing Entry Clustering Model Generation Entry Feature Creation Log Cleansing 1 2 3 4 5 6 Expert Input Knowledge Base Creation FeedbackTraining Real-time Log File Processing Timestamp Correlation & Ranking 8 97 Batch Feedback Log File Collection Data Cleansing & Reduction waited for 'ASM file metadata operation', seq_num: 29 2016-10-20 02:12:56.937 : OCRRAW:1: kgfo_kge2slos error stack at kgfoAl06: ORA-29701: unable to connect to Cluster Synchronization Service 2016-10-20 02:23:02.000 : OCRRAW:1: kgfo_kge2slos error stack at kgfoAl06: ORA-29701: unable to connect to Cluster Synchronization Service 2016-10-20 02:23:03.563 : OCRRAW:1: kgfo_kge2slos error stack at kgfoAl06: ORA-29701: unable to connect to Cluster Synchronization Service waited for [STR] seq_num: [NSTR] [NSTR] [NSTR] : [NSTR] [NSTR] unable to connect to Cluster Synchronization Service
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Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | .. Seen in Bugs Total Bugs Seen Seen in Files Total Files Seen Total Count .. .. 13 40 1440 5088 2890 .. Feature Extraction waited for [STR] seq_num: [NSTR] [NSTR] [NSTR] : [NSTR] [NSTR] unable to connect to Cluster Synchronization Service Knowledge Base Indexing Entry Clustering Model Generation Log Cleansing 1 3 4 5 6 Expert Input Knowledge Base Creation FeedbackTraining Real-time Log File Processing Timestamp Correlation & Ranking 8 97 Batch Feedback Entry Feature Creation 2
30.
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Oracle and/or its affiliates. All rights reserved. | Data Clustering Record Merging and feature aggregation for records belonging to same log signature Knowledge Base Indexing Entry Clustering Model Generation Log Cleansing 1 3 4 5 6 Expert Input Knowledge Base Creation FeedbackTraining Real-time Log File Processing Timestamp Correlation & Ranking 8 97 Batch Feedback 2 Entry Feature Creation
31.
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Oracle and/or its affiliates. All rights reserved. | Model Generation Data Clustering Expert Input Decision Tree Classifier First time labelling through functional rules Labelled dataset Result EvaluationUpdate Labelling 3 4 5 76 Entry Clustering Log Cleansing 1 3 Training Real-time Log File Processing Timestamp Correlation & Ranking 8 9 Batch Feedback 2 Knowledge Base Indexing Model Generation 4 5 6 Expert Input Knowledge Base Creation Feedback 7 Entry Feature Creation
32.
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Oracle and/or its affiliates. All rights reserved. | Autonomous Health - Database Performance Preserving instance performance when database resources are constrained
33.
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Oracle and/or its affiliates. All rights reserved. | Database Health - Applied Machine Learning • Actual Internal and External customer data drives model development • Applied purpose-built Applied ML for knowledge extraction • Expert Dev team scrubs data • Generates Bayesian Network-based diagnostic root-cause models • Uses BN-based run-time models to perform real-time prognostics Discovers Potential Cluster & DB Problems CHA Dev TeamLog ASH Metrics ML Knowledge Extraction BN Models Expert Supervision DB+Node Runtime Models Feedback Scrub Data CHA CHA
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Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Database Data Flow Overview Autonomous Health – Database Performance OS DataDB Data Database Prognostics Engine Alert & Preventive Action • Reads OS and DB Performance data directly from memory • Uses Machine Learning models and data to perform prognostics • Detects common RAC database problems • Performs root cause analysis • Sends alerts and preventative actions to Cloud Ops per target
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Oracle and/or its affiliates. All rights reserved. | Data Sources and Data Points Autonomous Health – Database Performance Time CPU ASM IOPS Network % util Network_ Packets Dropped Log file sync Log file parallel write GC CR request GC current request GC current block 2-way GC current block busy Enq: CF - conten tion … 15:16:00 0.90 4100 13% 0 2 ms 600 us 0 0 300 us 1.5 ms 0 A Data Point contains > 150 signals (statistics and events) from multiple sources OS, ASM , Network DB ( SH, AWR session, system and PDB statistics ) Statistics are collected at a 1 second internal sampling rate , synchronized, smoothed and aggregated to a Data Point every 5 seconds
36.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Data Flow Overview Autonomous Health – Database Performance
37.
