Marrying Service Management with Big Data Approaches

Marrying Service Management
with Service Delivery
“Big Data” Approaches
David Wagner
TeamQuest Advocate

TeamQuest and the TeamQuest logo are registered trademarks in the US, EU and elsewhere.
All other trademarks and service marks are the property of their respective owners.
Copyright © 2012 TeamQuest Corporation. All Rights Reserved.

Agenda
• The TeamQuest Why?
• Big Data: conceptual overview
• 2013 Capacity Management 101:
– Goals
– History
– Obstacles
• TeamQuest Approach (flavored by “big data”)
– Concepts
– Values

Why does TeamQuest Exist?

• At TeamQuest, we believe that having and using the
right amount of resources at all times is a societal
imperative.
– Anything less is failure
– Anything more is wasteful
• 20+ years sole focus
– ensuring our customers can continuously and
automatically perform at their utmost level of efficiency
– ensuring business service performance, conserving scarce
resources, saving money and improving productivity


What this Presentation is… and is not!

• IS:
– IT Optimization
– Marrying non-traditional disciplines to Capacity
Management
– Applying Big data approaches to Capacity Management
• Faster and larger value
• More scalable
– New ways to think about optimization
• Not just technology anymore
• Is NOT!
– A Primer on “Big Data”
– Hadoop ecosystem deep dive, etc…

Big Data at 50,000 feet…

• Big Data is about data  actionable information
– Plethora of existing sources
• Technology
• Business
• Service
– Learning new insights from “old” data
– Key is Analytics
• Deep
• Across
• But… Capacity Management?


Technology Approaches

• Data Access and Aggregation
– Build huge “data marts” (aka: Data Warehousing)
– Integrate with multiple different data sources
• Technology (e.g. Server, Network, Storage, etc.)
• Service (Catalog, Metrics, Tickets, etc.)
• Business (KPIs, Plans, Transactions, etc.)
• Implement Analytics against/across
– Flexible and adaptive
– Turn data within, into actionable information across
• But… Capacity Management???


2013 Capacity Management 101 - History

• Answering “what if” questions…
– Change in technology, demand, etc… impact?
– Focus on Optimizing Server Cost versus Performance
• Extremely Technology-centric
– Servers, Mainframes
– Occasionally Storage or Network – in isolation
• Big Value and Return, but also effort
– Highly trained staff
– Requires building a central, long term repository (CMIS)
– Scalability of Staff, Tools, …, Politics!

New Considerations

• Maintain traditional value, and add
– Optimize more resources
– Amplify
– Accelerate
• Increase Business relevance
– Valuable predictive analytics in business and service
context
– Optimize Efficiency
• Virtualization and Cloud Scale to everything
– Many to many inter-relationships; Capacity critical


How: Leverage existing ITIL domain solutions
• Service Strategy
– Financial management
• Service Design
– Service Level and Availability management
• Service Transition
– Asset, Change and Configuration Management
• Service Operations
– Service Desk
– Application and IT operations
– Event, Incident, Problem
• Or, in simpler terms…
– Integrate Capacity across ITIL V2: Service Support and Service Delivery!


How…

• Integrate and Analyze across multiple sources/tools
– Technology (e.g. Server, Network, Storage, etc.)
– Service (Catalog, Metrics, Tickets, etc.)
– Business (KPIs, Plans, Transactions, etc.)
• Single pane of “Analytic Glass”
– Ability to tie, correlate, and operate across
• Tear down that wall!
– Don’t force reinvention or duplication: technology or
process!
– Flexible and adaptive
– Turn data within, into actionable information across

How: Obstacles and another approach…

• Data Access and Aggregation
– Building huge “data marts” (fka: Data Warehousing)
• Complexity = (data ETL) x (# sources) x (maintenance effort)
• Compliance: Data duplication, privacy, audit, etc…
• Costly and time consuming
• Implementing Analytics against/across
– General purpose BI Analytics for Capacity?
– Traditional Performance/Capacity for General Purpose?
• “Big Data” + ITIL = Optimized Capacity
Management?
– Federation!

New Approach
• Concept: True Federation
– Influenced by Big data
– Take data from wherever it already exists
• Leave it there!
– Automation of “on demand” analysis and
exception-reporting across data sets
• Single-pane-of-glass capacity management
– Standardized analysis and reporting
– Business, service, and technology data

Some Examples
• Response time versus performance
• Business, Service, Asset and Power
• Tickets and performance/capacity
• Spanning pure technology silo challenges


Correlate Utilization with Response Time

Response Time Correlated with Utilization
(from HP BSM, fka: Mercury BAC) (from BMC BCO)

Value
14


Asset Financials

• Standalone, Financials are interesting
– Sourced Hyperion
• mapped to performance and capacity…
– Response time
– Business Transactions
15
– Power consumption

Business, Service, Asset and Power together!

16


Service Desk and Capacity

Copyright © 2012 TeamQuest Corporation. All Rights Reserved. 17

Virtual Machines to and from
Storage…

Copyright © 2012 TeamQuest Corporation. All Rights Reserved. 18

TPI is the best KPI

Application A Here’s the
spans multiple system-level TPIs
VMs… one for systems
queuing badly… hosting
Application a (1
host, 3 VMs)

Auto-generate linkable
dashboard – Application
Workload vs. System
duringworst queuing


TPI is the best KPI

Linked to details
on the queuing
workload… here: With a total
And breakdown, IO or
Total VM0 picture queuing
CPU? There’s IO queuing
breakdown…
at “peak bad” timeframe!


TPI is the best KPI
Auto-generate No corresponding
analysis of throughput spike!
System Devices Time to look at
and their queuing Storage/Array
side…
Follow the device
in question…


If or when there is
storage-centric RT data – Back end response time
analyze it! 10 mSec isn’t is pretty normal…
good…

TPI is the best KPI
Auto-generate
Volume related
analysis

Nothing stands out… if analyst
wants different views they can be
launched: in time, device, volume,
system, file-system and system,
application and workload context!


TPI is the best KPI
Same thing with
Link (or any other)
Analysis


TPI is the best KPI
Back-end analysis
of
Controller/drives

Always in context
of our Application
on that system, at
that time


Combined Views: Mapping VM – IO
Contention

Storage-sourced Server / Application-
sourced


Additional Considerations
• Federation maps virtually all sources… but not all
– Low effort
– Rapid to value
• Analytics must work
– One/more sources
– Modularly, combinatorially
• You will want rule-based automation
– Flexible, easy to modify
– Only way to “human scale”
• Pick “low hanging fruit” for first project(s)
– Build from your wins


Department of Shameless Commerce
• Consider Attending today’s afternoon session:
– “Optimizing IT Costs and Services with Big Data
(Little Effort!) – Case Studies
– 4:35 – 5:35: Track 12, Session 1012, DaVinci 3
• Expands on these concepts and presents
comprehensive case study


TeamQuest delivers value
• 25% reduction in IT budget in 3 years
(a large insurance company)
• 94% reduction in app slowdowns in 4 months
(a large healthcare organization)
• $5M saved by avoiding new hardware
purchases
(a large insurance company)
• $3 million saved by delaying IT procurement
(a large UK financial services company)


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