Digital transformation, which requires an unprecedented scale and complex infrastructure, has sparked an IT service availability crisis in the enterprise. Traditional bottom-up, rules-driven approaches to incident management have outlived their usefulness. Artificial intelligence for IT operations, or AIOps, provides a new direction by using operational data to drive a top-down approach to service availability. This enables greater business agility while improving customer service, lowering operation costs, and boosting the productivity of IT operations teams. In this talk, Moogsoft CEO, Phil Tee, describes how AIOps provides enterprises with a critical advantage and paves the way for the future of service assurance.

1. Overcoming Tomorrow's
Operational Challenges
with AIOps
Phil Tee, CEO | Phil@Moogsoft.com27 March 2019

2. © 2019 M oogsoft. A ll rights reserved.
Mainframe Era
Golden
Distributed Computing
Tarnished
Software Defined
Everything
Monitoring
Platforms
Monolithic Suites
ISV Platforms
Patchwork, Open Source,
Departmental
Source Events
Fixed
~10-20 eps
Custom/Standard (SNMP) + Fixed
~100-1000 eps
Chaotic, Unstructured
~1000-100,000 eps
Configuration
Static
Time between change ~ days (TBC)
Flexible
TBC ~ hours
Chaotic
TBC <1 second
Infrastructure
Monolithic
Single Vendor
Multi vendor
UNIX/IP/Windows Client Server
Virtualized/Containers
Fluid/UNIX/PRP/Mobile/Micro Services
1980s 1990s 2000s 2010s
What Will Tomorrow Look Like?

3. © 2019 M oogsoft. A ll rights reserved.
The Economy Drives IT
Form Follows Function…
...Function Follows Need
Digital
Transformation
demands DevOps &
Elastic
Birth of the
Mainframe
on an American
Airlines flight
Deregulation
drives the
client/server to
virtual transition

4. © 2019 M oogsoft. A ll rights reserved.
On-Demand Economy Mandates CONSTANT CHANGE & ZERO DOWNTIME
In this Digitally Transformed World, We...
Purchase Transact Interact Access

5. © 2019 M oogsoft. A ll rights reserved.
Scale
• 105
+ Moving Parts
• 106
+ Notifications
• 109+ Data Points
• 1012
→ 10120
+ Possible Failure Modes
+ Bounded by the estimated information content
of the universe!
Current and Future Demands
Compulsion to Change
Complexity
• Reduction in the Unit of Compute
• Mainframe → Server → VM → Container
• Multiple Orders of Magnitude
• Increase in Change Cycle
• Fully Fluid CI/CD Cycle

6. © 2019 M oogsoft. A ll rights reserved.
Siloed
teams
and tools
Too
many
alerts
No context
when an
incident
occurs
No
early
warning
DevOps
lacks
proactive
assurance
DATA INFORMATIONOverwhelmed by and a lack of
Traditional IT Ops Caught Flat-Footed

7. © 2019 M oogsoft. A ll rights reserved.
75-80%
of IT
organizations
are siloed
95% of
Alerts
lack
informational
value
>45%
incidents
involve “all
hands” war
room
>73%
incidents
raised by
end usersMany siloed
war rooms
DATA INFORMATIONOverwhelmed by and a lack of
Traditional IT Ops Caught Flat-Footed

8. © 2019 M oogsoft. A ll rights reserved.
Evidence of Systematic Failure
Troubling Trends: CIO Survey Results
74%
Incidents detected by
customers before
support is aware
66%
Existing monitoring
solutions identify less
than half of all
performance issues or
outages
59%
Growing IT complexity
is leading to more
outages
Greater Scale, Less Visibility

9. © 2019 M oogsoft. A ll rights reserved.
INCREASE frequency of change, stability and availability of IT services1
REDUCE resource operations workload and INCREASE productivity2
CONSOLIDATE tools3
MIGRATE to the cloud4
SUPPORT software-defined services5
SUPPORT microservices-based software architecture6
IT Ops Priorities Driven by Digital Transformation

10. © 2019 M oogsoft. A ll rights reserved.
AFTER
State + Measurement Combined
DATA EVENTS
SYSTEM
Data
AI
Feature
Act
• Fluid
• High Scale
• Determinate
What to Do?
SYSTEM EVENTS
BEFORE
“State” “Measurement”
Analyze
Act
Measure
Hypothesize
RULES
• Rigid
• Low Scale
• Indeterminate

11. © 2019 M oogsoft. A ll rights reserved.
Typical
AIOps
Use Cases
Time
Topology
Entropy
TextABC

12. © 2019 M oogsoft. A ll rights reserved.
Collaboration
RESOLUTION
Learning
Collaboration
Situation
Similarity
Reduce MTTR
through
collaboration
Inference
SITUATIONS
CAUSALITY
Probable
Root-Cause
Vertex
Entropy
Reduce MTTD
by pinpointing
causality
Discovery
CORRELATION
ABC
Text
Topology
Time
CLUSTERS
Reduce workloads,
time to act and
business disruption
by proactively
detecting
actionable issues
TOPOLOGICAL
GRAPH
DATA
Relevance
10101001
01001001
00110001
0101
<f(x),x1> <g(x),x1>
INFORMATION
RICH DATA
STREAM
ENTROPY
Industrialize
change by
surfacing relevant
alerts from noise
Knowledge
Recycling
Capture experience,
make it
widely available
Automating All Dimensions of AIOps

13. © 2019 M oogsoft. A ll rights reserved.
Level 0/NOC Operators
• Improve efficiency by consolidating related alerts together
• Reduce catch-n-dispatch activities
AIOps Makes Your Teams
Faster, Smarter, and More Productive
Support SMEs & Developers
• Pass incident resolution knowledge to lower support tiers
• Collaborate across complex multi-disciplinary incidents
IT Operations Managers
• Delivery service-level state monitoring
• Improve efficiency and job satisfaction
• Identify and address repeating mundane work with run book
automation
Problem Managers
• Investigate and problem-solve for frequently repeating
P3-P5 incidents
IT Senior Management
• Achieve overall per-alert efforts reduction
• Re-purpose the savings towards business’s bottom line

14. © 2019 M oogsoft. A ll rights reserved.
An AIOps Leader
Innovation
• Technology pioneers
• +50 patents
• $100M in funding since 2012
(Goldman Sachs, Cisco,
Redpoint, Wing)
Experience
• Top tier team, 20+ yrs in
the domain
• Purpose built platform
• 7th generation solution
• Multiple products
Value
Transforming the
economics for +130
of the Global 2000,
across verticals

15. © 2019 M oogsoft. A ll rights reserved.
Entropy Time Language Topology
Vertex
Entropy
Algorithmic
Correlation
Engine
Feedback
Probable
Root Cause
Alert
Significance
Timestamp
Patterns
Text value
similarities
Network
proximity
patterns
Critical node
identification
Algorithms
application with
a deterministic
policy
Learnings from
Ops behavior
Root cause
identification
Our +50 Machine Learning Patents
Make AI Valuable and Consumable
ABC
Correlation
Noise
Reduction
Causality

16. Thank You!
Ph
Phil Tee, CEO | Phil@Moogsoft.com27 March 2019