capacity planning: engineering infrastructure performance
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Capacity Planning:Engineering Infrastructure
Performance
Capacity Planning:Engineering Infrastructure
Performance
2© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Business and Technology Scenario
�Data center consolidation is forcing IT organizations to evaluate overall performance
�Budget pressures are forcing more prudent resource utilization and infrastructure purchasing
�Overall discipline in IT infrastructure planning has been dismal and needs to improve
� IT organizations must minimize capital expenditures while preserving good service
�Growing operational process maturity requires better accountability and information sharing across processes
3© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Critical Issues
�Fine-tune IT services by optimizing infrastructure performance
�Address the challenges of capacity planning in distributed environments
�Technology solutions for capacity planning
4© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Optimizing Infrastructure Performance
�2003 IAM study results on capacity planning
�Business drivers for capacity planning
�Preemptive performance planning approaches
� Engineering discipline yields superior quality and efficiency
Balance Capacity for Optimum Performance and Cost Effectiveness
Optimizing Performance
Balance performance and cost through a structured capacity planning process
5© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
2003 IAM Study Results
Optimizing Performance
Budget for capacity planning technology and staff to address changing business demands
Capacity planning is the #1 critical issue for large enterprises — capacity planning spending plans:
Spending increase
Spending cut
16.121.2
29.1
18.625.6
20.8 21.829.3
12.1 15.0 13.1 12.2 13.7 13.6 13.78.0
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Small Midsize Large Executive IT LOB North
America
Rest of
World
Decreased
Stayed the same
Increased
Median
6© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Capacity Planning Business Drivers
�Costs need to be reduced
�Operational expenses
�Capital expenses
�Resource utilization is low (data center higher)
�Windows average is 10%-15%
�Unix average is 20%
�Consolidation
�Growing operational maturity spawns strong capacity planning
� Excess capacity can no longer mask demands
Optimizing Performance
Avoid sloppiness in provisioning of excess capacity in favor of engineering discipline
7© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Quality Through Engineering Discipline
�Complex systems followstructured engineeringpreceding production
�Autos, aerospace, semiconductors
�IT systems are complex and must be treated likewise
� Engineering discipline prevents costly redesign
�Capacity planning helps fulfill this discipline
Optimizing Performance
Feedback in Engineered Development Flows
Engineering 101: Employ several evaluation approaches; no one method fulfills all needs
Design Model Build Test Deploy
Developmental Progress
Improvement Iterations
8© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Performance Planning Approaches
� Incident analysis
�Performance triggers
�Long-term patterns
�Performance trending
�Be careful — results can be misleading
�Modeling/simulation
� Evaluate both peak periods and averages
Optimizing Performance
Combine people, processes, and technology to address distributed system complexity
Classify Utilization for Consolidation Candidates
811
27
2
0
10
20
30
40
50
60
0-20 21-40 41-60 61-80 81-100
Percent Sustained Utilization
Number of Servers 52
9© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Capacity Planning in Distributed Systems
�Distributed introduces new:
�Complexity
�Operational maturity
�Process development
�Server consolidation
� End-to-end efforts
Leverage lessons learned from decades of legacy capacity planning
Distributed Capacity Planning
Process Integration
Configuration
Management
Capacity
Management
Change
Management
Infrastructure
Development
Application
Development
Service-Level
Management
Performance
Management
Business
Relationship
Management
Business
Requirements
10© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Borrow Structure From the Mainframe
�Mainframe capacity planning is mature but inflexible
� Far more distributed system complexity
�Mainframe is more tightly contained and homogenous
�Mainframe relationships are more static
�Legacy system capacity provisioning is mature
�Distributed systems will get more complex
� Legacy operations exhibit strong discipline
�Distributed operations need to become more conservative
Distributed Capacity Planning
Build on established mainframe processes for distributed capacity planning
11© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Process Development
�All three inputsare necessary
�Many CPeffortsmiss business requirements
