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IT Service Management for Hybrid Clouds

© 2017 IBM Corporation
IBM Application Performance Management2
IBM Predictive Insights3
IBM Event Management4
Introduction1
IBM Log Analysis5

By 2020, 95 percent of the top 100
enterprise software companies will have
incorporated one or more cognitive
technologies2
80% of CxOs are experimenting with
different business models or thinking
of doing so1
1. Redefining Boundaries, Insights from the Global C-suite Study, IBM Institute for Business Value, 2015
2. Cognitive technologies enhance enterprise software, Deloitte, 2016

2. Hybrid Integration
to unlock existing
data and applications
3. DevOps Productivity
to develop, experiment
and iterate at speed
1. Choice with Consistency
to put the right workload in
the right place
4. Powerful, Accessible
Data & Analytics
5. Cognitive Solutions
to build understanding and
learning into decisions and
interactions
to extract deeper insight
Five Guiding Principles
2

“Bi-Modal” IT requires a new management approach
Traditional Mode Agile Mode
Some, big IT projects Many, small
2-3 years Time to go live 2-3 months
Lower Change rate Higher
Centralized Governance Decentralized
Cloud-ready, on-prem Tools Cloud-Native
ITIL, CMMI Processes DevOps, Lean
Systems of Record
Operational Excellence
Systems of Engagement
Transformation & Differentiation
Hybrid Apps
Agile ManagementTraditional Management Hybrid Ops
Sources: The agile CIO: Mastering digital disruption. http://blog.kpmg.ch/the-agile-cio-mastering-digital-disruption/

Slowdowns and outages impact customers, revenue, and reputation
Application Performance Management can keep your apps up and protect your
revenue and reputation
E-commerce Websites
One of the largest e-commerce websites in the world experienced twelve
outages in a year, impacting their customers and reputation (link)
Financial and customer-impacting applications
Trading halted for half a day on the biggest US exchange for financial
options following an outage caused by software problems (link)
New technology and mobile applications
Customers poured out their wrath via social media after the largest video
streaming company had an over 20-hour outage on Christmas Eve (link)
Application Performance Management
Applications are critical to today's businesses

Application Performance Management for the Hybrid Cloud
What are the challenges for the Operations Teams?
Why aren’t operations teams preventative today?
●
Too much data to analyse manually
●
Existing analytic techniques, such as standard thresholds, are not up to the task
●
They cannot detect problems while they are emerging (before business impact)
●
Set threshold too high, insufficient warning before total failure.
●
Set threshold too low, too much noise, everything is ignored
●
Not all required system parameters are monitored
If there is no ‘early detection’ before the outage, operations teams can only react while outage is already in effect and already losing money...

Application Performance Management
Sense the Business Activity and Commonly Used Resources
Discipline Outcome (Event Management)
● Sense the User Experience (End-To-End)
● At the earliest stage possible
● With small footprint
● Monitor Related Resources
● OS
● Databases, Middle-ware, …
→ Detect system outages and SOS situations
● Threshold based
● Distinct value correlation
Prediction
●
Continuous Performance Data Gathering
●
From Monitoring
●
From Event Systems
●
Continuous Log Gathering
●
System Logs
●
Application Logs
→Anomaly Detection
●
Learn Common Behavior
●
Uncover unexpected Relationship
● Detect threads before end users complain
● Earn re-action time to fix problem before users are affected
● Detect long-term threshold violations as well as unexpected behaviour
Monitoring
Log Analysis
● Help analyse given situation
● Search large amount of structured and unstructured data
● Find quick solution with guided help assessments
● Visualize the distribution of events and messages
● Close the gap between silos – consolidate
● Expert advise based on Artificial Intelligence

Application Performance Management V8 architecture

IBM Application Performance Management
Consistent Portfolio across Delivery Models
Find performance bottlenecks in
application code
Find performance bottlenecks in
application code
IBM APM Advanced
End user experience, end-to-end transaction tracking and
application resource monitoring
IBM APM Advanced
End user experience, end-to-end transaction tracking and
application resource monitoring
IBM APM Base
Application-Aware Infrastructure Monitoring for huge
array of infrastructure & resources
IBM APM Base
Application-Aware Infrastructure Monitoring for huge
array of infrastructure & resources
Single Platform available as SaaS, On-Premise, HybridSingle Platform available as SaaS, On-Premise, Hybrid
Bluemix
Monitoring
Bluemix
Monitoring

