1. © 1995 Daniel A. Menascé Capacity Planning in Client/Server Environments Daniel A. Menascé George Mason University Fairfax, VA 22030 USAmenasce@cs.gmu.edu

2. © 1995 Daniel A. Menascé Outline n Part I: Client/Server Systems n Part II: Introduction to Capacity Planning n Part III: A Capacity Planning Methodology for C/S Environments n Part IV: Performance Prediction Models for C/S Environments

3. © 1995 Daniel A. Menascé Outline (continued) n Part V: Advanced Predictive Models of C/S Systems n Part VI: Case Study n Bibliography

4. © 1995 Daniel A. Menascé Part I: Client/Server (C/S) Systems

5. © 1995 Daniel A. Menascé Definitions and Basic Concepts n Client n Server n Work division between client and server n Client/Server communication

6. © 1995 Daniel A. Menascé DB server R R... router FDDI ring LAN segment 1 LAN segment 2 Definitions and basic concepts

7. © 1995 Daniel A. Menascé Definitions and basic concepts: Client n Workstation with graphics and processing capabilities. n Graphical User Interface (GUI) implemented at the client. n Partial processing executed at the client.

8. © 1995 Daniel A. Menascé Definitions and basic concepts: Server n Machine with much larger processing and I/O capacity than the client. n Serves the various requests from the clients. n Executes a significant portion of the processing and I/O of the requests generated at the client.

9. © 1995 Daniel A. Menascé Work division between client and server GUI COMM. I/O Pre & Post Process. Processing DB ClientServer communications network

10. © 1995 Daniel A. Menascé Interaction between client and server Remote Procedure Call (RPC) client DB server pre-proces- sing post-proces- sing server processing execute_SQL(par1,par2,...) result_SQL(...)

11. © 1995 Daniel A. Menascé Part II: Introduction to Capacity Planning

12. © 1995 Daniel A. Menascé Migration to C/S example: “downsizing” a claim processing application n DB server connected to several PCs through an Ethernet LAN n GUI application executing at the PCs n LAN connected to the enterprise mainframe through a T1 line n DB server is updated every night.

13. © 1995 Daniel A. Menascé Migration to C/S systems mainframe based system mainframe T1 line

14. © 1995 Daniel A. Menascé Migration to C/S DB server based system mainframe T1 line DB server LAN gateway

15. © 1995 Daniel A. Menascé Migration to C/S: some important questions n How many clients can be supported by the DB server while maintaining a response time below 2.5 sec? n How long does it take to update the DB every night?

16. © 1995 Daniel A. Menascé Migration to C/S example: measurements with a prototype n During 30 minutes (1,800 sec): –25% CPU utilization –30% disk utilization – 800 transactions were executed n Each transaction used: 1,800 * 0.25 / 800 = 0.56 sec of CPU and 1,800 * 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.

17. © 1995 Daniel A. Menascé Good News and Bad News n 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. 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.

18. © 1995 Daniel A. Menascé DB Server Model arriving transactions cpu disk DB server departing transactions

19. © 1995 Daniel A. Menascé CPU or I/O Times service demand = 0.56 seg queue waiting time ?

20. © 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.

21. © 1995 Daniel A. Menascé C/S Migration Example: desired results no. of client workstations response time (sec) service level

22. © 1995 Daniel A. Menascé Part III: A Capacity Planning Methodology for Client/Server Environments

23. © 1995 Daniel A. Menascé Configuration Plan Investment Plan Personnel Plan Understanding the Environment Workload Characterization Workload Model Validation and Calibration Workload Forecasting Performance Prediction Cost Prediction Valid Model Cost Model Developing a Cost Model Performance Model Cost/Performance Analysis Capacity Planning Methodology for Client Server Environments

