© 1995 daniel a. menascé capacity planning in client/server environments daniel a. menascé george...

© 1995 Daniel A. Menascé

Capacity Planning in Capacity Planning in Client/Server EnvironmentsClient/Server Environments

Daniel A. MenascéDaniel A. Menascé

George Mason UniversityGeorge Mason University

Fairfax, VA 22030Fairfax, VA 22030

USAUSA

[email protected]@cs.gmu.edu


OutlineOutline

Part I: Client/Server SystemsPart I: Client/Server Systems Part II: Introduction to Capacity Part II: Introduction to Capacity

PlanningPlanning Part III: A Capacity Planning Part III: A Capacity Planning

Methodology for C/S EnvironmentsMethodology for C/S Environments Part IV: Performance Prediction Part IV: Performance Prediction

Models for C/S EnvironmentsModels for C/S Environments


Outline (continued)Outline (continued)

Part V: Advanced Predictive Models of Part V: Advanced Predictive Models of C/S SystemsC/S Systems

Part VI: Case StudyPart VI: Case Study BibliographyBibliography


Part I:Part I:Client/Server (C/S) SystemsClient/Server (C/S) Systems


Definitions and Basic ConceptsDefinitions and Basic Concepts

ClientClient ServerServer Work division between client and Work division between client and

serverserver Client/Server communicationClient/Server communication


DB server

DB server

R

R

. . .

. . .router

router

FDDI ring

LAN segment 1

LAN segment 2

Definitions and basic conceptsDefinitions and basic concepts


Definitions and basic concepts:Definitions and basic concepts:ClientClient

Workstation with graphics and Workstation with graphics and processing capabilities.processing capabilities.

Graphical User Interface (GUI) Graphical User Interface (GUI) implemented at the client.implemented at the client.

Partial processing executed at the Partial processing executed at the client.client.


Definitions and basic concepts:Definitions and basic concepts:ServerServer

Machine with much larger processing Machine with much larger processing and I/O capacity than the client.and I/O capacity than the client.

Serves the various requests from the Serves the various requests from the clients.clients.

Executes a significant portion of the Executes a significant portion of the processing and I/O of the requests processing and I/O of the requests generated at the client.generated at the client.


Work division between client and Work division between client and serverserver

GUI

COMM. COMM.

I/OPre & Post

Process.

Processing

DB

Client Server

communications network


Interaction between client and serverInteraction between client and serverRemote Procedure Call (RPC)Remote Procedure Call (RPC)

client DB server

pre-proces-sing

post-proces-sing

serverprocessing

execute_SQL(par1,par2,...)

result_SQL(...)


Part II:Part II:Introduction to Capacity PlanningIntroduction to Capacity Planning


Migration to C/S example:Migration to C/S example:“downsizing” a claim processing application“downsizing” a claim processing application

DB server connected to several PCs DB server connected to several PCs through an Ethernet LANthrough an Ethernet LAN

GUI application executing at the PCsGUI application executing at the PCs LAN connected to the enterprise LAN connected to the enterprise

mainframe through a T1 linemainframe through a T1 line DB server is updated every night.DB server is updated every night.


Migration to C/S systems Migration to C/S systems mainframe based systemmainframe based system

mainframe

T1 line


Migration to C/S Migration to C/S DB server based systemDB server based system

mainframe

T1 line

DB server

LANgateway


Migration to C/S:Migration to C/S:some important questionssome important questions

How many clients can be supported by How many clients can be supported by the DB server while maintaining a the DB server while maintaining a response time below 2.5 sec? response time below 2.5 sec?

How long does it take to update the DB How long does it take to update the DB every night? every night?


Migration to C/S example: Migration to C/S example: measurements with a prototypemeasurements with a prototype

During 30 minutes (1,800 sec):During 30 minutes (1,800 sec):– 25% CPU utilization25% CPU utilization– 30% disk utilization30% disk utilization– 800 transactions were executed800 transactions were executed

Each transaction used:Each transaction used:

1,800 * 0.25 / 800 = 0.56 sec of CPU and1,800 * 0.25 / 800 = 0.56 sec of CPU and

1,800 * 0.30 / 800 = 0.68 sec of disk.1,800 * 0.30 / 800 = 0.68 sec of disk.


Good News and Bad NewsGood News and Bad News

Good News: we know the CPU and I/O Good News: we know the CPU and I/O service time of each transaction. service time of each transaction.

Bad News: transactions at the DB server Bad News: transactions at the DB server compete for CPU and I/O compete for CPU and I/O queues queues will will form at each device. We don’t know how form at each device. We don’t know how long each transaction long each transaction waits waits in the queue in the queue for the CPU and for the disk. for the CPU and for the disk.


