Capacity Planning in
Client/Server Environments
Daniel A. Menascé
George Mason University
Fairfax, VA 22030
USA
menasce@cs.gmu.edu
© 1995 Daniel A. Menascé
Outline
 Part
I: Client/Server Systems
 Part II: Introduction to Capacity
Planning
 Part III: A Capacity Planning
Methodology for C/S Environments
 Part IV: Performance Prediction
Models for C/S Environments
© 1995 Daniel A. Menascé
Outline (continued)
 Part
V: Advanced Predictive Models of
C/S Systems
 Part VI: Case Study
 Bibliography
© 1995 Daniel A. Menascé
Part I:
Client/Server (C/S) Systems
© 1995 Daniel A. Menascé
Definitions and Basic Concepts
Client
 Server
 Work division between client and
server
 Client/Server communication

© 1995 Daniel A. Menascé
Definitions and basic concepts
DB server
...
LAN segment 1
R
router
FDDI ring
LAN segment 2
R
router
...
DB server
© 1995 Daniel A. Menascé
Definitions and basic concepts:
Client
Workstation with graphics and
processing capabilities.
 Graphical User Interface (GUI)
implemented at the client.
 Partial processing executed at the
client.

© 1995 Daniel A. Menascé
Definitions and basic concepts:
Server
Machine with much larger processing
and I/O capacity than the client.
 Serves the various requests from the
clients.
 Executes a significant portion of the
processing and I/O of the requests
generated at the client.

© 1995 Daniel A. Menascé
Work division between client and
server
Client
Server
GUI
Processing
Pre & Post
Process.
I/O
COMM.
COMM.
DB
communications network
© 1995 Daniel A. Menascé
Interaction between client and server
Remote Procedure Call (RPC)
client
pre-processing
DB server
execute_SQL(par1,par2,...)
server
processing
post-processing
result_SQL(...)
© 1995 Daniel A. Menascé
Part II:
Introduction to Capacity Planning
© 1995 Daniel A. Menascé
Migration to C/S example:
“downsizing” a claim processing application
DB server connected to several PCs
through an Ethernet LAN
 GUI application executing at the PCs
 LAN connected to the enterprise
mainframe through a T1 line
 DB server is updated every night.

© 1995 Daniel A. Menascé
Migration to C/S systems
mainframe based system
T1 line
mainframe
© 1995 Daniel A. Menascé
Migration to C/S
DB server based system
DB server
LAN
gateway
T1 line
mainframe
© 1995 Daniel A. Menascé
Migration to C/S:
some important questions
 How
many clients can be supported by
the DB server while maintaining a
response time below 2.5 sec?
 How long does it take to update the DB
every night?
© 1995 Daniel A. Menascé
Migration to C/S example:
measurements with a prototype

During 30 minutes (1,800 sec):
– 25% CPU utilization
– 30% disk utilization
– 800 transactions were executed
Each transaction used:
1,800 * 0.25 / 800 = 0.56 sec of CPU and
1,800 * 0.30 / 800 = 0.68 sec of disk.

© 1995 Daniel A. Menascé
Good News and Bad News
Good News: we know the CPU and I/O
service time of each transaction.
 Bad News: transactions at the DB server
compete for CPU and I/O  queues will
form at each device. We don’t know how
long each transaction waits in the queue
for the CPU and for the disk.

© 1995 Daniel A. Menascé
DB Server Model
arriving
transactions
departing
transactions
cpu
disk
DB server
© 1995 Daniel A. Menascé
CPU or I/O Times
?
service demand
= 0.56 seg
queue waiting
time
© 1995 Daniel A. Menascé
Capacity Planning Definition
Capacity Planning is the process of
predicting when the service levels will
be violated as a function of the workload
evolution, as well as the determination of
the most cost-effective way of delaying
system saturation.
© 1995 Daniel A. Menascé
C/S Migration Example:
desired results
response
time
(sec)
9
8
7
6
5
4
3
2
service
level
1
0
50
60
70
80
100
no. of client workstations
© 1995 Daniel A. Menascé
Part III:
A Capacity Planning Methodology
for Client/Server Environments
© 1995 Daniel A. Menascé
Capacity Planning Methodology for Client Server
Environments
Understanding the Environment
Developing a Cost Model
Workload Characterization
Workload
Model
Cost
Model
Validation and Calibration
Performance
Model
Workload Forecasting
Cost Prediction
Performance Prediction
Valid
Model
Cost/Performance Analysis
Configuration
Plan
Investment
Plan
Personnel
Plan
© 1995 Daniel A. Menascé
Capacity Planning Methodology for Client Server
Environments
Understanding the Environment
Developing a Cost Model
Workload Characterization
Workload
Model
Cost
Model
Validation and Calibration
Performance
Model
Workload Forecasting
Cost Prediction
Performance Prediction
Valid
Model
Cost/Performance Analysis
Configuration
Plan
Investment
Plan
Personnel
Plan
© 1995 Daniel A. Menascé
Understanding the Environment