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Oracle and/or its affiliates. All rights reserved. | Models Capture the Dynamic Behavior of all Normal Operation Models Capture all Normal Operating Modes 0 5000 10000 15000 20000 25000 30000 35000 40000 10:00 2:00 6:00 5100 9025 4024 2350 4100 22050 10000 21000 4400 2500 4900 800 IOPS user commits (/sec) log file parallel write (usec) log file sync (usec) A model captures the normal load phases and their statistics over time , and thus the characteristics for all load intensities and profiles . During monitoring , any data point similar to one of the vectors is NORMAL. One could say that the model REMEMBERS the normal operational dynamics over time In-Memory Reference Matrix (Part of “Normality” Model) IOPS #### 2500 4900 800 #### User Commits #### 10000 21000 4400 #### Log File Parallel Write #### 2350 4100 22050 #### Log File Sync #### 5100 9025 4024 #### … … … … … …
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Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | CHA Model: Find Similarity with Normal Values Autonomous Health – Database Performance Observed values (Part of a Data Point) Estimator/predictor (ESEE): “based on my normality model, the value of IOPS should be in the vicinity of ~ 4900, but it is reported as 10500, this is causing a residual of ~ 5600 in magnitude”, Fault detector: “such high magnitude of residuals should be tracked carefully! I’ll keep an eye on the incoming sequence of this signal IOPSand if it remains deviant I’ll generate a fault on it”. In-Memory Reference Matrix (Part of “Normality” Model) IOPS #### 2500 4900 800 #### User Commits #### 10000 21000 4400 #### Log File Parallel Write #### 2350 4100 22050 #### Log File Sync #### 5100 9025 4024 #### … … … … … … 10500 20000 4050 10250 … Residual Values (Part of a Data Point) 5600 -1000 -50 325 … Observed - Predicted =
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Oracle and/or its affiliates. All rights reserved. | Inline and Immediate Fault Detection and Diagnostic Inference Autonomous Health – Database Performance Machine Learning, Pattern Recognition, & BN Engines Time CPU ASM IOPS Network % util Network_ Packets Dropped Log file sync Log file parallel write GC CR request GC current request GC current block 2-way GC current block busy Enq: CF - conten tion … 15:16:00 0.90 4100 88% 105 2 ms 600 us 504 ms 513 ms 2 ms 5.9 ms 0 15:16:00 OK OK HIGH 1 HIGH 2 OK OK HIGH 3 HIGH 3 HIGH 4 HIGH 4 OK Input : Data Point at Time t Fault Detection and Classification Diagnostic Inference 15:16:00 Symptoms 1. Network Bandwidth Utilization 2. Network Packet Loss 3. Global Cache Requests Incomplete 4. Global Cache Message Latency Root Cause (Target of Corrective Action) Network Bandwidth Utilization Diagnostic Inference Engine
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Oracle and/or its affiliates. All rights reserved. | Cross Node and Cross Instance Diagnostic Inference Autonomous Health - Cluster Health Advisor 15:16:00 Root Cause (Target of Corrective Action) Network Bandwidth Utilization Diagnostic Inference Engine 15:16:00 Root Cause (Target of Corrective Action) Network Bandwidth Utilization Diagnostic Inference Engine 15:16:00 Root Cause (Target of Corrective Action) Network Bandwidth Utilization Diagnostic Inference Engine Cross Target Diagnostic Inference Node 1 Node 2 Node 3 Corrective Action Target
41.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Autonomous Health - Maintenance Slot Identification Find the next best window when maintenance can be performed with minimal service impact
42.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Autonomous Health - Maintenance Slot Identification • Identify Relevant Workload Metrics – Ex: Average Active Sessions, CPU/Mem/IO Utilization • Time Series Decomposition – Trend – Seasonality – Residual • Workload Seasonality Determination Locating Minimas • Optimum Window Identification and Validation Model Generation and Training Flow