DB Server ModelDB Server Model

arrivingtransactions

cpu

disk

DB server

departingtransactions


CPU or I/O TimesCPU or I/O Times

service demand = 0.56 seg

queue waitingtime

?


Capacity Planning DefinitionCapacity Planning Definition

Capacity Planning is the process of Capacity Planning is the process of predictingpredicting when the service levels will when the service levels will be violated as a function of the be violated as a function of the workload workload evolutionevolution, as well as the determination of , as well as the determination of the most cost-effective way of delaying the most cost-effective way of delaying system saturation. system saturation.


C/S Migration Example:C/S Migration Example:desired resultsdesired results

0

1

2

3

4

5

6

7

8

9

50 60 70 80 100

no. of client workstations

responsetime(sec)

servicelevel


Part III:Part III:A Capacity Planning Methodology A Capacity Planning Methodology

for Client/Server Environmentsfor Client/Server Environments


ConfigurationPlan

InvestmentPlan

PersonnelPlan

Understanding the Environment

Workload Characterization

WorkloadModel

Validation and Calibration

Workload Forecasting

Performance PredictionCost PredictionValidModel

Cost Model

Developing a Cost Model

PerformanceModel

Cost/Performance Analysis

Capacity Planning Methodology for Client Server Environments


Understanding the EnvironmentUnderstanding the Environment

Hardware and System SoftwareHardware and System Software Network Connectivity MapNetwork Connectivity Map Network ProtocolsNetwork Protocols Server ConfigurationsServer Configurations Types of Applications Types of Applications Service Level AgreementsService Level Agreements Support and Management StructureSupport and Management Structure Procurement Procedures Procurement Procedures


Example of Understanding the Example of Understanding the EnvironmentEnvironment

5,000 PCs (386 e 486) running DOS and Windows 5,000 PCs (386 e 486) running DOS and Windows 3.1 and 800 UNIX workstations.3.1 and 800 UNIX workstations.

IBM MVS mainframe.IBM MVS mainframe. 80 LANs in 20 buildings connected by an FDDI 80 LANs in 20 buildings connected by an FDDI

100 Mbps backbone. 100 Mbps backbone. 50 Cisco routers.50 Cisco routers. Network technologies: FDDI, Ethernet, T1 links Network technologies: FDDI, Ethernet, T1 links

and Internet.and Internet.


Example of Understanding the Example of Understanding the Environment (continued)Environment (continued)

Protocols being routed: TCP/IP and Novell IPX.Protocols being routed: TCP/IP and Novell IPX. Servers: 80% are 486 and Pentiums and 20% are Servers: 80% are 486 and Pentiums and 20% are

RISC workstations running UNIX.RISC workstations running UNIX. Applications: office automation (e-mail, Applications: office automation (e-mail,

spreadsheets, wordprocessing), access to DBs (SQL spreadsheets, wordprocessing), access to DBs (SQL servers) and resource sharing. servers) and resource sharing.

Future applications: teleconferencing, EDI, image Future applications: teleconferencing, EDI, image processing. processing.


ConfigurationPlan

InvestmentPlan

PersonnelPlan



WorkloadModel




Cost Model


PerformanceModel




Workload CharacterizationWorkload Characterization

Process of partitioning the global workload into Process of partitioning the global workload into subsets called subsets called workload componentsworkload components. Examples of . Examples of workload components:workload components:– DB transactions,DB transactions,– requests to a file server or,requests to a file server or,– jobs with similar characteristics.jobs with similar characteristics.

Workload components are composed of Workload components are composed of basic basic componentscomponents..


Workload Characterization: workload Workload Characterization: workload components and basic componentscomponents and basic components

Workload Component Basic Component

e-mail - send message- receive message

Access to a DB server - query- update

Acesso to mainframe - session establishment- execution of remote function.- session termination


Workload CharacterizationWorkload Characterization Basic Component ParametersBasic Component Parameters

Workload Intensity ParametersWorkload Intensity Parameters– number of messages sent/hournumber of messages sent/hour– number of query transactions/secnumber of query transactions/sec

Service Demand ParametersService Demand Parameters– average message lengthaverage message length– average I/O time per query transaction. average I/O time per query transaction.


Workload Characterization Workload Characterization MethodologyMethodology

Identification of Workload ComponentsIdentification of Workload Components Identification of Basic Components.Identification of Basic Components. Parameter Selection.Parameter Selection. Data Collection: benchmarks and ROTSData Collection: benchmarks and ROTS

(Rules of Thumb) may be used.(Rules of Thumb) may be used. Workload partitioning: averaging and Workload partitioning: averaging and

clustering.clustering.