Hardware and System Software
Network Connectivity Map
Network Protocols
Server Configurations
Types of Applications
Service Level Agreements
Support and Management Structure
Procurement Procedures
© 1995 Daniel A. Menascé
Example of Understanding the
Environment





5,000 PCs (386 e 486) running DOS and Windows
3.1 and 800 UNIX workstations.
IBM MVS mainframe.
80 LANs in 20 buildings connected by an FDDI
100 Mbps backbone.
50 Cisco routers.
Network technologies: FDDI, Ethernet, T1 links
and Internet.
© 1995 Daniel A. Menascé
Example of Understanding the
Environment (continued)




Protocols being routed: TCP/IP and Novell IPX.
Servers: 80% are 486 and Pentiums and 20% are
RISC workstations running UNIX.
Applications: office automation (e-mail,
spreadsheets, wordprocessing), access to DBs (SQL
servers) and resource sharing.
Future applications: teleconferencing, EDI, image
processing.
© 1995 Daniel A. Menascé
Capacity Planning Methodology for Client Server
Environments
Understanding the Environment
Developing a Cost Model
Workload Characterization
Workload
Model
Cost
Model
Validation and Calibration
Performance
Model
Workload Forecasting
Cost Prediction
Performance Prediction
Valid
Model
Cost/Performance Analysis
Configuration
Plan
Investment
Plan
Personnel
Plan
© 1995 Daniel A. Menascé
Workload Characterization


Process of partitioning the global workload into
subsets called workload components. Examples of
workload components:
– DB transactions,
– requests to a file server or,
– jobs with similar characteristics.
Workload components are composed of basic
components.
© 1995 Daniel A. Menascé
Workload Characterization: workload
components and basic components
Workload Component
Basic Component
e-mail
- send message
- receive message
- query
- update
- session establishment
- execution of remote function.
- session termination
Access to a DB server
Acesso to mainframe
© 1995 Daniel A. Menascé
Workload Characterization
Basic Component Parameters


Workload Intensity Parameters
– number of messages sent/hour
– number of query transactions/sec
Service Demand Parameters
– average message length
– average I/O time per query transaction.
© 1995 Daniel A. Menascé
Workload Characterization
Methodology
Identification of Workload Components
 Identification of Basic Components.
 Parameter Selection.
 Data Collection: benchmarks and ROTS
(Rules of Thumb) may be used.
 Workload partitioning: averaging and
clustering.

© 1995 Daniel A. Menascé
Workload Characterization
Data Collection Alternatives
- use “benchmarks”
and ROTs only
- use measurements,
“benchmarks” and
ROTs
- use measurements
only
Data Collection Facilities
None
Some
Detailed
© 1995 Daniel A. Menascé
Benchmarks
National Software Testing Laboratories
(NSTL): servers and applications.
 Transaction Processing Council (TPC)
 System Performance Evaluation
Cooperative (SPEC)
 AIM Benchmark suites

