1. COMP9321 Web Application Engineering Semester 2, 2017
Dr. Amin Beheshti
Service Oriented Computing Group, CSE, UNSW Australia
Week 10
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2. Assignment 1 Marks on GradeBook Assignment 2 Demo this week
Assignments
Assignment 1 Marks on GradeBook Assignment 2 Demo this week
Assignment 3 is due end of Week 12
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3. Performance
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4. Performance
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5. Performance
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6. Performance
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7. Performance
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8. Scalability
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9. Scalability
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10. Scalability
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11. Why does performance and scalability matter ?
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12. Why does performance and scalability matter ?
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13. Why does performance and scalability matter ?
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14. Why does performance and scalability matter ?
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15. Why does performance and scalability matter ?
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16. Why does performance and scalability matter ?
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17. Performance Analysis and Evaluation
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18. Performance Analysis and Evaluation
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19. Performance Analysis and Evaluation
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20. Defining goals
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21. Defining goals
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22. Performance Metrics
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23. Performance Metrics
RTT
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24. Performance Metrics
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25. Performance Metrics
Thrashing is an unstable collapse of throughput of a system as the load is increased.
After we reach max throughput, we have reached the throughput plateau...
e.g. plateau can be considered as the point where the throughput really falls off …
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26. Performance Metrics
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27. Performance Metrics
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28. Performance Metrics
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29. Performance Metrics
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30. Modelling
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31. Modelling
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32. Modelling
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33. Modelling
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34. Modelling Requests
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35. Modelling Requests
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36. Modelling Requests
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37. Modelling Requests
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38. Modelling Requests
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39. Modelling Requests
A power law is a relationship in which a relative change in one quantity gives rise to a proportional relative change in the other quantity, independent of the initial size of those quantities.
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40. Modelling Requests
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41. Modelling Requests
Nagios monitors your entire IT infrastructure to ensure systems, applications, services, and business processes are functioning properly.
Sysperfinfo is a tabular view of the performance counters. E.g.  you can access SQL Server related performance counter values through the system table sysperfinfo…
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42. Modelling Requests
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43. Modelling Requests
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44. Modelling Requests
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45. Modelling Requests
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46. Modelling Requests
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47. Modelling Resources
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48. Modelling Resources
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49. Using Modelling for Performance Analysis
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50. Using Modelling for Performance Analysis
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51. Using Modelling for Performance Analysis
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52. Performance Testing
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53. Performance Testing
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54. Performance Testing
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55. Operational Analysis
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56. Operational Analysis
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57. Operational Analysis
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58. Operational Analysis
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59. Operational Laws
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60. Operational Laws
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61. Operational Laws
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62. Operational Laws
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63. Operational Laws
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64. Operational Laws
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65. Operational Laws
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66. Operational Laws
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67. Operational Laws
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68. Operational Laws
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69. Operational Laws
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70. Operational Laws
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71. Operational Laws
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72. Operational Laws
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73. Operational Laws
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74. Operational Laws
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75. Operational Laws
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76. Operational Laws
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77. Bottleneck Analysis
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78. Bottleneck Analysis COMP9321, 17s2, Week 10
Asymptotic analysis is a method of describing limiting behavior.
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79. Bottleneck Analysis
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80. A few statistical quantities
The quartiles of a ranked set of data values are the three points that divide the data set into four equal groups, each group comprising a quarter of the data
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81. Architectural Considerations (Network)
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82. Architectural Considerations - Network
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83. Architectural Considerations - Network
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84. Architectural Considerations - Network
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85. Architectural Considerations (Server)
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86. Architectural Considerations - Server
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87. Architectural Considerations - Server
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88. Architectural Considerations - Server
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89. General Techniques for Improving Performance and Scalability
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90. General Techniques for Improving Performance and Scalability
Caching/Replication
Parallelism
Redundancy
Asynchrony
Resource Pooling
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91. General Techniques for Improving Performance and Scalability
Caching/Replication
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92. General Techniques for Improving Performance and Scalability
2. Parallelism
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93. General Techniques for Improving Performance and Scalability
3. Redundancy
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94. General Techniques for Improving Performance and Scalability
4. Asynchrony
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95. General Techniques for Improving Performance and Scalability
5. Resource Pooling
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96. Appendix
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97. -Improving Performance using HTTP features -Improving Database Access -Characteristics of Scalability -Scaling and Request Load Balancing
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98. Improving Performance using HTTP features
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99. Improving Performance using HTTP features
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100. Improving Performance using HTTP features
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101. Improving Performance using HTTP features
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102. Improving Performance using HTTP features
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103. Improving Performance using HTTP features
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104. Improving Performance using HTTP features
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105. Improving Performance using HTTP features
AJAX = Asynchronous JavaScript and XML
Update a web page without reloading the page
Request data from a server - after the page has loaded 
Receive data from a server - after the page has loaded
Send data to a server - in the background
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106. Improving Performance using HTTP features
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107. Improving Database Access
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108. Improving Database Access
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109. Improving Database Access
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110. Improving Database Access
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111. Improving Database Access
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112. Improving Database Access
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113. Improving Database Access
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114. Improving Database Access
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115. Improving Database Access
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116. Improving Database Access
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117. Improving Database Access
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118. Improving Database Access
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119. Improving Database Access
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120. Improving Database Access
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121. Improving Database Access
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122. Improving Database Access
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123. Characteristics of Scalability
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124. Characteristics of Scalability
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125. Characteristics of Scalability
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126. Characteristics of Scalability
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127. Scaling and Request Load Balancing
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128. Scaling & Request Load Balancing
Types of Scaling:
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129. Scaling & Request Load Balancing
Types of Scaling:
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130. Scaling & Request Load Balancing
Request Load Balancing for Horizontal Scaling
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131. Scaling & Request Load Balancing
Request Load Balancing for Horizontal Scaling
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132. Scaling & Request Load Balancing
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133. Scaling & Request Load Balancing
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134. Scaling & Request Load Balancing
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135. Scaling & Request Load Balancing
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136. Scaling & Request Load Balancing
Hours and Wages
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137. Scaling & Request Load Balancing
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138. Scaling & Request Load Balancing
Application-Level Load Balancing with Stateless Logic
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139. Scaling & Request Load Balancing
Application-Level Load Balancing with Stateless Logic
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140. Scaling & Request Load Balancing
Application-Level Load Balancing with Stateless Logic
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141. Scaling & Request Load Balancing
Scaling Databases - Replication
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142. Scaling & Request Load Balancing
Scaling Databases - Replication
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143. Scaling & Request Load Balancing
Scaling Databases - Replication
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144. Scaling & Request Load Balancing
Scaling Databases - Replication
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145. Scaling & Request Load Balancing
Scaling Databases - Replication
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146. References
Performance by Design: Computer Capacity Planning by Example by D. Menasce, V.A.F. Almeida, L. Dowdy, Prentice Hall
The Art of Computer System Performance Analysis by Raj Jain, Wiley Press
Performance Testing Guidance for Web Applications: Patterns & Practice by Meier, et. al, Microsoft Corporation
Building Scalable and High-performance Java Web Applications by Greg Barish, Addison Wesley
Building Scalable Web Sites by Cal Henderson, O’Reilly Books
Gregor Roth, ”Server load balancing architectures”, JavaWorld, October 2008
http: //
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