24. © 1995 Daniel A. Menascé Configuration Plan Investment Plan Personnel Plan Understanding the Environment Workload Characterization Workload Model Validation and Calibration Workload Forecasting Performance Prediction Cost Prediction Valid Model Cost Model Developing a Cost Model Performance Model Cost/Performance Analysis Capacity Planning Methodology for Client Server Environments

25. © 1995 Daniel A. Menascé Understanding the Environment n Hardware and System Software n Network Connectivity Map n Network Protocols n Server Configurations n Types of Applications n Service Level Agreements n Support and Management Structure n Procurement Procedures

26. © 1995 Daniel A. Menascé Example of Understanding the Environment n 5,000 PCs (386 e 486) running DOS and Windows 3.1 and 800 UNIX workstations. n IBM MVS mainframe. n 80 LANs in 20 buildings connected by an FDDI 100 Mbps backbone. n 50 Cisco routers. n Network technologies: FDDI, Ethernet, T1 links and Internet.

27. © 1995 Daniel A. Menascé Example of Understanding the Environment (continued) n Protocols being routed: TCP/IP and Novell IPX. n Servers: 80% are 486 and Pentiums and 20% are RISC workstations running UNIX. n Applications: office automation (e-mail, spreadsheets, wordprocessing), access to DBs (SQL servers) and resource sharing. n Future applications: teleconferencing, EDI, image processing.

28. © 1995 Daniel A. Menascé Configuration Plan Investment Plan Personnel Plan Understanding the Environment Workload Characterization Workload Model Validation and Calibration Workload Forecasting Performance Prediction Cost Prediction Valid Model Cost Model Developing a Cost Model Performance Model Cost/Performance Analysis Capacity Planning Methodology for Client Server Environments

29. © 1995 Daniel A. Menascé Workload Characterization n 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. n Workload components are composed of basic components.

30. © 1995 Daniel A. Menascé Workload Characterization: workload components and basic components

31. © 1995 Daniel A. Menascé Workload Characterization Basic Component Parameters n Workload Intensity Parameters –number of messages sent/hour –number of query transactions/sec n Service Demand Parameters –average message length –average I/O time per query transaction.

32. © 1995 Daniel A. Menascé Workload Characterization Methodology n Identification of Workload Components n Identification of Basic Components. n Parameter Selection. n Data Collection: benchmarks and ROTS (Rules of Thumb) may be used. (Rules of Thumb) may be used. n Workload partitioning: averaging and clustering.

33. © 1995 Daniel A. Menascé Workload Characterization Data Collection Alternatives

34. © 1995 Daniel A. Menascé Benchmarks n National Software Testing Laboratories (NSTL): servers and applications. n Transaction Processing Council (TPC) n System Performance Evaluation Cooperative (SPEC) n AIM Benchmark suites

35. © 1995 Daniel A. Menascé Configuration Plan Investment Plan Personnel Plan Understanding the Environment Workload Characterization Workload Model Validation and Calibration Workload Forecasting Performance Prediction Cost Prediction Valid Model Cost Model Developing a Cost Model Performance Model Cost/Performance Analysis Capacity Planning Methodology for Client Server Environments

36. © 1995 Daniel A. Menascé Workload Model Validation

37. © 1995 Daniel A. Menascé Configuration Plan Investment Plan Personnel Plan Understanding the Environment Workload Characterization Workload Model Validation and Calibration Workload Forecasting Performance Prediction Cost Prediction Valid Model Cost Model Developing a Cost Model Performance Model Cost/Performance Analysis Capacity Planning Methodology for Client Server Environments

38. © 1995 Daniel A. Menascé Workload Forecasting Process of predicting the workload intensity. Process of predicting the workload intensity. tps

39. © 1995 Daniel A. Menascé Workload Forecasting Forecasting Business Units n Number of business elements that determine the workload evolution –number of invoices –number of accounts –number of employees –number of claims –number of beds

40. © 1995 Daniel A. Menascé Workload Forecasting Methodology n Application Selection n Identification of Forecasting Business Units (FBUs) n Statistics gathering on FBUs n FBU forecasting (use linear regression, moving averages, exponential smoothing) and business strategic plans.