© 1995 Daniel A. Menascé
Capacity Planning Methodology for Client Server
Environments
Understanding the Environment
Developing a Cost Model
Workload Characterization
Workload
Model
Cost
Model
Validation and Calibration
Performance
Model
Workload Forecasting
Cost Prediction
Performance Prediction
Valid
Model
Cost/Performance Analysis
Configuration
Plan
Investment
Plan
Personnel
Plan
© 1995 Daniel A. Menascé
Workload Model Validation
Model Validation
Actual
Workload
Synthetic
Workload
System
System
measured
response
times
measured
response
times
acceptable?
YES
NO
Model
Calibration
Valid Workload Model
© 1995 Daniel A. Menascé
Capacity Planning Methodology for Client Server
Environments
Understanding the Environment
Developing a Cost Model
Workload Characterization
Workload
Model
Cost
Model
Validation and Calibration
Performance
Model
Workload Forecasting
Cost Prediction
Performance Prediction
Valid
Model
Cost/Performance Analysis
Configuration
Plan
Investment
Plan
Personnel
Plan
© 1995 Daniel A. Menascé
Workload Forecasting
Process of predicting the workload intensity.
tps
45
40
35
30
25
20
15
10
5
0
Queries
Updates
Q1
Q2
Q3
Q4
© 1995 Daniel A. Menascé
Workload Forecasting
Forecasting Business Units

Number of business elements that
determine the workload evolution
– number of invoices
– number of accounts
– number of employees
– number of claims
– number of beds
© 1995 Daniel A. Menascé
Workload Forecasting
Methodology
Application Selection
 Identification of Forecasting Business
Units (FBUs)
 Statistics gathering on FBUs
 FBU forecasting (use linear regression,
moving averages, exponential
smoothing) and business strategic plans.

© 1995 Daniel A. Menascé
Linear Regression Example
y = 101.7x + 1072.3
2
R = 0.9672
2000
1800
1600
1400
Series1
1200
1000
Linear
(Series1)
800
600
400
200
7
6
5
4
3
0
2
1
2
3
4
5
6
7
no. invoices
1200.0
1230.0
1400.0
1467.0
1590.0
1682.5
1784.2
1
month
© 1995 Daniel A. Menascé
Capacity Planning Methodology for Client Server
Environments
Understanding the Environment
Developing a Cost Model
Workload Characterization
Workload
Model
Cost
Model
Validation and Calibration
Performance
Model
Workload Forecasting
Cost Prediction
Performance Prediction
Valid
Model
Cost/Performance Analysis
Configuration
Plan
Investment
Plan
Personnel
Plan
© 1995 Daniel A. Menascé
Performance Prediction


Predictive models: analytic or simulation based.
Analytic models are based on Queuing Networks
(QNs)
– efficient
– allow for the fast analysis of a large number of
scenarios
– ideal for capacity planning
© 1995 Daniel A. Menascé
Performance Prediction
factors that impact performance
 Client
stations
 Servers
 Communication media
 Protocols
 Interconnection devices (bridges,
routers and gateways)
© 1995 Daniel A. Menascé
Performance Prediction
Model Accuracy
• coarse grain model
• little effort required for
data collection
• fine
grain model
• detailed data collection
for parameterization
Model Accuracy
Low
High
© 1995 Daniel A. Menascé
Performance Prediction
An Example
...
LAN Segment 1
R
router
FDDI ring
LAN segment 2
R
router
...
© 1995 Daniel A. Menascé
Performance Prediction
QN for Example
E1
C1
R1
DB
server 1
D1
B1
F
interconnection
between
segments
E2
B2
D2
R2
DB
server 2
C2
© 1995 Daniel A. Menascé
Performance Prediction
Response Times for the Example
4
3.5
Response
Time
(sec)
3
2.5
2
1.5
1
0.5
0
50
60
80
Number of clients
© 1995 Daniel A. Menascé
Capacity Planning Methodology for Client Server
Environments
Understanding the Environment
Developing a Cost Model
Workload Characterization
Workload
Model
Cost
Model
Validation and Calibration
Performance
Model
Workload Forecasting
Cost Prediction
Performance Prediction
Valid
Model
Cost/Performance Analysis
Configuration
Plan
Investment
Plan
Personnel
Plan
© 1995 Daniel A. Menascé
Performance Model Validation
Model Validation
Actual
System
Performance
Model
Measurements
Computations
measured
response
times
computed
response
times
acceptable?
Model
Calibration
NO
YES
Valid Performance Model
© 1995 Daniel A. Menascé
Capacity Planning Methodology for Client Server
Environments
Understanding the Environment
Developing a Cost Model
Workload Characterization
Workload
Model
Cost
Model
Validation and Calibration
Performance
Model
Workload Forecasting
Cost Prediction
Performance Prediction
Valid
Model
Cost/Performance Analysis
Configuration
Plan
Investment
Plan
Personnel
Plan
© 1995 Daniel A. Menascé
A Cost Model for C/S
Environments
Less than 5% of US companies quantify or
control PC and LAN costs.
 Some hidden costs in C/S environments:

–
–
–
–
hardware maintenance and support
software maintenance and upgrades
software distribution costs
personnel costs (approx.. 60% of total cost)
© 1995 Daniel A. Menascé
Some Cost ROTs




Software and hardware upgrades cost 10%
of purchase price per year.
A LAN administrator costs between
US$500 and US$700 per client WS/month.
Training costs vary between US$1,500 and
US$3,000 per technical staff person/year.
40% of personnel costs are in resource
management, 40% in application
development, and 20% in other categories.
© 1995 Daniel A. Menascé
Part IV:
Performance Prediction Models for
C/S Environments
© 1995 Daniel A. Menascé
Queues and Queuing Networks
completing transactions
Client
LAN
cpu
disk
completing transactions
DB server
© 1995 Daniel A. Menascé
Operational Analysis:
Quick Review
 Little’s
Law
 Utilization Law
 Forced Flow Law
 Service Demand Law
 Response Time Law
© 1995 Daniel A. Menascé
Single Queue
tps
X tps
W
S
R
average transaction arrival rate
X = average throughput
© 1995 Daniel A. Menascé
Single Queue
tps
X tps
W
S
R
W average waiting time
S = average service time
R = average response time
© 1995 Daniel A. Menascé
Single Queue
tps
X tps
W
S
R
R=W+S
© 1995 Daniel A. Menascé
Little’s Law
PUB
avg. number
people in the
pub
=
avg. arrival
rate at the X
pub
avg. time
spent at the
pub
© 1995 Daniel A. Menascé
Little’s Law
Example
A DB server executes 10 transactions per
second. On the average, 20 transactions
are being executed simultaneously. What
is the average transaction response time?
© 1995 Daniel A. Menascé
Little’s Law
Example
X =  10 tps
N = 20
Little’s Law: N =  R 
R = N /  = 20 /10 = 2 sec
© 1995 Daniel A. Menascé
Little’s Law applied to single
queues
tps
X tps
N
R
EquilibriumX
N = R
© 1995 Daniel A. Menascé
Utilization Law
busy time /
measurement interval=
no. transactions/
measurement interval =
B/T
C/T
tps
U
X tps
S
busy time /
no. transactions =
B/C
© 1995 Daniel A. Menascé
Utilization Law
U=B/T
tps
X= C/T
U
X tps
S
S = B/C
© 1995 Daniel A. Menascé
Utilization Law
U=B/T
tps
X= C/T
U
X tps
S
S = B/C
U = B / T = (B/C) / (T/C) = S * X
© 1995 Daniel A. Menascé
Utilization Law
tps
U
X tps
S
U=S*X
© 1995 Daniel A. Menascé
Utilization Law
Example
Each access to the DB server’s disk takes 25
msec on the average. During a one hour
interval, 108,000 I/O’s to the disk were
executed. What is the disk utilization?
© 1995 Daniel A. Menascé
Utilization Law
Example
S = 0.025 sec
X = 108,000 / 3,600 = 30 accesses/sec
Utilization Law: U = S * X 
U = 0.025 * 30 = 0.75 = 75 %
© 1995 Daniel A. Menascé
Forced Flow Law
i
Xi
Xo
Xi = Vi * Xo
Vi = avg. no. visits to device i per transaction
© 1995 Daniel A. Menascé
Forced Flow Law
Example
Each transaction executed on the DB
server performs 3 disk accesses on the
average. The disk utilization measured
during a one hour interval was 50%.
During the same interval, 7,200
transactions were executed. What is the
average service time at the disk?
© 1995 Daniel A. Menascé
Forced Flow Law
Example
Given:
Vi = 3 disk accesses per transaction
Ui = 30% = 0.3
Xo = 7,200 / 3,600 = 2 tps
Utilization Law: Ui = Si * Xi Si = Ui / Xi
Forced Flow Law: Xi = Vi * Xo
Xi = 3 * 2 = 6 tps
Si = Ui / Xi = 0.3 / 6 = 50 msec
© 1995 Daniel A. Menascé
Service Demand Law
S1
S2
S3
S4
service demand (D)
U
D = Si = V * S
i
© 1995 Daniel A. Menascé
Service Demand Law
S1
S2
S3
S4
service demand (D)
U
X o= C / T tps
D = (U * T) / C = U / (C / T) = U / X
© 1995 Daniel A. Menascé
Service Demand Law
S1
S2
S3
S4
service demand (D)
U
Xo= C / T tps
D = V * S = U / Xo
© 1995 Daniel A. Menascé
Response Time Law
i
Ri
Ro
Ro = Vi * Ri
i
Vi = avg. no. visits to device i per transaction
© 1995 Daniel A. Menascé
Operational Analysis: summary
Law: N = R
 Utilization Law: U = S * X
 Forced Flow Law: Xi = Vi * Xo
 Service Demand Law: D = U / Xo
 Response Time Law: Ro =  Vi*Ri
 Little’s
i
© 1995 Daniel A. Menascé
Queuing Networks
i
Xo
Ri
Ro
Given: service demands and no. of customers
Find: average response time (Ro), throughput
(Xo), average queue length per device.
© 1995 Daniel A. Menascé
Queuing Networks
Types of Devices
queuing device: load independent
Si(n) = Si for all n
queuing device: load dependent
Si(n) = f(n)
delay device
RT(i,n) = Di
© 1995 Daniel A. Menascé
Queuing Network Solution

