Machine Learning Diagnostics Using Oracle AHF-Pub

65 Pages·2017·9.06 MB·English

Machine Learning Diagnostics Using
Oracle Autonomous Health Framework
Mark V. Scardina – Director of Product Management
Ankita Khandelwal – Product Manager
Oracle Autonomous Health Framework
October 4, 2017
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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, and timing of any features or
functionality described for Oracle’s products remains at the sole discretion of Oracle.
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Program Agenda
1 Introducing Applied Machine Learning for Operations
Applied Machine Learning for Real-time Prevention
2
Applied Machine Learning for Rapid Recovery
3
ODA Management Appliance Profile
4
For Further Information / Q & A
5
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Program Agenda with Highlight
1 Introducing Applied Machine Learning for Diagnostics
Applied Machine Learning for Real-time Prevention
2
Applied Machine Learning for Rapid Recovery
3
ODA Management Appliance Profile
4
For Further Information / Q & A
5
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Why Applied Machine Learning?
• Brings an application’s perspective versus a platform toolkit viewpoint
• Brings data science, algorithms, and domain expertise together
• Packages machine learning into usable, real-world operational algorithms
and models that are applied at runtime
• Produces results and recommendations easily understood and trusted by
non-data scientist/analyst end-users
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Applied Machine Learning for Diagnostics
• Generic ML-extracted Data Clusters
are insufficient for diagnostics
Feedback
• Operational data correlation does
not determine root cause
ASH
Subject Matter
• Trusted root cause determination Expert
critical to swift corrective actions ML
Knowledge
Extraction
• Algorithms selected and models Application
Optimized
Human
built require domain expertise Supervision Models
Scrub Data
Model
• Models refined via field feedback
Generation
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Cluster
Verification
Utility
Quality of
Oracle 12c
Service ORAchk
Management
Autonomous
Health
Cluster
Memory
Framework Health
Guard
Monitor
Powered by Applied Machine Learning Hang Cluster
Manager Health
Advisor
Trace File
Analyzer
Managed Centrally by ODA DSC
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Applied ML in Oracle Autonomous Health Framework
Real-time Prevention Rapid Recovery
Inputs Inputs
Best
Bugs/SRs Metrics Logs Diagnostics Prognostics Alerts Metrics Logs Bugs/SRs
Practices
Cluster Health Advisor
Trace File Analyser SRs
QoSM Hang Manager
Policies
Manual Auto GI Mgmt Manual Auto
Preventative Repository Corrective
Actions Actions
Oracle Enterprise Manager Oracle Support Services
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Program Agenda
1 Introducing Applied Machine Learning for Diagnostics
Applied Machine Learning for Real-time Prevention
2
Applied Machine Learning for Rapid Recovery
3
ODA Management Appliance Profile
4
For Further Information / Q & A
5
Copyright © 2017,Oracle and/or its affiliates. All rights reserved. | 10