41. © 1995 Daniel A. Menascé Linear Regression Example

42. © 1995 Daniel A. Menascé Configuration Plan Investment Plan Personnel Plan Understanding the Environment Workload Characterization Workload Model Validation and Calibration Workload Forecasting Performance Prediction Cost Prediction Valid Model Cost Model Developing a Cost Model Performance Model Cost/Performance Analysis Capacity Planning Methodology for Client Server Environments

43. © 1995 Daniel A. Menascé Performance Prediction n Predictive models: analytic or simulation based. n Analytic models are based on Queuing Networks (QNs) –efficient –allow for the fast analysis of a large number of scenarios –ideal for capacity planning

44. © 1995 Daniel A. Menascé Performance Prediction factors that impact performance n Client stations n Servers n Communication media n Protocols n Interconnection devices (bridges, routers and gateways)

45. © 1995 Daniel A. Menascé Performance Prediction Model Accuracy

46. © 1995 Daniel A. Menascé Performance Prediction An Example R R... router FDDI ring LAN Segment 1 LAN segment 2

47. © 1995 Daniel A. Menascé Performance Prediction QN for Example

48. © 1995 Daniel A. Menascé Performance Prediction Response Times for the Example Response Time (sec) Number of clients

49. © 1995 Daniel A. Menascé Configuration Plan Investment Plan Personnel Plan Understanding the Environment Workload Characterization Workload Model Validation and Calibration Workload Forecasting Performance Prediction Cost Prediction Valid Model Cost Model Developing a Cost Model Performance Model Cost/Performance Analysis Capacity Planning Methodology for Client Server Environments

50. © 1995 Daniel A. Menascé Performance Model Validation

51. © 1995 Daniel A. Menascé Configuration Plan Investment Plan Personnel Plan Understanding the Environment Workload Characterization Workload Model Validation and Calibration Workload Forecasting Performance Prediction Cost Prediction Valid Model Cost Model Developing a Cost Model Performance Model Cost/Performance Analysis Capacity Planning Methodology for Client Server Environments

52. © 1995 Daniel A. Menascé A Cost Model for C/S Environments n Less than 5% of US companies quantify or control PC and LAN costs. n 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)

53. © 1995 Daniel A. Menascé Some Cost ROTs n Software and hardware upgrades cost 10% of purchase price per year. n A LAN administrator costs between US$500 and US$700 per client WS/month. n Training costs vary between US$1,500 and US$3,000 per technical staff person/year. n 40% of personnel costs are in resource management, 40% in application development, and 20% in other categories.

54. © 1995 Daniel A. Menascé Part IV: Performance Prediction Models for C/S Environments

55. © 1995 Daniel A. Menascé Queues and Queuing Networks cpu disk completing transactions LAN Client DB server completing transactions

56. © 1995 Daniel A. Menascé Operational Analysis: Quick Review n Little’s Law n Utilization Law n Forced Flow Law n Service Demand Law n Response Time Law

57. © 1995 Daniel A. Menascé Single Queue WS R  tps X tps  average transaction arrival rate X = average throughput

58. © 1995 Daniel A. Menascé Single Queue WS R  tps X tps W  average waiting time S = average service time R = average response time

59. © 1995 Daniel A. Menascé Single Queue WS R  tps X tps R = W + S

60. © 1995 Daniel A. Menascé Little’s Law PUB avg. number people in the pub = avg. arrival rate at the pub X avg. time spent at the pub

61. © 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?