Basic Technique : Mean Value Analysis
(MVA)
 Feature: simple, iterative and efficient.
© 1995 Daniel A. Menascé
Mean Value Analysis
Residence Time Equation

Residence Time (RT) at device i
RT (i,n) = Di + Di*NQ (i,n-1))
my total
service time
total waiting time =
total service time of all
customers I find ahead of me
© 1995 Daniel A. Menascé
Mean Value Analysis
Residence Time Equation

Residence Time (RT) at device i
RT (i,n) = Di * (1 + NQ (i,n-1))
where NQ (i,n) is the average number of
transactions at device i when there are n
transactions in the system.
© 1995 Daniel A. Menascé
Mean Value Analysis
Throughput Equation
n trans.
i
Xo
Ri
Ro
Little’s Law: n = Xo * Ro = Xo *  RT(i,n)
i
© 1995 Daniel A. Menascé
Mean Value Analysis
Throughput Equation

Throughput Xo (n)
Xo (n) = n /  RT (i,n)
i
where n is the number of transactions in the
system.
© 1995 Daniel A. Menascé
Mean Value Analysis
Queue Length Equation
X (i, n)
i
...
...
R (i, n)
NQ (i,n)
Little’s Law NQ (i, n) = R (i, n) * X (i, n)
© 1995 Daniel A. Menascé
Mean Value Analysis
Queue Length Equation
Little’s Law NQ (i, n) = R (i, n) * X (i, n)
Forced Flow Law X (i, n) = Vi * Xo (n)

NQ (i, n) = R (i, n) * Vi * Xo (n) =
RT (i, n) * Xo (n)
© 1995 Daniel A. Menascé
Mean Value Analysis
Queue Length Equation

Average Queue Length NQ (i, n)
NQ (i, n) = RT (i, n) * Xo (n)
© 1995 Daniel A. Menascé
Mean Value Analysis
Combining the 3 equations
Di * (1 + NQ (i,n-1))
RT (i,n) =
Di if device i is a delay device
Xo (n) = n / RT (i,n)
NQ (i, n) = RT (i, n) * Xo (n)
where NQ (i, 0) = 0 for all device i.
© 1995 Daniel A. Menascé
Mean Value Analysis
Combining the 3 equations
n=1
RT (i, 1) = Di * (1 + NQ (i, 0)) =
Di * (1 + 0) = Di
Xo (1) = 1 / RT (i,1)
NQ (i, 1) = RT (i, 1) * Xo (1)
© 1995 Daniel A. Menascé
Mean Value Analysis
Combining the 3 equations
n=2
RT (i, 2) = Di * (1 + NQ (i, 1))
Xo (2) = 2 / RT (i,2)
NQ (i, 2) = RT (i, 2) * Xo (2)
© 1995 Daniel A. Menascé
Mean Value Analysis
Example
n RT(cpu)
0
0.00
1
0.56
2
0.81
3
1.05
RT(disk)
0.00
0.68
1.05
1.45
Ro
0.00
1.24
1.87
2.50
Xo NQ(cpu)
0.00
0.00
0.81
0.45
1.07
0.87
1.20
1.26
NQ(disk)
0.00
0.55
1.13
1.74
© 1995 Daniel A. Menascé
Revisiting the C/S migration example
Response Time vs. No. Clients
6
5
4
SLA
3
2
1
0
20
30
40
50
60
70
80
© 1995 Daniel A. Menascé
C/S example: additional disk
Response Time vs. No. Clients
6
5
4
Orig.Server
Add.Disk
SLA
3
2
1
0
50
60
70
80
© 1995 Daniel A. Menascé
Part V:
Advanced Models for the
Performance Prediction of C/S
Systems
© 1995 Daniel A. Menascé
Example: Telemarketing
Application
Customers order products through a catalog.
 Orders are made by phone using a credit
card.
 30,000 orders are received every day.
 Calls are placed on hold for the first
available representative.