Workload CharacterizationWorkload Characterization

Data Collection Alternatives Data Collection Alternatives

None Some Detailed

- use measurements only

- use measurements, “benchmarks” and ROTs

- use “benchmarks” and ROTs only

Data Collection Facilities


BenchmarksBenchmarks

National Software Testing Laboratories National Software Testing Laboratories (NSTL): servers and applications.(NSTL): servers and applications.

Transaction Processing Council (TPC)Transaction Processing Council (TPC) System Performance Evaluation System Performance Evaluation

Cooperative (SPEC)Cooperative (SPEC) AIM Benchmark suitesAIM Benchmark suites


ConfigurationPlan

InvestmentPlan

PersonnelPlan



WorkloadModel




Cost Model


PerformanceModel




Workload Model ValidationWorkload Model Validation

SyntheticWorkload

ActualWorkload

System System

measuredresponsetimes

measuredresponsetimes

acceptable?NO

YES

Model Calibration

Model Validation

Valid Workload Model


ConfigurationPlan

InvestmentPlan

PersonnelPlan



WorkloadModel




Cost Model


PerformanceModel




Workload ForecastingWorkload Forecasting

Process of predicting the workload intensity.Process of predicting the workload intensity.

0

5

10

15

20

25

30

35

40

45

Q1 Q2 Q3 Q4

Queries

Updates

tps


Workload ForecastingWorkload Forecasting Forecasting Business Units Forecasting Business Units

Number of business elements that Number of business elements that determine the workload evolutiondetermine the workload evolution

– number of invoicesnumber of invoices

– number of accountsnumber of accounts

– number of employeesnumber of employees

– number of claimsnumber of claims

– number of bedsnumber of beds


Workload Forecasting Workload Forecasting MethodologyMethodology

Application SelectionApplication Selection Identification of Forecasting Business Identification of Forecasting Business

Units (FBUs)Units (FBUs) Statistics gathering on FBUsStatistics gathering on FBUs FBU forecasting (use linear regression, FBU forecasting (use linear regression,

moving averages, exponential moving averages, exponential smoothing) and business strategic plans. smoothing) and business strategic plans.


Linear Regression ExampleLinear Regression Example

y = 101.7x + 1072.3

R2 = 0.9672

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1 2 3 4 5 6 7

Series1

Linear(Series1)

month no. invoices1 1200.02 1230.03 1400.04 1467.05 1590.06 1682.57 1784.2


ConfigurationPlan

InvestmentPlan

PersonnelPlan



WorkloadModel




Cost Model


PerformanceModel




Performance PredictionPerformance Prediction

Predictive models: analytic or simulation based.Predictive models: analytic or simulation based. Analytic models are based on Queuing Networks Analytic models are based on Queuing Networks

(QNs)(QNs)– efficientefficient– allow for the fast analysis of a large number of allow for the fast analysis of a large number of

scenariosscenarios– ideal for capacity planningideal for capacity planning


Performance PredictionPerformance Predictionfactors that impact performancefactors that impact performance

Client stationsClient stations ServersServers Communication mediaCommunication media ProtocolsProtocols Interconnection devices (bridges, Interconnection devices (bridges,

routers and gateways)routers and gateways)



Model Accuracy Model Accuracy

-

HighLow

• coarse grain model• little effort required for data collection

Model Accuracy

• fine grain model• detailed data collection for parameterization



An ExampleAn Example

R

R

. . .

. . .router

router

FDDI ring

LAN Segment 1

LAN segment 2


Performance PredictionPerformance Prediction QN for Example QN for Example

DB server 1

DB server 2

E1

E2

B1

B2

F

D1

D2

C1

C2

R1

R2

inter-connectionbetweensegments


Performance PredictionPerformance Prediction Response Times for the Example Response Times for the Example

50 60 800

0.5

1

1.5

2

2.5

3

3.5

4

50 60 80

Res

po

nse

Tim

e(s

ec)

Number of clients


ConfigurationPlan

InvestmentPlan

PersonnelPlan



WorkloadModel




Cost Model


PerformanceModel




Performance Model ValidationPerformance Model Validation

PerformanceModel

ActualSystem

Measurements Computations

measuredresponse times

computedresponsetimes

acceptable?NO

YES

ModelCalibration

Model Validation

Valid Performance Model


ConfigurationPlan

InvestmentPlan

PersonnelPlan



WorkloadModel




Cost Model


PerformanceModel




A Cost Model for C/S A Cost Model for C/S EnvironmentsEnvironments

Less than 5% of US companies quantify or Less than 5% of US companies quantify or control PC and LAN costs. control PC and LAN costs.