Web-Tier Middleware TierWeb-Tier Backend-Tier
Traditional Data Center
IBM Application Performance Management in the Cloud
Extending the Monitoring Scope
Cloud Services
User
experience

IBM APM is where your applications are
IT Ops
DevOps
Application
Application
Application
VM
VM
VM
VMVM
Container
On-Premise Data Center
Agent
Agent
Applications
Public Clouds
AWS, Azure, etc
Services
Applications
Private Clouds
OpenStack, VMWare, etc
Applications
Agent
Agent
LoB
Optimized Experience for Persona
Event
Management
IBM APM Solution

IBM Monitoring V8 Coverage
Custom Agents
Using Agent Builder
Find additional info for selected areas by clicking on the icon in focus (not all yet defined)

Demo @ IBM Marketplace
http://ibm.co/APMWASwebcast

Understand End-User Transactions
Integrate monitoring data
from IBM Bluemix

End-User Request
Overview
Dive Into End-User Transactions
Request Topology
Path
Request Sequense
Dashboard

Anomaly Detection with Predictive Insights

Large retail pharmacy wants on prem for
most of their regular applications but SaaS
only for specific seasonal applications
Challenges
•Flu application is highly seasonal
•Customer runs this application in cloud and wants a service to
manage it
•Customer wants to use on prem event management and
ticketing capabilities
•Customer will use existing on-prem systems of record systems
with local monitoring but wants an integrated view across from
cloud
Benefits
•Leverage existing assets/processes and institutional capabilities
in rolling out APM
•Do not create “islands” of monitoring
•Adopt “best suited” or hybrid management structure

Product Highlights
●
Behavioural learning solution for quick time-to-value
●
Understands how your IT & network infrastructure is inter-
related from a holistic viewpoint
●
Behavioural learning solution for quick time-to-value
●
Utilizes real-time, streaming analytics to provide early
warning alerts for abnormal issues
●
Leverage existing performance & monitoring management
solutions
●
Consolidates and unifies performance data
●
Works with IBM & non-IBM management solutions
IBM Operations Analytics – Predictive Insights

What it is…
Predict Outages Before They Occur
Predict
Challenge: Reacting to outages is not enough – to ensure your mission critical applications are always available
24X7, you must prevent outages by detecting problems before they become service impacting
IBM Operations Analytics
- Predictive Insights
Proactive Outage Avoidance
New next-generation behavioural learning and
predictive analytic solution.
Discovers how your IT & Network infrastructure is
related from a holistic viewpoint.
Maximizes early detection of problems
Identifies problems before you know to look for
them, catching them the first time they happen.
Fast value and strong return on investment

Adaptable solution for continuous change
Agile solution that supports dynamic infrastructures such as cloud, which
are constantly in flux
Heterogeneous support
Flexible solution that works easily with multiple platforms and multiple
performance management vendor solutions
Leverage existing investments
No rip & replace, utilize existing performance management solution
Key Success Factors of a predictive solution

Analytics for detecting and avoiding service disruption
Uses advanced Watson analytics single and multi-metric algorithms
Models metric relationships across domains and heterogeneous environments
Leverages IBM Big Data & Smart Planet technology
Embeds InfoSphere Streams, IBM’s unique streaming analytic engine
Enables ultra-high scalability commodity server computing clusters and
large algorithm sizes to maximize machine intelligence value
Quickly integrate to any monitoring source using a large library of out-of-
the-box connectors
Works in non-IBM environments, as well as integrating tightly with IBM
suite.
Leveraging IBM Technologies

Correlation of Multiple Metrics
Statistical models can discover mathematical relationships between metrics
Core Banking
Application
Core Banking
Application
z/OSz/OS
ESBESB
AIXAIX
Java / WASJava / WAS
RHELRHEL
OracleOracle
WindowsWindows
ApplicationApplication
Internet Banking
GG
II
BB
DD
CC
EE
FF
HH
AA
Internet Banking

