<Insert Picture Here>

Introduction to enterprise applications capacity planning

using queuing models

Leonid Grinshpan, Ph.D.

Consulting Technical Director

2

Disclaimer

The following presentation is intended for information purposes

only, and may not be incorporated into any contract. It is not a

commitment to deliver any material, code, or functionality

All brands and trademarks mentioned are the property of their

owners.

3

Presentation’s goal

The presentation provides an introduction to enterprise

applications capacity planning using queuing models

Oracle’s Consulting uses presented methodology to estimate

hardware architecture and capacity of planned for

deployment enterprise applications for Oracle customers

4

Presentation’s structure

Methodology of enterprise applications capacity planning

Section 1

Do we really need modeling for capacity planning?

Section 2

Section 3

Solving models and evaluating what-if scenarios

5

Section 1

DO WE REALLY NEED MODELING FOR

CAPACITY PLANNING?

Can we just go by our gut feeling?

6

Quiz 1. Ticket reservation application

• A ticket reservation application is deployed on one server with 8

CPUs.

• System delivers 10 seconds response time and utilizes server’s CPU

at 50% under workload described in a table:

Q. How many more users an application can support without

response time degradation exceeding 10% ?

Transaction name Number of users Transaction rate

Ticket Reservation (TR) 250 6 per user per hour

7

Quiz 1. Ticket reservation application (cont)

8

Quiz 2. Ticket reservation and on-line payment

applications

• A ticket reservation application per Quiz 1 uses only 50% of 8 CPU

server capacity. Let’s deploy on the same server on-line payment

application.

• Workload for both applications is described in a table:

Q. How many on-line payment users system can support

delivering on-line payment transaction time 8 seconds?

Transaction name Number of users Transaction rate

Ticket Reservation (TR) 250 6 per user per hour

On-line payment (OLP) ???? 10 per user per hour

9

Quiz 2. Ticket reservation and on-line payment

applications (cont)

10

Section 2

METHODOLOGY OF ENTERPRISE

APPLICATIONS CAPACITY PLANNING

11

Generalized enterprise application architecture

12

What is a main application performance indicator

from the users perspective?

13

Mapping application into queuing model

Hardware server representation

Total time in node = time in waiting queue + time in processing unit

14

Mapping application into queuing model (cont 2)

15

Mapping application into queuing model (cont 3)

The relationships between the components of a real system and the

components of its model

Component of application Matching object in

queuing model

Users Node “Users”

Web server Node “Web server”

Application and Database

server

Node “A&D server”

Requests from users Cars

16

Transaction response time and transaction profile

Transaction time is a time spent in “cloud”

17

Transaction response time and transaction profile (cont 2)

18

Input data for capacity planning

List of business transactions

Number of users per each business transaction

Per each transaction a number of transactions per user per hour

(transaction rate).

Transaction name Number of users Transaction rate

Report ABC 20 12

Business Rule X 10 8

Consolidation Y 5 3

1. Workload characterization

19

Input data for capacity planning (cont 2)

2. Transactions profiles

Transaction profile is comprised of the time intervals a transaction has spent in

system servers it has visited when application was serving only that single

transaction

Transaction name Service demand (seconds)

Web server A&D server

Report ABC 0.5 0.5

Business Rule X 0.5 2.5

Consolidation Y 0.5 9.5

20

Building and solving models using open source

Java Modeling Tool

Open source software package Java Modeling Tools (JMT) can be downloaded from

http://jmt.sourceforge.net/.

21

Definition of nodes in JMT

Node “Users” has as many processing units as total number of users

Node “Web server” has one processing unit

Node “A&D server” has one processing unit

22

Definition of workload and transaction profile in

JMT

23

Solving JMT model

Report ABC time is 0.52 + 0.57 = 1.09 sec

Business Rule X time is 0.52 + 2.84 = 3.36 sec

Consolidation Y time is 0.52 + 10.78 = 11.30 sec

24

Solving JMT model (cont 2)

Total utilization of Web server is 4.6% and A&D server is 12.7%

Transaction Business Rule X utilizes Web server at 1.1% and A&D server at 5.5%

Transaction Consolidation Y utilizes Web server at 0.2% and A&D server at 3.9%

25

What is needed for enterprise application capacity

planning ?

1. Description of system architecture (hardware servers , hosted software components)

2. Input data:

Workload characterization (flow of business transactions from system users)

Transaction profiles (service demand from single transaction)

3. Model solver

26

Section 3

SOLVING MODELS AND EVALUATING

WHAT-IF SCENARIOS

27

Open source vs. commercial model solvers

Open source solvers

Limited functionality

Lack of extensive and up-to-day libraries of hardware platforms

Commercial solvers

Accept input data in a form of hardware and software specifications and benchmarks

Automate study of multiple what-if scenarios

Components of broader suites

Include collectors of input data that can be feed into model directly

28

Building and solving models using commercial

products

Transaction

name

Service demand (seconds)

Web server App server DB server

Report ABC 0.5 0.2 0.3

Business Rule X 0.5 1.0 1.5

Consolidation Y 0.5 7.0 2.5

Transaction profiles

Three-tier system

29

Building and solving models using commercial

products (cont 2)

Specification of hardware servers

Description of business transactions

30

Building and solving models using commercial

products (cont 3)

Transaction profiles

31

Building and solving models using commercial

products (cont 4)

Description of users

32

Building and solving models using commercial

products (cont 5)

MAIN MODEL DELIVERABLES

Average transaction response time for each transaction

Utilization of each hardware server

Transaction time (seconds) Utilization of system servers (%)

33

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

6. Server farms

7. Redistribution of application’s software components

34

Analysis of what-if scenarios

Faster Application server

All three servers are faster

35

Analysis of what-if scenarios (cont 2)

Transaction time (seconds)

Baseline system

Transaction time (seconds)

System with fast Application server

36

Analysis of what-if scenarios (cont 3)

Transaction time (seconds)

System with fast Application server

Transaction time (seconds)

System with three fast servers

37

Analysis of what-if scenarios (cont 3)

Utilization of system servers (%)

38

Example of an Oracle enterprise application

production deployment

DO WE REALLY NEED MODELING FOR CAPACITY

PLANNING?

Can we just go by our gut feeling?

39

To learn more about enterprise applications

capacity planning please check

author’s book

“Solving Enterprise Applications Performance

Puzzles: Queuing Models to the Rescue”

(available in bookstores and from Web

booksellers)

http://www.amazon.com/Solving-Enterprise-

Applications-Performance-

Puzzles/dp/1118061578/ref=sr_1_1?ie=UTF8&

qid=1326134402&sr=8-1

https://www.amazon.com/author/leonid.grinshpan

Contact Leonid Grinshpan at:

leonid.grinshpan@oracle.com