Enterprise Capacity Optimization - Capacity Management Over Everything

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Enterprise Capacity Optimization
Capacity Management over Everything
David Wagner
TeamQuest Advocate

• Today’s Capacity Management Problems
• Introducing a “Big Data” Approach
• Some Examples
Agenda

TODAY’S CAPACITY MANAGEMENT
PROBLEMS
3

• Basic Analytics
– Monitoring trends
– Siloed problem analysis
– Capacity planning focused on optimizing server cost versus performance
• Extremely technology-centric
– Servers, mainframes
– Occasionally storage or network – in isolation
• Big value and return, but also a big effort
– Requires highly trained staff
– Requires building a central, long-term repository (CMIS)
– Problems with scalability of staff and tools … Politics!
Capacity Management
Today’s Tradition

• Build on traditional value
– Optimize more resources
– Amplify
– Accelerate
• Increase business relevance
– Valuable predictive analytics in business and service context
– Optimize efficiency
• Virtualization and cloud  scale everything
– Many-to-many interrelationships; capacity critical
New Capacity Management Goals

Needed: Cross-enterprise Capacity
Management

“What is the health of the
infrastructure?”
“How efficiently are
we using the
infrastructure?”
“How many of those do
we have?”
“Changes compared to
the last reporting
period?”
“What’s the capacity
forecast?”
“What were the
exceptions?”
“What’s the risk
assessment?”
Questions You Need Answered

The Environment:
Continuously Changing

• Continuous consolidation and repurposing
– Least used (servers, virtual servers)
– Most power (use, cost)
– Most expensive (total OpEx)
– By application
– By specific point in time
• Financial Optimization
Virtualization Optimization

• Asset database
– Server CapEx, OpEx and licensing costs
• Service catalog
– Mapping of applications to servers/virtual servers
• Power consumption
– kWh per server
• Power cost
– Spreadsheet: cost per kWh over time
• Resource utilization
– By server, virtual server and application
Virtualization Optimization Data Sources

• 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 Service Management and Service Delivery!
– Integrate capacity with “everything”!
What Is Needed:
Leverage Existing Solutions and Data

• Build a huge “data mart” (i.e. PMDB)
• Complexity = (data ETL) x (# sources) x
(maintenance effort)
• Compliance: Data duplication, privacy, audit,
etc…
• Extremely costly and time-consuming
• Not Agile
• Lock in
A Sub-optimal Solution:
Data Warehousing

INTRODUCING A “BIG DATA”
APPROACH
A Better Solution
13

• Federate data, don’t centralize it
– Technology (e.g. server, network, storage, etc.)
– Service (catalog, metrics, tickets, etc.)
– Business (KPIs, plans, transactions, etc.)
• Automated analytics across all data sources
– Flexible and adaptive
– Turn data into actionable information
– Single-pane-of-glass capacity management
A Better Solution:
A “Big Data” Approach

The “Big Data” Approach:
Play Well with Others

SOME EXAMPLES
A “Big Data” Approach
16

• Response time versus performance
– Speed MTTR in service context
• Business, service, asset and power
– Continuous financial optimization
• Service tickets and performance/capacity
– Risk management
• Spanning pure technology silo challenges
– Improve availability and speed MTTR
Examples of
Analysis across Data Sources

18
Correlate Utilization with Response Time
Response Time
(from HP BSM, fka: Mercury BAC)
Correlated with Utilization
(from BMC BCO)
Value

Financial Optimization Report

Service Desk and Capacity

Servers AND Storage - Together

Servers AND Storage - Together
Huge
Value
for
VMware
Environments:
Cross the Chasm!

Servers AND Storage:
Automating High-value Analytics

• Large Insurance
– Saved $25M over 3 years – expense deferral while maintaining SLAs
– Improved resource utilization by 400%
• Large Finance
– Deferred massive, planned DataCenter build out by 18 months and
counting ($20M+
– Reduced Service Risks, lowered operational costs, expanded staff
scope/reach
• AAS Provider
– Sped MTTR for client-facing services by 400%
• Telecommunications
– Sped client-facing infrastructure MTTR, cost-reduction, right-sizing
28
Results

• Inventory processes
– Data collection
– Data storage
– Capacity Analysis/Reporting
• Inventory Business KPIs
• Identify “low hanging fruit”
– By Service
– By BU
– By Platform
– By Technology/silo
• Get started
29
Summary

• Powerful analytics
• Cloud scalability
• Fast ROI
• Cross-silo solution
TeamQuest Software & Services
IT Service Optimization
• Improved productivity
• Decreased costs
• Reliable service delivery
• Risk mitigation

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