43.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | START_TIME CNT 2018-04-11 15:00:00 290 2018-04-11 16:00:00 31120 2018-04-11 17:00:00 21530 2018-04-11 18:00:00 26240 2018-04-11 19:00:00 40520 2018-04-11 20:00:00 54270 2018-04-11 21:00:00 51460 2018-04-11 22:00:00 44310 2018-04-11 23:00:00 25690 START_TIME 2018-04-11 15:00:00 -0.226098 2018-04-11 16:00:00 -0.069821 2018-04-11 17:00:00 -0.350088 2018-04-11 18:00:00 -0.187483 2018-04-11 19:00:00 -0.513240 2018-04-11 20:00:00 0.019737 2018-04-11 21:00:00 0.059213 2018-04-11 22:00:00 -0.011312 2018-04-11 23:00:00 -0.179156 START_TIME 2018-04-11 15:00:00 5.669881 2018-04-11 16:00:00 10.345606 2018-04-11 17:00:00 9.977203 2018-04-11 18:00:00 10.175040 2018-04-11 19:00:00 10.609551 2018-04-11 20:00:00 10.901727 2018-04-11 21:00:00 10.848560 2018-04-11 22:00:00 10.698966 2018-04-11 23:00:00 10.153857 Current Date : 2018-05-12 15:00:00 Current Position in Seasonality : -0.22609829742533585 Best Maintenance Period in next Cycle : 2018-05-12 19:00:00 Worst Maintenance Period in next Cycle : 2018-05-13 08:00:00 Original observation data1 Apply convolution filter & average2 Calculate seasonality3 Use seasonality to predict best maintenance window 4 Autonomous Health - Maintenance Slot Identification Seasonality Determination to Window Identification Flow
44.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Autonomous Health - Maintenance Slot Identification Validating Performance Against Random or Periodic Window Selection
45.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Autonomous Health - Bug Duplicate Identification Discovers Duplicate Bugs, Correlated Issues and Prioritizes Based Upon Customer Impact
46.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | SRDCs (Service Request Diagnostic Collection) Oracle Grid Infrastructure & Databases TFAML 1 TFAML detects a fault 2Diagnostics are collected 3 Distributed diagnostics are consolidated and packaged 4 Notification of fault is sent 5 Diagnostic collection is uploaded to Oracle Storage Service for later analysis Object Store
47.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Maintenance Slot Identification
48.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | BUG DB Adaptive Bug Search – Applied Machine Learning • Bugs are submitted from over 400 Oracle products • Performs ML Logistic Regression on training set of bugs to generate model • Displays up to 8 possible duplicates per bug or SR • Feedback improves model accuracy – Direct from developers – Indirect from bug updates Discovers Duplicate Bugs and Correlated Issues ABS Dev TeamBugs Bugs DupBugs ML Logistic Regression Model Generation Expert Supervision ABS Runtime Model Dev Feedback Bug Submission Bug and Duplicates Together ABS Service Feedback Scrub Data
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Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Autonomous Health – Adaptive Bug Search (ABS) • Issues parsed into different features – Error stack, Trace data, Problem description, etc. • Issues represented as a cluster of features – i.e. All bugs in a bug tree contribute towards the feature set • Logistic Regression applied to build a model – Model defines the significance of each feature • Similarity between issues computed using the model – Identifies the root of the cluster (aka bug tree) • Feedback used to improve the model – Feedback is automatically derived based on how the bug gets closed High Level Flow
50.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Autonomous Health - Anomaly Analysis Identify a series of events as connected and representing the signature of a problem
51.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Longest Common Subsequence of Anomalous Entries 51 1. Start by classifying a problem such as an important ORA or CRS error 2. Find occurrences of the problem across many different log files 3. Identify anomalous entries and lifecycle events in chronological order within a predefined time window around the occurrence of the problem in all the logs 4. Compare the repeating anomalous / lifecycle entries to identify the longest common subsequence of anomalous entries Find the Finite State Automata(FSA)
52.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Example signatures and their analysis Sample Central Event : 2017-01-19 16:51:20.562 [OCSSD(24862)]CRS-1656: The CSS daemon is terminating due to a fatal error; Details at (:CSSSC00012:) in /tools/list/grid/orabase/diag/crs/ur102ora3502c/crs/trace/ocssd.trc 52 Knowledge Id Sample Line (States in FSA for central event) 52CC1E8631FC2674E053B580E80AB08D 2016-10-16 21:22:36.520+CRS-5008: Invalid attribute value: en4 for the network interface 52CC1E8632082674E053B580E80AB08D 2016-10-16 21:25:11.516 [OCSSD(6816354)]CRS-1608: This node was evicted by node 3, rwsbs03; details at (:CSSNM00005:) in /u01/app/crsusr/diag/crs/rwsbs02/crs/trace/ocssd.trc. 52CC1E8632212674E053B580E80AB08D 2016-10-16 21:25:17.927 [OCSSD(18219406)]CRS-1654: Clean up of CRSD resources finished successfully. 52CC1E8631EC2674E053B580E80AB08D 2016-10-16 21:25:17.927 [OCSSD(18219406)]CRS-1655: CSSD on node rwsbs01 detected a problem and started to shutdown. 