62. © 1995 Daniel A. Menascé Little’s Law Example X =  10 tps N = 20 Little’s Law: N =  R  R = N / = 20 /10 = 2 sec

63. © 1995 Daniel A. Menascé Little’s Law applied to single queues  tps X tps N N =  R Equilibrium  X R

64. © 1995 Daniel A. Menascé Utilization Law no. transactions/ measurement interval =  tps X tps S U busy time / measurement interval= busy time / no. transactions = B/T C/T B/C

65. © 1995 Daniel A. Menascé Utilization Law X = C / T  tps X tps S U U = B / T S = B / C

66. © 1995 Daniel A. Menascé Utilization Law X = C / T  tps X tps S U U = B / T S = B / C U = B / T = (B/C) / (T/C) = S * X

67. © 1995 Daniel A. Menascé Utilization Law  tps X tps S U U = S * X

68. © 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?

69. © 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 %

70. © 1995 Daniel A. Menascé Forced Flow Law i Xi Xo Xi = Vi * Xo Vi = avg. no. visits to device i per transaction

71. © 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?

72. © 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

73. © 1995 Daniel A. Menascé Service Demand Law U S1 S2 S3 S4 service demand (D) D =  Si = V * S i

74. © 1995 Daniel A. Menascé Service Demand Law X o= C / T tps U S1 S2 S3 S4 service demand (D) D = (U * T) / C = U / (C / T) = U / X

75. © 1995 Daniel A. Menascé Service Demand Law Xo= C / T tps U S1 S2 S3 S4 service demand (D) D = V * S = U / Xo

76. © 1995 Daniel A. Menascé Response Time Law Ro =  Vi * Ri Vi = avg. no. visits to device i per transaction Ro i Ri i

77. © 1995 Daniel A. Menascé Operational Analysis: summary Little’s Law: N =  R Little’s Law: N =  R n Utilization Law: U = S * X n Forced Flow Law: Xi = Vi * Xo n Service Demand Law: D = U / Xo Response Time Law: Ro =  Vi*Ri Response Time Law: Ro =  Vi*Ri i

78. © 1995 Daniel A. Menascé Queuing Networks i Ri Xo Ro Given: service demands and no. of customers Find: average response time (Ro), throughput (Xo), average queue length per device.

79. © 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

80. © 1995 Daniel A. Menascé Queuing Network Solution n Basic Technique : Mean Value Analysis (MVA) n Feature: simple, iterative and efficient.

81. © 1995 Daniel A. Menascé Mean Value Analysis Residence Time Equation n Residence Time (RT) at device i RT (i,n) = Di + Di*NQ (i,n-1)) 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

82. © 1995 Daniel A. Menascé Mean Value Analysis Residence Time Equation n Residence 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 transactions at device i when there are n transactions in the system.

83. © 1995 Daniel A. Menascé Mean Value Analysis Throughput Equation i Ri Ro Xo n trans. Little’s Law: n = Xo * Ro = Xo *  RT(i,n) i

84. © 1995 Daniel A. Menascé Mean Value Analysis Throughput Equation n Throughput Xo (n) Xo (n) = n /   RT (i,n) Xo (n) = n /   RT (i,n) where n is the number of transactions in the system. i

85. © 1995 Daniel A. Menascé Mean Value Analysis Queue Length Equation i... X (i, n) R (i, n) NQ (i,n) Little’s Law  NQ (i, n) = R (i, n) * X (i, n)

86. © 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)

87. © 1995 Daniel A. Menascé Mean Value Analysis Queue Length Equation NQ (i, n) = RT (i, n) * Xo (n) n Average Queue Length NQ (i, n)

88. © 1995 Daniel A. Menascé Mean Value Analysis Combining the 3 equations NQ (i, n) = RT (i, n) * Xo (n) Xo (n) = n /  RT (i,n) Di * (1 + NQ (i,n-1)) where NQ (i, 0) = 0 for all device i. Di Di RT (i,n) = if device i is a delay device

89. © 1995 Daniel A. Menascé Mean Value Analysis Combining the 3 equations NQ (i, 1) = RT (i, 1) * Xo (1) Xo (1) = 1 /  RT (i,1) RT (i, 1) = Di * (1 + NQ (i, 0)) = Di * (1 + 0) = Di Di * (1 + 0) = Di n = 1