Some hidden costs in C/S environments:Some hidden costs in C/S environments:– hardware maintenance and supporthardware maintenance and support– software maintenance and upgradessoftware maintenance and upgrades– software distribution costssoftware distribution costs– personnel costs (approx.. 60% of total cost)personnel costs (approx.. 60% of total cost)


Some Cost ROTsSome Cost ROTs Software and hardware upgrades cost 10% of Software and hardware upgrades cost 10% of

purchase price per year. purchase price per year. A LAN administrator costs between US$500 A LAN administrator costs between US$500

and US$700 per client WS/month.and US$700 per client WS/month. Training costs vary between US$1,500 and Training costs vary between US$1,500 and

US$3,000 per technical staff person/year.US$3,000 per technical staff person/year. 40% of personnel costs are in resource 40% of personnel costs are in resource

management, 40% in application development, management, 40% in application development, and 20% in other categories.and 20% in other categories.


Part IV:Part IV:Performance Prediction Models for Performance Prediction Models for

C/S EnvironmentsC/S Environments


Queues and Queuing NetworksQueues and Queuing Networks

cpu

disk

completing transactions

LANClient

DB servercompleting transactions


Operational Analysis:Operational Analysis:Quick ReviewQuick Review

Little’s LawLittle’s Law Utilization LawUtilization Law Forced Flow LawForced Flow Law Service Demand LawService Demand Law Response Time LawResponse Time Law


Single QueueSingle Queue

W S

R

tps X tps

average transaction arrival rateX = average throughput



W S

R

tps X tps

W average waiting timeS = average service timeR = average response time



W S

R

tps X tps

R = W + S


Little’s LawLittle’s Law

PUB

avg. numberpeople in thepub

= avg. arrivalrate at thepub

Xavg. timespent at thepub


Little’s LawLittle’s LawExampleExample

A DB server executes 10 transactions per second. On the average, 20 transactions are being executed simultaneously. What is the average transaction response time?


Little’s LawLittle’s LawExampleExample

X = 10 tpsN = 20

Little’s Law: N = R

R = N / = 20 /10 = 2 sec


Little’s Law applied to single Little’s Law applied to single queuesqueues

tps X tps

N

N = R EquilibriumX

R


Utilization LawUtilization Lawno. transactions/measurement interval =

tps X tps

S

U

busy time /measurement interval=

busy time /no. transactions =

B/T C/T

B/C


Utilization LawUtilization Law

X = C / T

tps X tps

S

U

U = B / T

S = B / C



X = C / T

tps X tpsS

U

U = B / T

S = B / C

U = B / T = (B/C) / (T/C) = S * X



tps X tps

S

U

U = S * X


Utilization LawUtilization LawExampleExample

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?


Utilization LawUtilization LawExampleExample

S = 0.025 sec

X = 108,000 / 3,600 = 30 accesses/sec

Utilization Law: U = S * X

U = 0.025 * 30 = 0.75 = 75 %


Forced Flow LawForced Flow Law

i Xi

Xo

Xi = Vi * Xo

Vi = avg. no. visits to device i per transaction


Forced Flow LawForced Flow LawExampleExample

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?


Forced Flow LawForced Flow LawExampleExample

Given:Vi = 3 disk accesses per transaction Ui = 30% = 0.3Xo = 7,200 / 3,600 = 2 tps

Utilization Law: Ui = Si * Xi Si = Ui / XiForced Flow Law: Xi = Vi * Xo Xi = 3 * 2 = 6 tps

Si = Ui / Xi = 0.3 / 6 = 50 msec


Service Demand LawService Demand Law

U

S1S2S3S4

service demand (D)

D = Si = V * Si



X o= C / T tpsU

S1S2S3S4

service demand (D)

D = (U * T) / C = U / (C / T) = U / X



Xo= C / T tpsU

S1S2S3S4

service demand (D)

D = V * S = U / Xo


Response Time LawResponse Time Law

Ro = Vi * Ri

Vi = avg. no. visits to device i per transaction

Ro

i

Ri

i


Operational Analysis: summaryOperational Analysis: summary

Little’s Law: N = Little’s Law: N = RR Utilization Law: U = S * XUtilization Law: U = S * X Forced Flow Law: Xi = Vi * XoForced Flow Law: Xi = Vi * Xo Service Demand Law: D = U / XoService Demand Law: D = U / Xo Response Time Law: Ro = Response Time Law: Ro = Vi*Ri Vi*Ri

i


Queuing NetworksQueuing Networks

i

RiXo

Ro

Given: service demands and no. of customersFind: average response time (Ro), throughput (Xo), average queue length per device.