52CC1E8632272674E053B580E80AB08D 2016-10-16 21:25:19.431 [OCSSD(18219406)]CRS-8503: Oracle Clusterware process OCSSD with operating system process ID 18219406 experienced fatal signal or exception code 6. 52CC1E8632202674E053B580E80AB08D 2016-10-16 21:25:21.788 [CRSD(44696012)]CRS-0805: Cluster Ready Service aborted due to failure to communicate with Cluster Synchronization Service with error [3]. Details at (:CRSD00109:) in /u01/app/crsusr/diag/crs/rwsbs01/crs/trace/crsd.trc. 52CC1E86208C2674E053B580E80AB08D 2016-10-18 02:02:00.835 : CSSD:6684: (:CSSSC00012:)clssscExit: A fatal error occurred and the CSS daemon is terminating abnormally 52CC1E861F132674E053B580E80AB08D CLSB:6684: Oracle Clusterware infrastructure error in OCSSD (OS PID 12452524): Fatal signal 6 has occurred in program ocssd thread 6684; nested signal count is 1 52CC1E861E552674E053B580E80AB08D Incident 393 created, dump file: /u01/app/crsusr/diag/crs/rwsbs02/crs/incident/incdir_393/ocssd_i393.trc 52CC1E861F332674E053B580E80AB08D 2016-10-18 02:02:07.113 : SKGFD:5655: ERROR: -9(Error 27041, OS Error (IBM AIX RISC System/6000 Error: 47: Write-protected media 52CC1E86207C2674E053B580E80AB08D 2016-10-18 02:02:07.774 : CSSD:5655: clssnmvDiskCreate: Cluster guid ea34893b9442ef79ff642d70699aff9d found in voting disk /dev/rbs01_100G_asm1 does not match with the cluster guid 7b63590c34fa5f44bf6944aefa4ee85d obtained from the GPnP profile 52CC1E863DB82674E053B580E80AB08D 2017-01-19 16:48:01.057 [OCSSD(24862)]CRS-1649: An I/O error occurred for voting file: /dev/rdsk/c1d16; details at (:CSSNM00059:) in /tools/list/grid/orabase/diag/crs/ur102ora3502c/crs/trace/ocssd.trc. 52CC1E863DBC2674E053B580E80AB08D 2017-01-19 16:49:40.550 [OCSSD(24862)]CRS-1615: No I/O has completed after 50% of the maximum interval. Voting file /dev/rdsk/c1d16 will be considered not functional in 99508 milliseconds
53.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Example signatures and their analysis 5 Mins before Central Event 5 Minute After Central Event Central Event 52CC1E863 1FC2674E0 53B580E80 AB08D 52CC1E862 07C2674E0 53B580E80 AB08D 52CC1E861 F332674E0 53B580E80 AB08D 52CC1E861 E552674E0 53B580E80 AB08D 52CC1E861 F132674E0 53B580E80 AB08D 52CC1E862 08C2674E0 53B580E80 AB08D 52CC1E863 2202674E0 53B580E80 AB08D 52CC1E863 2272674E0 53B580E80 AB08D 52CC1E863 1EC2674E0 53B580E80 AB08D 52CC1E863 2212674E0 53B580E80 AB08D 52CC1E863 2082674E0 53B580E80 AB08D 52CC1E863 DBC2674E0 53B580E80 AB08D 52CC1E863 DB82674E0 53B580E80 AB08D 52CC1E867 22C2674E0 53B580E80 AB08D
54.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Autonomous Health - Anomaly Analysis Generating Event Signatures Event Signature 35 Event Signature 3435 Event Signature 494 Event Signature 3948 Event Signature 292 Event Signature 434933 Node Eviction 1 Timeline Event Signature 3434 Event Signature 3435 Event Signature 4344 Event Signature 3048 Event Signature 202 Event Signature 434983 Node Eviction 2 Timeline Event Signature 35 Event Signature 3435 Event Signature 3048 Event Signature 3948 Event Signature 292 Event Signature 434933 New Signature Check for weighted probabilistic match ProblemSignatureRepository
55.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Real-time Prevention • Data Ingestion – Kernel Smoothing and Moving Average – Interpolation and Imputation • Prediction and Pattern Recognition – Multivariate and Auto-Associative Regression – Clustering, Similarity Operators and Bayes Networks • Fault and Anomaly Detection – Sequential Probability Ratio Tests – Conditional Probability Filters & Hidden Markov Models • Prognosis and Diagnosis – Bayesian Belief Networks and Probabilistic Inference – Remaining Useful Life Regression and GPM Models Rapid Recovery Autonomous Health Platform ML Technologies • Data Ingestion – ELK – Lucene • Prediction and Pattern Recognition – TF-IDF and Bag-of-Words modelling – Sequence Matcher – K-nearest Neighbour • Fault and Anomaly Detection – Decision Trees and Random Forest – Sequential Pattern Mining • Prognosis and Diagnosis – Recurrent neural Network – Long short-term memory Predictive Analysis
56.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Autonomous Health – Part of Oracle AIOps for the Cloud • Historical Data Management • Streaming Data Management • Log Data Ingestion • Metric Data Ingestion • Autonomous Collections • Automated Pattern Discovery and Prediction • Anomaly Detection • Root Cause Determination • Autonomous Operations
57.
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Oracle and/or its affiliates. All rights reserved. |
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