90. © 1995 Daniel A. Menascé Mean Value Analysis Combining the 3 equations NQ (i, 2) = RT (i, 2) * Xo (2) Xo (2) = 2 /  RT (i,2) RT (i, 2) = Di * (1 + NQ (i, 1)) n = 2

91. © 1995 Daniel A. Menascé Mean Value Analysis Example

92. © 1995 Daniel A. Menascé Revisiting the C/S migration example Response Time vs. No. Clients

93. © 1995 Daniel A. Menascé C/S example: additional disk Response Time vs. No. Clients

94. © 1995 Daniel A. Menascé Part V: Advanced Models for the Performance Prediction of C/S Systems

95. © 1995 Daniel A. Menascé Example: Telemarketing Application n Customers order products through a catalog. n Orders are made by phone using a credit card. n 30,000 orders are received every day. n Calls are placed on hold for the first available representative.

96. © 1995 Daniel A. Menascé Example: Telemarketing Application n Question: How many representatives are needed to guarantee that an incoming call will not have to wait more than 5 sec on the average?

97. © 1995 Daniel A. Menascé Example: Telemarketing application DB server LAN

98. © 1995 Daniel A. Menascé Example: Telemarketing Application n m (to be determined ) workstations and an SQL server. n Ethernet LAN (10 Mbps) n SQL server: one CPU and one disk.

99. © 1995 Daniel A. Menascé Telemarketing Application Hierarchical Model User Model C/S model 1 m call arrival rate Average waiting time per call Xc (k) k=0,..., m application, server, and LAN parameters

100. © 1995 Daniel A. Menascé Telemarketing Application User Level Model 0 1 2 m k... Xc (1)Xc (2) Xc (m) k = no. of calls in the system

101. © 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:

102. © 1995 Daniel A. Menascé User Level Model 0 1 2 m k... Xc (1)Xc (2) Xc (m) Flow balance equations: state 0:  p(0) = Xc(1). p(1)

103. © 1995 Daniel A. Menascé User Level Model 0 1 2 m k... Xc (1)Xc (2) Xc (m) Flow balance equations: state 0:  p(0) = Xc(1). p(1) state 2:  p(1 ) = Xc(2). p(2)

104. © 1995 Daniel A. Menascé User Level Model 0 1 2 m k... Xc (1)Xc (2) 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)

105. © 1995 Daniel A. Menascé User Level Model 0 1 2 m k... Xc (1)Xc (2) 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)

106. © 1995 Daniel A. Menascé User Level Model 0 1 2 m k... Xc (1)Xc (2) Xc (m) Solution to flow balance equations:

107. © 1995 Daniel A. Menascé User Level Model Solution to flow balance equations:

108. © 1995 Daniel A. Menascé User Level Model average waiting time per call, W where

109. © 1995 Daniel A. Menascé C/S Model cpu disk completing transactions LAN Client DB server completing transactions

110. © 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.

111. © 1995 Daniel A. Menascé Telemarketing Application Average waiting time per call no. clients waiting time (sec)

112. © 1995 Daniel A. Menascé Telemarketing Application Average waiting time per call No. clients waiting time (sec) minimum no. representatives: 176

113. © 1995 Daniel A. Menascé Part VI: Capacity Planning Case Study for C/S Environments: A Retailing Company (example adapted from Giacone’94)

114. © 1995 Daniel A. Menascé Retailing Company: initial configuration mainframe

115. © 1995 Daniel A. Menascé Retailing company: new configuration mainframe 4 MB 16 MB 56 Kbps server router

116. © 1995 Daniel A. Menascé Retailing Company n Mainframe stores enterprise DB in DB2. n Several times during the day detail records are sent from all stores to the mainframe. n A C/S application was developed to efficiently support a Decision Support System (DSS).