Queuing NetworksQueuing NetworksTypes of DevicesTypes of Devices

queuing device: load independentSi(n) = Si for all n

queuing device: load dependent Si(n) = f(n)

delay device RT(i,n) = Di


Queuing Network SolutionQueuing Network Solution

Basic Technique Basic Technique : : Mean Value Analysis Mean Value Analysis (MVA)(MVA)

FeatureFeature: : simple, iterative and efficient. simple, iterative and efficient.


Mean Value AnalysisMean Value AnalysisResidence Time EquationResidence Time Equation

Residence Time (RT) at device iResidence Time (RT) at device i

RT (i,n) = Di + Di*NQ (i,n-1))RT (i,n) = Di + Di*NQ (i,n-1))

my totalservice time

total waiting time =total service time of all customers I find ahead of me


Mean Value Analysis Mean Value Analysis Residence Time EquationResidence Time Equation

Residence Time (RT) at device iResidence Time (RT) at device i

RT (i,n) = Di * (1 + NQ (i,n-1))RT (i,n) = Di * (1 + NQ (i,n-1))

where NQ (i,n) is the average number of where NQ (i,n) is the average number of transactions at device transactions at device i i when there are when there are nn transactions in the system.transactions in the system.


Mean Value Analysis Mean Value Analysis Throughput EquationThroughput Equation

iRi

Ro

Xo

n trans.

Little’s Law: n = Xo * Ro = Xo * RT(i,n)i


Mean Value Analysis Mean Value Analysis Throughput EquationThroughput Equation

Throughput Xo (n)Throughput Xo (n)

Xo (n) = n / Xo (n) = n / RT (i,n)RT (i,n)

where where nn is the number of transactions in the is the number of transactions in the system. system.

i


Mean Value Analysis Mean Value Analysis Queue Length EquationQueue Length Equation

i. . . . . .

X (i, n)

R (i, n)

NQ (i,n)

Little’s Law NQ (i, n) = R (i, n) * X (i, n)


Mean Value Analysis Mean Value Analysis Queue Length EquationQueue 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)


Mean Value AnalysisMean Value AnalysisQueue Length EquationQueue Length Equation

NQ (i, n) = RT (i, n) * Xo (n)

Average Queue Length NQ (i, n)Average Queue Length NQ (i, n)


Mean Value Analysis Mean Value Analysis Combining the 3 equationsCombining the 3 equations

NQ (i, n) = RT (i, n) * Xo (n)NQ (i, n) = RT (i, n) * Xo (n)

Xo (n) = n / Xo (n) = n / RT (i,n)RT (i,n)

Di * (1 + NQ (i,n-1))Di * (1 + NQ (i,n-1))

where NQ (i, 0) = 0 for all device i.

DiDiRT (i,n) =RT (i,n) = if device i is a delay device



NQ (i, 1) = RT (i, 1) * Xo (1)NQ (i, 1) = RT (i, 1) * Xo (1)

Xo (1) = 1 / Xo (1) = 1 / RT (i,1)RT (i,1)

RT (i, 1) = Di * (1 + NQ (i, 0)) =RT (i, 1) = Di * (1 + NQ (i, 0)) =

Di * (1 + 0) = DiDi * (1 + 0) = Di

n = 1



NQ (i, 2) = RT (i, 2) * Xo (2)NQ (i, 2) = RT (i, 2) * Xo (2)

Xo (2) = 2 / Xo (2) = 2 / RT (i,2)RT (i,2)

RT (i, 2) = Di * (1 + NQ (i, 1)) RT (i, 2) = Di * (1 + NQ (i, 1))

n = 2


Mean Value AnalysisMean Value AnalysisExampleExample

n RT(cpu) RT(disk) Ro Xo NQ(cpu) NQ(disk)0 0.00 0.00 0.00 0.00 0.00 0.001 0.56 0.68 1.24 0.81 0.45 0.552 0.81 1.05 1.87 1.07 0.87 1.133 1.05 1.45 2.50 1.20 1.26 1.74


Revisiting the C/S migration Revisiting the C/S migration exampleexample

Response Time vs. No. ClientsResponse Time vs. No. Clients

0

1

2

3

4

5

6

20 30 40 50 60 70 80

SLA


C/S example: additional diskC/S example: additional disk Response Time vs. No. ClientsResponse Time vs. No. Clients

0

1

2

3

4

5

6

50 60 70 80

Orig.Server

Add.Disk

SLA


Part V:Part V:Advanced Models for the Advanced Models for the

Performance Prediction of C/S Performance Prediction of C/S SystemsSystems


Example: Telemarketing Example: Telemarketing ApplicationApplication

Customers order products through a Customers order products through a catalog. catalog.

