ai-based cloud back strategy · cloud apps & tools, managed by oracle, behind your firewall...
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AI-based Cloud Back StrategyShyam Nath / Jayant Thomas (JT)
Shyam Nath, Oracle (IoT & Cloud)
Author: One of the first IIoTArchitecture Books
@ShyamVaran
Head of Machine Learning & AIhttps://www.linkedin.com/in/jayantthomas/
IoT Development Top Seller
• Both Shyam and JT worked at GE Digital, working on Industrial IoT Platform and Applications
• JT focused on Engineering side, Shyam focused on Product Management and IIoT Ecosystem (Predix platform used microservices paradigm)
• Subsequently JT has worked for Veritas - a data protection company, then in healthcare domain
• Shyam has worked for Oracle –Cloud Architecture
Copyright © 2018 Veritas Technologies4
Background
• Importance of Backup and Disaster Recovery (DR) in Enterprises
–ERP Applications
–Data Warehouse / Decision Support
– IoT and Emerging Technology Applications
• Different scenarios• Applications are running on-premises (own data center)
• Applications are mainly SaaS
• Applications hosted on Public Cloud as IaaS/PaaS
• Hybrid cloud
• Other scenarios – acquisition, divestiture etc.
Copyright © 2018 Veritas Technologies5
Introduction
Oracle Cloud Infrastructure (OCI)
Cloud apps & tools, managed by Oracle, behind your firewall
Integration
Mobile
Business Insight Collaboration
Custom AppsData Mgmt
Tools & services to build, extend, & deploy cloud
applications
Analytics
ERP
Data
Modern HR
CX
Supply Chain
Cloud applications to accelerate your business
Storage
Compute
Networking
Public cloud built for enterprises, optimized for Oracle Apps & Platform, integrated with open ecosystem
BROAD OPEN ECOSYSTEM
Third party apps, tools, and services to complete solutions
HYBRID
MicroServices and Oracle DB 19c
• AI-based predictive learning algorithms are being developed to recognize the differences between real and false disaster recovery situations – what is a connectivity issue vs an application outage.
• AI is being for developing the predictive learnings and automatically perform proactive recoveries, eliminating outages or reducing the impact before the end users of the Enterprise Applications realize it.
• Eventual Goal: The AI/ML-powered self-driving backup, provide a system that can automate the daily backup and recovery operations needed to meet the SLAs and other enterprise data protection goals
Copyright © 2018 Veritas Technologies9
AI / ML in DR
The Evolving Support Experience
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Streamlined Support Experience
Proactive & PrescriptiveReactive
Predictive Insights at Work
Bring the Power of AIOPS to your Infrastructure
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AI/ML Based Predictive Analytics
Maintenance Insights
Resource Insights
Support Insights
ML Platform Overview
Copyright © 2018 Veritas Technologies12
Copyright © 2018 Veritas Technologies.13
ML Platform using Kubernetes
Registry
ML AlgoML Algo
ML AlgoML Algo
ML AlgoML Algo2
WorkflowScheduler
Copyright © 2018 Veritas Technologies.14
Registry
ML AlgoML Algo
ML AlgoML Algo
ML AlgoML Algo2
ARGO Workflow
Airflow Workflow
ML Runtime on Kubernetes
Arango & Influx DBAudit
Copyright © 2018 Veritas Technologies.15
Registry
ML AlgoML Algo
ML AlgoML Algo
ML AlgoML Algo2
ARGO Workflow
Airflow Workflow
ML Runtime on Kubernetes
Arango & Influx DB
External API Kubernetes Cluster
Audit
• Reliability
• Dynamic Scaling
• Building Pipelines easily by chaining many different algorithms
• State management was easy with NoSQL DB providing the scalability
–Easily Persist data into DB and access the data in the next workflow task
Copyright © 2018 Veritas Technologies16
What Problem it solved ?
Predictive Insight in action
Copyright © 2018 Veritas Technologies17
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System Reliability Score (SRS)
RS1 [0-100] + Evidence
Reliability Score + Evidence backing the score
Config Drifts
Active, Predictive Faults
Storage Forecast
DedupAnomaly+ + +
RS2 [0-100] + Evidence
RS3 [0-100] + Evidence
RS4 [0-100] + Evidence
Model-N...+ +Predictive Events+
RS5 [0-100] + Evidence
RS-N [0-100] + Evidence
Recommendations – Flow, Feedback, Learning
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ManualRecommendation
Filtering &
Prioritization
Auto-SupportUI
Re
com
me
nd
ati
on
Ad
viso
rs
SuggestedRecommendations
ApplianceSpecific
Recommendations
Storage Forecast
Thermal Anomaly
Survival Analysis
ConfigurationDrift
NaïveProbability
Management Services Layer
Cleansing, filtering, learning
Feedback
Capture Tribal Knowledge
Learn, Correct, Adjust on feedback
Storage Usage Forecast
Time System Log Share Advanced Disk MSDP MSDP Catalog
NBU Catalog
Config
2016-04-11T17:34:55.37602
14.0 15.00415
0.0 947.537800 6312.08 1.66180 0.0 21.13150
2016-04-11T17:49:51.58820
18.0 15.01245
0.0 947.542024 6312.14 1.66230 0.0 21.36500
Params: {Appliance ID,Storage Pools,...
}
Storage Usage Reliability Score Reliability Score [0-100] +
Evidence
Storage Forecast Algorithm
Forecast DataConstraintsWeighting
Time System Log Share Advanced Disk MSDP MSDP Catalog
NBU Catalog
Config
2016-04-12 19.0 15.01345 0.0 947.5300 6312.16 1.66380 0.0 21.456
2016-04-13 20.5 15.01456
0.0 947.5424 6312.18 1.66430 0.0 21.635
Copyright © 2018 Veritas Technologies21
Copyright © 2018 Veritas Technologies22
Copyright © 2018 Veritas Technologies23
• Backup and Restore is a complex process and involves OnPrem and Cloud H/W & S/w
• Backing up different workloads to different targets is important.
• AI can help with improving the backup strategies and with preventing failures.
• Contact:
[email protected] / [email protected]
Thank You
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Summary