© 1995 Daniel A. Menascé
Example: Telemarketing
Application

Question:
How many representatives are needed
to guarantee that an incoming call will
not have to wait more than 5 sec on the
average?
© 1995 Daniel A. Menascé
Example: Telemarketing
application
DB server
LAN
© 1995 Daniel A. Menascé
Example: Telemarketing
Application
m
(to be determined ) workstations and
an SQL server.
 Ethernet LAN (10 Mbps)
 SQL server: one CPU and one disk.
© 1995 Daniel A. Menascé
Telemarketing Application
Hierarchical Model
call
arrival
rate
Average
waiting
time
per call
User Model
1
m
Xc (k) k=0, ..., m
application,
server, and
LAN
parameters
C/S model
© 1995 Daniel A. Menascé
Telemarketing Application
User Level Model

0
Xc (1)

1

2
Xc (2)
...
Xc (m)

m
...
Xc (m)

k
...
Xc (m)
k = no. of calls in the system
© 1995 Daniel A. Menascé

Telemarketing Application
User Level Model
• Computation of the average call arrival rate 
• 30,000 calls/day
• 12 hours of operation per day
• balanced traffic during the day:
30.000  calls / day

 0.69  calls / sec
12  3.600
© 1995 Daniel A. Menascé
User Level Model

0

1
Xc (1) Xc (2)

2
...

m
Xc (m)
...
Xc (m)

k
...
Xc (m)
• Flow balance equations:
state 0:
p(0) = Xc(1) . p(1)
© 1995 Daniel A. Menascé
User Level Model

0

1
Xc (1) Xc (2)

2
...

m
Xc (m)
...
Xc (m)

k
...
Xc (m)
• Flow balance equations:
state 0: p(0) = Xc(1) . p(1)
state 2: p(1 ) = Xc(2) . p(2)
© 1995 Daniel A. Menascé
User Level Model

0

1
Xc (1) Xc (2)

2
...

m
Xc (m)
...
Xc (m)

k
...
Xc (m)
• Flow balance equations:
state 0: p(0) = Xc(1) . p(1)
state 2: p(1 ) = Xc(2) . p(2)
...........
state m: p(m-1) = Xc(m). p(m)
© 1995 Daniel A. Menascé
User Level Model

0

1
Xc (1) Xc (2)

2
...

m
Xc (m)
...
Xc (m)

k
...
Xc (m)
• Flow balance equations:
state 0: p(0) = Xc(1) . p(1)
state 2: p(1 ) = Xc(2) . p(2)
...........
state m: p(m-1) = Xc(m). p(m)
...........
state k:
p(k-1) = Xc(m). p(k)
© 1995 Daniel A. Menascé
User Level Model

0

1

2
...
Xc (1) Xc (2)

m
Xc (m)
...
Xc (m)

k
...
Xc (m)
• Solution to flow balance equations:
k 1

p( k )  P(0)  
i0
Xc(i  1)
X c ( j )  X c ( m)j  m
© 1995 Daniel A. Menascé
User Level Model
• Solution to flow balance equations:
Pk  P0  / ( Xc (1)... Xc (k )) k  m
k
k m
 P0  /[ Xc (1)... Xc (m)( Xc (m)) ]
k
km
© 1995 Daniel A. Menascé
User Level Model
average waiting time per call, W