Orders are made by phone using a credit Orders are made by phone using a credit card. card.

30,000 orders are received every day.30,000 orders are received every day. Calls are placed on hold for the first Calls are placed on hold for the first

available representative. available representative.



Question:Question:

How many representatives are needed How many representatives are needed to guarantee that an incoming call will to guarantee that an incoming call will not have to wait more than 5 sec on the not have to wait more than 5 sec on the average?average?


Example: Telemarketing Example: Telemarketing applicationapplication

DB server

LAN



mm (to be determined ) workstations and (to be determined ) workstations and an SQL server.an SQL server.

Ethernet LAN (10 Mbps)Ethernet LAN (10 Mbps) SQL server: one CPU and one disk. SQL server: one CPU and one disk.


Telemarketing ApplicationTelemarketing Application Hierarchical Model Hierarchical Model

User Model

C/S model

1

m

call arrivalrate

Averagewaitingtimeper call

Xc (k) k=0, ..., m

application,server, andLANparameters


Telemarketing ApplicationTelemarketing ApplicationUser Level ModelUser Level Model

0 1 2 m k. . . . . . . . .

Xc (1) Xc (2) Xc (m) Xc (m) Xc (m)

k = no. of calls in the system


Telemarketing ApplicationTelemarketing ApplicationUser Level ModelUser 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

12 3 6000 69

. /

.. / sec

calls daycalls


User Level ModelUser Level Model

0 1 2 m k. . . . . . . . .

Xc (1) Xc (2) Xc (m) Xc (m) Xc (m)

• Flow balance equations: state 0: p(0) = Xc(1) . p(1)



0 1 2 m k. . . . . . . . .

Xc (1) Xc (2) Xc (m) Xc (m) Xc (m)

• Flow balance equations: state 0: p(0) = Xc(1) . p(1) state 2: p(1 ) = Xc(2) . p(2)



0 1 2 m k. . . . . . . . .

Xc (1) Xc (2) Xc (m) Xc (m) 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)



0 1 2 m k. . . . . . . . .

Xc (1) Xc (2) Xc (m) Xc (m) 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)



0 1 2 m k. . . . . . . . .

Xc (1) Xc (2) Xc (m) Xc (m) Xc (m)

• Solution to flow balance equations:

p k PXc ii

k

( ) ( )( )

010

1

X j X m j mc c( ) ( )


User Level ModelUser Level Model• Solution to flow balance equations:

P P X X kkk

c c 0 1 / ( ( )... ( )) k m

P X X m X mkc c c

k m0 1 /[ ( )... ( )( ( )) ]

k m


User Level ModelUser Level Modelaverage waiting time per call, Waverage waiting time per call, W

W N kPw kw

k

/ ( / ) 11

where

P Pkw

jj

m

0k 0

k 0 Pk m


C/S ModelC/S Model

cpu

disk

completing transactions

LANClient

DB servercompleting transactions


C/S ModelC/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.


Telemarketing ApplicationTelemarketing ApplicationAverage waiting time per callAverage waiting time per call

0

200

400

600

800

1000

1200

1400

125 135 145 155 165 175 185 195no. clients

waitingtime (sec)


Telemarketing ApplicationTelemarketing ApplicationAverage waiting time per callAverage waiting time per call

0

1

2

3

4

5

6

7

8

9

150 155 160 165 170 175 180 185

No. clients

waiting time (sec) minimum no. representatives: 176


Part VI:Part VI:Capacity Planning Case Study for Capacity Planning Case Study for

C/S Environments:C/S Environments:A Retailing CompanyA Retailing Company

(example adapted from Giacone’94)(example adapted from Giacone’94)


Retailing Company: initial Retailing Company: initial configurationconfiguration

mainframe


Retailing company: new configurationRetailing company: new configuration

mainframe

4 MB

16 MB

56 Kbps

server

router

router


Retailing CompanyRetailing Company

Mainframe stores enterprise DB in DB2.Mainframe stores enterprise DB in DB2. Several times during the day detail records Several times during the day detail records

are sent from all stores to the mainframe.are sent from all stores to the mainframe. A C/S application was developed to A C/S application was developed to

efficiently support a Decision Support efficiently support a Decision Support System (DSS). System (DSS).