�Business requirements drive capacityassessments
�Cycles indefinitely
Distributed Capacity Planning
Optimize capacity and performance with a disciplined, consistent engineering process
Review Results
Capacity
Assessment
Business
Requirements
Application
Development
Application
Behaviors
Infrastructure
Characteristics
Capacity Is
Adequate
Capacity Is
Inadequate
Issue?Unrealistic
Requirements Application
Deficiencies
Infrastructure
Deficiencies
Business
Relationship
Management
Infrastructure
Development
12© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Server Consolidation
�Provides optimization, but watch usage patterns
�Geographic and LOB user communities
�Capacity of network and other supporting factors
�Application protocols
�Understand user impact
�Blind consolidation can cause major business disruption
Distributed Capacity Planning
Consider all infrastructure, applications, and users when embarking on consolidation
Workload Balancing via Consolidated Loads
0
5
10
15
20
25
30
35
40
0
5
10
15
20
25
30
35
40
0
5
10
15
20
25
30
35
0
5
10
15
20
25
30
35
13© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
End-to-End Capacity Planning
�A focus on technology silos is the norm
�A myopic history of silo-based operations
�Technology limitations
� End-to-end is the desired state
�Server, storage, and network are merging
�Endpoint inclusion will follow
�Adaptive organization and Web services will change everything
�Highly dynamic relationships
�Capacity on demand will automate some tasks
Distributed Capacity Planning
Optimize silos until capacity planning technology solutions evolve for end-to-end
14© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Capacity Planning Technology Solutions
�Understand the “magic” within planning tools
�Application-oriented infrastructure tuning
�Modeling and simulation
�Vendor landscape
Capacity Planning Technology
Capacity Measurement Accuracy Is Imperative
Acquire sufficient platforms for the intensive mathematical processing demands of tools
15© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
The Inner Workings of Planning Tools
�Statistical analysis
�Based on advanced performance management
�New innovations emerging from astrophysics, quantum mechanics, and bioinformatics
�Queuing theory models data flows through a system
�Discrete simulation is slow, but extremely precise
Capacity Planning Technology
Gain Visibility Into Internal Technology Functions
Blend capacity planning skills in operations research, infrastructure, and applications
16© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Application Orientation
� Infrastructure decisions serve applications
�Interactions within and across infrastructure impact results
�Application patterns must be understood by CP tools
�Complex applications require the best tools
� Limit efforts to genuine business requirements
Capacity Planning Technology
Applications Are the Tangible Service to Users
Build software models of application behaviors and infrastructure attributes
17© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Modeling
�Software simulation and emulation of inputs
�Difficult, but high return
�Getting easier
�Accuracy is critical
�Infrastructure characteristics
�Applicationbehaviors
�Business requirements
Capacity Planning Technology
Simplified Modeling Process
Seek capacity planning vendors with some form of modeling and simulation
Business
Requirements
Model
Modeling
Tool
Application
Behavioral
Model
Infrastructure
Characteristics
Model
Review
Results
Satisfied?
Modify
Infrastructure
Model
Modify
Application
Model
Modify
Requirements
Model
Change
Management
Yes No
Infrastructure
App
Reqmts
18© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Vendor Landscape
�Most vendors are small
�BMC is the only serious large vendor
�Expect other large vendors to enter
�Silo-centric offerings will become end-to-end
�M&A activity will increase
Capacity Planning Technology
Current Vendor Focus on Capacity Planning
Pressure vendors for end-to-end tools and expect more presence from familiar vendors
TeamQuest
HyPerformix
OPNET
BEZ Systems
BMC Software
Compuware
Server Network Database
19© 2003 META Group, Inc., Stamford, CT-USA, +1 (203) 973-6700, metagroup.com
Capacity Planning: Engineering Infrastructure Performance
�Optimize infrastructure performance
�Follow proven structured engineering processes
�Understand changing business requirements
� Tackle the complexity of distributed systems
�Leverage decades of mainframe experience
�Consider all hardware and software components for a complete end-to-end perspective
� Learn about technology details
�Develop expertise on the advanced methods used by capacity planning products
�Employ some level of modeling to combine actual infrastructure and application behaviors with realistic business requirements
TRANSFORMATION STEPS
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