117. © 1995 Daniel A. Menascé Retailing Company n The C/S application extracts and formats data from the mainframe and make them locally available. n Two UNIX servers are being considered: HP and NCR. n Question: how many clients can be supported by each type of server while keeping response times at acceptable levels?

118. © 1995 Daniel A. Menascé Retailing Company : configuration alternatives n Client: –PC 486 –16 MB/Windows 3.1 n NCR DB server: –UNIX –ORACLE –NCR 3555 4-Pentium (66 MHz) –256 MB RAM –SCSI disks

119. © 1995 Daniel A. Menascé Retailing Company : configuration alternatives n HP DB server –UNIX –ORACLE –HP 9000-H70 2 processors (96 MHz) –256 MB RAM –SCSI disks

120. © 1995 Daniel A. Menascé Retailing Company : configuration alternatives n LAN between client and DB server: –Token Ring 4 Mbps –TCP/IP n Link between LAN and mainframe –3 Kbytes/seg (38.4 Kbps) effective.

121. © 1995 Daniel A. Menascé Retailing Company : model parameterization n A typical transaction was developed. n The transaction was executed several times through a “script” on each server. n Used measurement utilities and tools: System Activity Reporter (sar), ps, netstat, accounting, SPI (NCR), e PCS (HP).

122. © 1995 Daniel A. Menascé Retailing Company : CPU service demand for NCR server UNIX : sar -u

123. © 1995 Daniel A. Menascé Retailing Company : CPU service demand for NCR server Dcpu = 1,267% / 15 * 900 sec * 4 9,600 transactions = avg. utilization measurement interval no. processors.32 sec/transaction

124. © 1995 Daniel A. Menascé Retailing Company : CPU service demand for HP server HP PCS: Ucpu = 53.5% 1,535 transactions

125. © 1995 Daniel A. Menascé Retailing Company : CPU service demand for HP server Dcpu = 53.7 % * 300 sec * 2 1,535 transactions = avg. utilization measurement interval no. processors.208 sec/transaction

126. © 1995 Daniel A. Menascé Retailing Company : disk service demand for NCR server “NCR SPI Disk I/O and Service Detail” 9,600 transactions

127. © 1995 Daniel A. Menascé Retailing Company : disk service demand for NCR server

128. © 1995 Daniel A. Menascé Retailing Company : disk service demand for HP server PCS from 7:30 to 7:35 1,525 transactions

129. © 1995 Daniel A. Menascé Retailing Company : disk service demand for HP server

130. © 1995 Daniel A. Menascé Response 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 60065070075080081081590095010001050 Rncr Rhp sec HP NCR no. clients

131. © 1995 Daniel A. Menascé Concluding Remarks n C/S environments offer multiple configuration alternatives: capacity, quantity and server location, connectivity and network capacity. n Decisions related to system sizing require the use of predictive performance models. n Analytic models are the best alternative for quickly analyzing multiple scenarios.

132. © 1995 Daniel A. MenascéBibliography n 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. n 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.

133. © 1995 Daniel A. MenascéBibliography n 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.

134. © 1995 Daniel A. Menascé Bibliography n 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.

135. © 1995 Daniel A. Menascé Bibliography n 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. Proc. CMG’94, Dec. 1994.

136. © 1995 Daniel A. Menascé Bibliography n Papers: –Petriu, D. and C. Woodside, Approximate MVA for Markov Models of Client/Server Systems, Proc. Third IEEE Symp. Parallel and MVA for Markov Models of Client/Server Systems, Proc. Third IEEE Symp. Parallel and Distributed Processing, Dallas 1991. Distributed Processing, Dallas 1991. –Rolia, J.A., M.Starkey, and G.Boersma, Modeling RPC Performance, Proc. IBM Canada CASCON’93, Toronto, Oct. 1993.

137. © 1995 Daniel A. Menascé THE END