W  N w /   (1 /  ) kPkw
k 1
where
P   j  0 Pj
w
k
m
 Pk  m
k0
k0
© 1995 Daniel A. Menascé
C/S Model
completing transactions
Client
LAN
cpu
disk
completing transactions
DB server
© 1995 Daniel A. Menascé
C/S Model
• If the LAN utilization is very low, model it as
a delay device (e.g., in high bandwidth LAN
segments).
• If the utilization is greater than 20%, model
the LAN as a load dependent device.
• Bridges and routers should be modeled as
delay devices where the delay is the latency in
sec/packet.
© 1995 Daniel A. Menascé
Telemarketing Application
Average waiting time per call
waiting
time (sec)
1400
1200
1000
800
600
400
200
0
125
135
145
155
165
175
185
195
no.
clients
© 1995 Daniel A. Menascé
Telemarketing Application
Average waiting time per call
waiting time (sec)
9
8
7
minimum no.
representatives: 176
6
5
4
3
2
1
0
150 155 160 165 170 175 180 185
No.
clients
© 1995 Daniel A. Menascé
Part VI:
Capacity Planning Case Study for
C/S Environments:
A Retailing Company
(example adapted from Giacone’94)
© 1995 Daniel A. Menascé
Retailing Company: initial
configuration
mainframe
© 1995 Daniel A. Menascé
Retailing company: new configuration
server
4 MB
router
56 Kbps
router
16 MB
mainframe
© 1995 Daniel A. Menascé
Retailing Company
Mainframe stores enterprise DB in DB2.
 Several times during the day detail records
are sent from all stores to the mainframe.
 A C/S application was developed to
efficiently support a Decision Support
System (DSS).

© 1995 Daniel A. Menascé
Retailing Company
The C/S application extracts and formats
data from the mainframe and make them
locally available.
 Two UNIX servers are being considered:
HP and NCR.
 Question: how many clients can be
supported by each type of server while
keeping response times at acceptable
levels?

© 1995 Daniel A. Menascé
Retailing Company :
configuration alternatives

Client:
– PC 486
– 16 MB/Windows 3.1

NCR DB server:
–
–
–
–
–
UNIX
ORACLE
NCR 3555 4-Pentium (66 MHz)
256 MB RAM
SCSI disks
© 1995 Daniel A. Menascé
Retailing Company :
configuration alternatives

HP DB server
–
–
–
–
–
UNIX
ORACLE
HP 9000-H70 2 processors (96 MHz)
256 MB RAM
SCSI disks
© 1995 Daniel A. Menascé
Retailing Company :
configuration alternatives

LAN between client and DB server:
– Token Ring 4 Mbps
– TCP/IP

Link between LAN and mainframe
– 3 Kbytes/seg (38.4 Kbps) effective.
© 1995 Daniel A. Menascé
Retailing Company :
model parameterization
A typical transaction was developed.
 The transaction was executed several times
through a “script” on each server.
 Used measurement utilities and tools:
System Activity Reporter (sar), ps, netstat,
accounting, SPI (NCR), e PCS (HP).