Retailing Company Retailing Company The C/S application extracts and formats The C/S application extracts and formats

data from the mainframe and make them data from the mainframe and make them locally available. locally available.

Two UNIX servers are being considered: HP Two UNIX servers are being considered: HP and NCR.and NCR.

Question: Question: how many clients can be how many clients can be supported by each type of server while supported by each type of server while keeping response times at acceptable levels? keeping response times at acceptable levels?


Retailing Company :Retailing Company :configuration alternativesconfiguration alternatives

Client:Client:– PC 486PC 486

– 16 MB/Windows 3.116 MB/Windows 3.1 NCR DB server:NCR DB server:

– UNIXUNIX

– ORACLEORACLE

– NCR 3555 4-Pentium (66 MHz)NCR 3555 4-Pentium (66 MHz)

– 256 MB RAM256 MB RAM

– SCSI disksSCSI disks



HP DB serverHP DB server– UNIXUNIX– ORACLEORACLE– HP 9000-H70 2 processors (96 MHz)HP 9000-H70 2 processors (96 MHz)– 256 MB RAM256 MB RAM– SCSI disksSCSI disks



LAN between client and DB server:LAN between client and DB server:– Token Ring 4 MbpsToken Ring 4 Mbps– TCP/IPTCP/IP

Link between LAN and mainframeLink between LAN and mainframe– 3 Kbytes/seg (38.4 Kbps) effective. 3 Kbytes/seg (38.4 Kbps) effective.


Retailing Company :Retailing Company :model parameterizationmodel parameterization

A typical transaction was developed.A typical transaction was developed. The transaction was executed several times The transaction was executed several times

through a “script” on each server. through a “script” on each server. Used measurement utilities and tools: Used measurement utilities and tools:

System Activity Reporter (sar), ps, netstat, System Activity Reporter (sar), ps, netstat, accounting, SPI (NCR)accounting, SPI (NCR), e , e PCS (HP)PCS (HP)..


Retailing Company :Retailing Company :CPU service demand for NCR server CPU service demand for NCR server

%usr %sys %wio %idle

10:44 23 10 1 66

10:45 72 20 2 6

10:46 70 20 2 8

... ... ... ... ...

10:58 39 11 4 46

Total 1267

UNIX : sar -u


Retailing Company :Retailing Company : CPU service demand for NCR server CPU service demand for NCR server

Dcpu =

1,267% / 15 * 900 sec * 4

9,600 transactions

=

avg.utilization

measurementinterval

no.processors

.32 sec/transaction


Retailing Company :Retailing Company : CPU service demand for HP serverCPU service demand for HP server

CPU%

07:40 54.1

07:41 53.7

... ...

07:45. 53.1

HP PCS:

Ucpu = 53.5% 1,535 transactions


Retailing Company :Retailing Company : CPU service demand for HP serverCPU service demand for HP server

Dcpu =

53.7 % * 300 sec * 2

1,535 transactions

=

avg.utilization

measurementinterval

no.processors

.208 sec/transaction


Retailing Company :Retailing Company : disk service demand for NCR serverdisk service demand for NCR server

“NCR SPI Disk I/O and Service Detail”

10:44 %Busy # Reads # Writes msec

d0100 1.0 0 43 13.5

d1010 49.6 1 2100 14.2

d0010 0.3 1 8 21.2

... ... ... ... ...

Total 3 31495

9,600 transactions


Retailing Company :Retailing Company : disk service demand for NCR serverdisk service demand for NCR server

disco Vr Vw S D0100 0.0000 0.059 13.2 0.781010 0.0003 3.113 14.2 44.210010 0.0001 0.012 20.8 0.25


Retailing Company :Retailing Company : disk service demand for HP serverdisk service demand for HP server

PCS from 7:30 to 7:35

08:30 %Busy # Reads # Writes

d1008 0.1 90 0

d1009 1.2 270 0

d1000 7.5 0 1680

... ... ... ...

1,525 transactions


Retailing Company :Retailing Company : disk service demand for HP serverdisk service demand for HP server

disco Vr Vw S D1008 0.30 0.00 14.1 4.161009 0.89 0.00 13.5 11.951000 0.00 5.51 12.5 68.85


Response Time vs. No. ClientsResponse Time vs. No. Clients

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

600 650 700 750 800 810 815 900 950 1000 1050

RncrRhp

sec

HP

NCR

no. clients


Concluding RemarksConcluding Remarks

C/S environments offer multiple C/S environments offer multiple configuration alternatives: capacity, configuration alternatives: capacity, quantity and server location, connectivity quantity and server location, connectivity and network capacity. and network capacity.

Decisions related to system sizing require Decisions related to system sizing require the use of predictive performance models. the use of predictive performance models.

