methodology of enterprise application capacity planning by real life examples
DESCRIPTION
This presentation contains real life examples of enterprise applications capacity planning methodology described in details in author’s book: “Solving Enterprise Applications Performance Puzzles: Queuing Models to the Rescue”TRANSCRIPT
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Methodology of enterprise applications capacity
planning by real life examples
Leonid Grinshpan, Ph.D.
Consulting Technical Director, Oracle Corporation
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The views expressed in this
presentation are author’s own and
do not reflect the views of the
companies he had worked for neither
Oracle Corporation.
All brands and trademarks mentioned
are the property of their owners.
The presentation is based on
2
The presentation is based on
author’s book
“Solving Enterprise Applications
Performance Puzzles: Queuing
Models to the Rescue”
(available in bookstores and from Web
booksellers from March 2012)
http://www.amazon.com/Solving-
Enterprise-Applications-Performance-
Puzzles/dp/1118061578/ref=sr_1_1?ie=
UTF8&qid=1326134402&sr=8-1
Presentation’s goal
Learning by examples is the fastest way to master enterprise
applications capacity planning methodology
This presentation contains real life examples of enterprise applications
capacity planning methodology described in details in author’s book:
“Solving Enterprise Applications Performance Puzzles:
3
“Solving Enterprise Applications Performance Puzzles:
Queuing Models to the Rescue”
(available in bookstores and from Web booksellers from March 2012)
http://www.amazon.com/Solving-Enterprise-Applications-Performance-
Puzzles/dp/1118061578/ref=sr_1_1?ie=UTF8&qid=1326134402&sr=8-1
Input data for capacity planning
1. Workload characterization
� List of business transactions
� Number of users per each business transaction
� Per each transaction an anticipated number of transactions per user
per hour (transaction rate).
� Per each transaction its response time expected by the application
users.
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Example of workload characterization
Input data for capacity planning
2. Hosting platform
� Hardware architecture (connections
among servers and numbers
of servers on each tier)
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Example of hardware architecture
� Distribution of application’s software components among servers
(software components hosted by each server)
� Specification of each server (number of cores and CPUs, clock
speed, RAM size etc)
� Operating system (Windows, LINUX, etc)
Input data for capacity planning
3. Transactions profiles
� Profile of each business transaction
Transaction profile is comprised of the time intervals a transaction has spent in
system servers it has visited when application was serving only single request
Example of transactions profiles
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Input data for capacity planning
4. What-if scenarios
Each modeling estimate is generated for a specified set of input data that includes:
– Workload.
– Profile of each transaction.
– Hardware architecture, specification of servers (number of servers, number of
CPUs on each server, server types).
– Distribution (hosting) of application’s components among servers.
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Changing any of the above represents new what-if scenario.
Mapping system into model
System
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System’s model
Solving model
�Author usesTeamQuest software to solve models http://teamquest.com/
� It is possible to solve models using open source software packages. One
of them is Java Modeling Tools (JMT); it is developed by Politecnico di
Milano and can be downloaded from http://jmt.sourceforge.net/. A few
following slides demonstrate its basic functionality.
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Solving model using opens source package
Workload definition
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Solving model using opens source package
Specification of hardware servers
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Solving model using opens source package
Specification of transaction profiles
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Solving model using opens source package
Modeling results (utilization of servers and transaction times)
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Model deliverables
DELIVERABLES
� Average transaction response time for each transaction
� Utilization of each hardware server
Transaction time (seconds) Utilization of system servers (%)
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Common what-if scenarios
1. Changing hardware platform
2. Analysis of different operating systems
3. Workload variations
4. Assessment of impact of network and remote users
5. Changing number of cores and CPUs
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5. Changing number of cores and CPUs
6. Server farms
7. Redistribution of application’s software components
Case study 1. Customer: Financial institution. Application: Financial reporting
Workload characterization
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Case study 1. Customer: Financial institution. Application: Financial reporting
Hosting platform
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Case study 1. Customer: Financial institution. Application: Financial reporting
Transactions profiles
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Case study 1. Customer: Financial institution. Application: Financial reporting
What-if scenarios
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Case study 1. Customer: Financial institution. Application: Financial reporting
Project recommendations
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Case study 2. Customer: Pharmaceutical corporation. Applications: Corporate
Planning and Budgeting, Financial reporting
Workload characterizationCustomer requested to provide capacity estimates for nine workloads
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Case study 2. Customer: Pharmaceutical corporation. Applications: Corporate
Planning and Budgeting, Financial reporting
Hosting platform
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Case study 2. Customer: Pharmaceutical corporation. Applications: Corporate
Planning and Budgeting, Financial reporting
Transactions profiles
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Case study 2. Customer: Pharmaceutical corporation. Applications: Corporate
Planning and Budgeting, Financial reporting
What-if scenarios
Customer requested to provide capacity estimates for five hardware platforms and nine workloads
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Case study 2. Customer: Pharmaceutical corporation. Applications: Corporate
Planning and Budgeting, Financial reporting
Analysis of modeling results
30 what-if scenarios evaluated, this is an example of one scenario modeling estimates and conclusions
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Case study 2. Customer: Pharmaceutical corporation. Applications: Corporate
Planning and Budgeting, Financial reporting
Project recommendations
Recommended hardware specifications for different workloads
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Example 1. Customer: Major credit card company. Applications: Financial
consolidation, Financial reporting
Workload characterizationWorkload is generated by the users located on different continents
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Example 2. Customer: Major credit card company. Applications: Financial
consolidation, Financial reporting
Analysis of modeling resultsComparison of transaction times for two hardware platforms
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Example 3. Customer: Major credit card company. Applications: Financial
consolidation, Financial reporting
Analysis of modeling resultsComparison of server utilizations for four hardware platforms
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Example 4. Customer: Medical equipment manufacturer. Applications: Financial
consolidation, Planning, Financial reporting
Analysis of modeling resultsIdentification of “hokey stick” effect
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For short transactions system exhibits classical hockey stick effect
because transaction time starts growing exponentially after number of users exceeds 200 (
polynomial black line on chart)
Contact author
Want to know more about
enterprise applications capacity planning?
Contact Leonid Grinshpan at [email protected]
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