Goal: Automatically learn normal mathematical relationships between metrics
Web Response TimeWRT BadWRT GoodUser RequestsTimeWeb Response
Time
Anomaly EventBusiness ImpactedEarly Warning
•Learns ‘Web Response Time’ has a normal causal relationship with ‘User Requests’ - WRT gets slower as user load gets higher.
•If this healthy historical relationship breaks down, say due to a memory leak, an anomaly is raised immediately
•The problem is detected even while WRT service is “good”
==>> Emerging problems can be detected even while service levels are good in absolute terms
Core Banking
Application
Core Banking
Application
z/OSz/OS
ESBESB
AIXAIX
Java / WASJava / WAS
RHELRHEL
OracleOracle
WindowsWindows
ApplicationApplication
Internet Banking
BM Operations Analytics – Predictive Insights
Usage Scenario

Consolidated Communications avoids network
outages and improves customer service
Need
• Monitoring a customer base of 250k access lines, 125k Internet, and 30k
video is a challenge
• Managing manual thresholds within this networking environment is a
nightmare
Benefits
•Using Predictive Insights, behavioral learning techniques generate alerts
automatically when something is not normal
•Enable earlier detection and insight into issues not detected by existing
monitoring systems
•Easily obtain impact analysis into how the network copes with various
failure conditions
“IBM helped detect 100 percent of the major incidents
that occurred, including silent failures, and helped us
eliminate manual thresholds, which will result in a cost
avoidance of $300K USD annually”
- Chris Smith, Director
Tools and Automation
Consolidated Communications Holdings, Inc.

IBM Netcool Operations Insights
Event Reduction with Operations Analytics
OPEX
Customer Satisfaction &
Operational Efficiency
# Events
>10
>100
>1k
>10k
Degree of Advanced Event
Data Processing
Implemented
Event Collection
Input
Event Filtering &
Suppression
Event De-duplication
State-based Correlation
Automated Resolution
Event Analytics
Seasonal Event Analytics
Identify regularly occurring events sorted by confidence level
and frequency
Event Search Analytics
Enabling faster problem identification, isolation and
resolution
Related Events Grouping
Out of the box domain expertise for known event relationships
Related Events Analytics
Leverage machine learning to identify groups of events that
always occur together in real time

IBM Netcool Operations Insight
Event Handling Solution
Event
Enrichment

IBM Netcool Operations Insight
Guided Demo

IBM’s IT Operations Analytics Suite
Application Performance Management Event Management
Applications | Systems | Workloads | Wireless | Network | Voice | Security | Mainframe | Sorage | Assets
Business
Success
Scopes
IBM
Big Data
Plattform
System & Log Monitoring
Effiziente ProblemlösungPrediction
RaveSPSS InfoSphere BigInsights WatsonStreams Cloud Insights
IBM Operations Analytics
Probleme erkennen bevor sie
entstehen
Predict
Große Datenmengen schnell
und intelligent durchsuchen
Search
Solutions from
IBM and Third
Party
Operations
environment

IBM Operations Analytics – Log Analysis
Challenge
Expert Advice
Any competitor can isolate problems. IBM helps clients quickly resolve them.
Breadth of Searchable Data
Search across all of your IT operational data to quickly resolve issues
Mainframe Support
Search System z (zLinux & zOS) application logs in addition to all your other
data
Big Data Platform
Built on top of the IBM Big Data Platform; industry-leading text
analytics included
Challenge: To diagnose service problems in applications and the infrastructure supporting them involves quickly analyzing
incredible amounts of both structured and unstructured data

China Merchants Bank selects IBM Operations AnalyticsChina Merchants Bank selects IBM Operations Analytics
3535
The Need
• Suffering service outages on front-end transactions – each outage was costing
the business significantly
• Needed a robust, scalable analytics platform to manage events from these
transactions
Solution
• IBM Operations Analytics - Log Analysis displaced Splunk to collect and analyze
event and system resource data to reduce mean-time to repair
• IBM Operations Analytics - Predictive Insights for self learning behavior on
numerous KPIs to prevent outages from occurring
How we won
• Led with a solution approach for all operations analytics capabilities
• Deep understanding of the client’s goals to cater a specific solution
Search & Predict

IBM IT Service Management
Additional Info Material
● IBM Systems Blog: IT System Management in the Cloud
● Marketplace – NOI Demo
● IBM Cloud Solutions
● E-Book “Hybrid Cloud for Dummies”
● IBM Bluemix Availability Monitoring
● “Growing up hybrid – Accelerating transformation” – An IBM Point of view

Questions
Detlef Wolf
+49 151 11750274
Senior Consultant
detlef.wolf@de.ibm.com

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