Analytic models are the best alternative for Analytic models are the best alternative for quickly analyzing multiple scenarios. quickly analyzing multiple scenarios.


BibliographyBibliography Book:Book:

– Capacity Planning and Performance Modeling: from Capacity Planning and Performance Modeling: from mainframes to clients-servers systemsmainframes to clients-servers systems, D. A. Menascé, , D. A. Menascé, V. A. F. Almeida, L. W. Dowdy, Prentice Hall, 1994.V. A. F. Almeida, L. W. Dowdy, Prentice Hall, 1994.

Papers:Papers:– G. GiaconeG. Giacone, Real World Client/Server Sizing, Real World Client/Server Sizing, Proc. of , Proc. of

CMG’94 , Dec., 1994. CMG’94 , Dec., 1994. – J. Gunther, J. Gunther, Benchmarking a Client/Server ApplicationBenchmarking a Client/Server Application, ,

Proc. CMG’94, Dec., 1994. Proc. CMG’94, Dec., 1994.


BibliographyBibliography Papers:Papers:

– M. SalsburgM. Salsburg, Capacity Planning in the Interoperable , Capacity Planning in the Interoperable EnterpriseEnterprise, Proc. CMG’94, Dec., 1994., Proc. CMG’94, Dec., 1994.

– T. Leo e D. RobertsT. Leo e D. Roberts, Benchmarking the File Server , Benchmarking the File Server Performance and CapacityPerformance and Capacity, Proc. CMG’94, Dec., 94., Proc. CMG’94, Dec., 94.

– Bell, T. E. and Falk, A. M., Bell, T. E. and Falk, A. M., Measuring Application Measuring Application Performance in Open SystemsPerformance in Open Systems, Proc. CMG’92 , Dec., , Proc. CMG’92 , Dec., 1992.1992.

– Ho, E., Ho, E., Performance Management of Distributed Performance Management of Distributed Open SystemsOpen Systems, Proc. CMG’92, Dec., 1992., Proc. CMG’92, Dec., 1992.


BibliographyBibliography Papers:Papers:

– Hufnagel, E., Hufnagel, E., The Hidden Costs of Client/Server, Your The Hidden Costs of Client/Server, Your Client/Server Survival KitClient/Server Survival Kit, supplement to Network , supplement to Network Computing, Vol. 5, 1994.Computing, Vol. 5, 1994.

– Information Technology Group, Information Technology Group, Cost of Computing, Cost of Computing, Comparative Study of Mainframe and PC/LAN Comparative Study of Mainframe and PC/LAN InstallationsInstallations, Mountain View, CA, 1994., Mountain View, CA, 1994.

– National Software Testing Laboratories, National Software Testing Laboratories, High High Performance 486 DX2 and Pentium SystemsPerformance 486 DX2 and Pentium Systems , PC Digest , PC Digest Ratings Report, Vol. 8, Number 2, February, 1994.Ratings Report, Vol. 8, Number 2, February, 1994.


BibliographyBibliography

Papers:Papers:– Vicente, J., Vicente, J., Network Capacity PlanningNetwork Capacity Planning, Proc. , Proc.

CMG’93, Dec. 1993. CMG’93, Dec. 1993. – Waldner, R., Waldner, R., Client/Server Capacity PlanningClient/Server Capacity Planning, ,

Proc. CMG’92, Dec. 1992.Proc. CMG’92, Dec. 1992.– Menascé , D., and V. Almeida, Menascé , D., and V. Almeida, Two-Level Two-Level

Performance Models of Client-Server SystemsPerformance Models of Client-Server Systems,,

Proc. CMG’94, Dec. 1994.Proc. CMG’94, Dec. 1994.


BibliographyBibliography

Papers:Papers:– Petriu, D. and C. Woodside, Petriu, D. and C. Woodside, ApproximateApproximate

MVA for Markov Models of Client/Server MVA for Markov Models of Client/Server Systems,Systems, Proc. Third IEEE Symp. Parallel and Proc. Third IEEE Symp. Parallel and

Distributed Processing, Dallas 1991.Distributed Processing, Dallas 1991.– Rolia, J.A., M.Starkey, and G.Boersma, Rolia, J.A., M.Starkey, and G.Boersma,

Modeling RPC PerformanceModeling RPC Performance, Proc. IBM , Proc. IBM Canada CASCON’93, Toronto, Oct. 1993.Canada CASCON’93, Toronto, Oct. 1993.


THE ENDTHE END

© 1995 daniel a. menascé capacity planning in client/server environments daniel a. menascé george...

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