© 1995 Daniel A. Menascé
Retailing Company :
CPU service demand for NCR server
UNIX :
10:44
10:45
10:46
...
10:58
Total
sar -u
%usr %sys %wio %idle
23
10
1
66
72
20
2
6
70
20
2
8
...
...
...
...
39
11
4
46
1267
© 1995 Daniel A. Menascé
Retailing Company :
CPU service demand for NCR server
measurement
avg.
interval
utilization
no.
processors
1,267% / 15 * 900 sec * 4
Dcpu =
9,600 transactions
=
.32 sec/transaction
© 1995 Daniel A. Menascé
Retailing Company :
CPU service demand for HP server
HP PCS:
07:40
07:41
...
07:45.
Ucpu = 53.5%
CPU%
54.1
53.7
...
53.1
1,535 transactions
© 1995 Daniel A. Menascé
Retailing Company :
CPU service demand for HP server
measurement
avg.
interval
utilization
no.
processors
53.7 % * 300 sec * 2
Dcpu =
1,535 transactions
=
.208 sec/transaction
© 1995 Daniel A. Menascé
Retailing Company :
disk service demand for NCR server
“NCR SPI Disk I/O and Service Detail”
10:44
d0100
d1010
d0010
...
Total
%Busy # Reads # Writes msec
1.0
0
43
13.5
49.6
1
2100
14.2
0.3
1
8
21.2
...
...
...
...
3
31495
9,600 transactions
© 1995 Daniel A. Menascé
Retailing Company :
disk service demand for NCR server
disco
Vr
Vw
S
0100 0.0000
0.059
1010 0.0003
3.113
0010 0.0001
0.012
D
13.2
14.2
20.8
0.78
44.21
0.25
© 1995 Daniel A. Menascé
Retailing Company :
disk service demand for HP server
PCS from 7:30 to 7:35
08:30
d1008
d1009
d1000
...
%Busy # Reads # Writes
0.1
90
0
1.2
270
0
7.5
0
1680
...
...
...
1,525 transactions
© 1995 Daniel A. Menascé
Retailing Company :
disk service demand for HP server
disco
Vr
1008
1009
1000
Vw
0.30
0.89
0.00
S
0.00
0.00
5.51
D
14.1
13.5
12.5
4.16
11.95
68.85
© 1995 Daniel A. Menascé
Response Time vs. No. Clients
sec
20.00
18.00
16.00
14.00
HP
12.00
Rncr
Rhp
10.00
8.00
NCR
6.00
4.00
2.00
no. clients
0.00
600
650
700
750
800
810
815
900
950
1000
1050
© 1995 Daniel A. Menascé
Concluding Remarks
C/S environments offer multiple
configuration alternatives: capacity, quantity
and server location, connectivity and
network capacity.
 Decisions related to system sizing require
the use of predictive performance models.
 Analytic models are the best alternative for
quickly analyzing multiple scenarios.

© 1995 Daniel A. Menascé
Bibliography

Book:
– Capacity Planning and Performance Modeling: from
mainframes to clients-servers systems, D. A.
Menascé, V. A. F. Almeida, L. W. Dowdy, Prentice
Hall, 1994.

Papers:
– G. Giacone, Real World Client/Server Sizing, Proc. of
CMG’94 , Dec., 1994.
– J. Gunther, Benchmarking a Client/Server
Application, Proc. CMG’94, Dec., 1994.
© 1995 Daniel A. Menascé
Bibliography

Papers:
– M. Salsburg, Capacity Planning in the Interoperable
Enterprise, Proc. CMG’94, Dec., 1994.
– T. Leo e D. Roberts, Benchmarking the File Server
Performance and Capacity, Proc. CMG’94, Dec., 94.
– Bell, T. E. and Falk, A. M., Measuring Application
Performance in Open Systems, Proc. CMG’92 , Dec.,
1992.
– Ho, E., Performance Management of Distributed
Open Systems, Proc. CMG’92, Dec., 1992.
© 1995 Daniel A. Menascé
Bibliography

Papers:
– Hufnagel, E., The Hidden Costs of Client/Server,
Your Client/Server Survival Kit, supplement to
Network Computing, Vol. 5, 1994.
– Information Technology Group, Cost of
Computing, Comparative Study of Mainframe and
PC/LAN Installations, Mountain View, CA, 1994.
– National Software Testing Laboratories, High
Performance 486 DX2 and Pentium Systems, PC
Digest Ratings Report, Vol. 8, Number 2,
February, 1994.
© 1995 Daniel A. Menascé
Bibliography

Papers:
– Vicente, J., Network Capacity Planning, Proc.
CMG’93, Dec. 1993.
– Waldner, R., Client/Server Capacity Planning,
Proc. CMG’92, Dec. 1992.
– Menascé , D., and V. Almeida, Two-Level
Performance Models of Client-Server Systems,
Proc. CMG’94, Dec. 1994.
© 1995 Daniel A. Menascé
Bibliography

Papers:
– Petriu, D. and C. Woodside, Approximate
MVA for Markov Models of Client/Server
Systems, Proc. Third IEEE Symp. Parallel and
Distributed Processing, Dallas 1991.
– Rolia, J.A., M.Starkey, and G.Boersma,
Modeling RPC Performance, Proc. IBM
Canada CASCON’93, Toronto, Oct. 1993.
© 1995 Daniel A. Menascé
THE END
© 1995 Daniel A. Menascé