Muutke küpsiste eelistusi

Solving Enterprise Applications Performance Puzzles: Queuing Models to the Rescue [Pehme köide]

4.40/5 (10 hinnangut Goodreads-ist)
  • Formaat: Paperback / softback, 256 pages, kõrgus x laius x paksus: 236x157x14 mm, kaal: 363 g, Screen captures: 30 B&W, 0 Color; Graphs: 50 B&W, 0 Color
  • Ilmumisaeg: 06-Mar-2012
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 1118061578
  • ISBN-13: 9781118061572
Teised raamatud teemal:
Solving Enterprise Applications Performance Puzzles: Queuing Models to the RescueSuurem pilt
  • Formaat: Paperback / softback, 256 pages, kõrgus x laius x paksus: 236x157x14 mm, kaal: 363 g, Screen captures: 30 B&W, 0 Color; Graphs: 50 B&W, 0 Color
  • Ilmumisaeg: 06-Mar-2012
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 1118061578
  • ISBN-13: 9781118061572
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781118061572.html
Märksõnad:
Poorly performing enterprise applications are the weakest links in a corporation's management chain, causing delays and disruptions of critical business functions. This groundbreaking book frames enterprise application performance engineering not as an art but as applied science built on model-based methodological foundation. The book introduces queuing models of enterprise application that visualize, demystify, explain, and solve system performance issues. Analysis of these models will help to discover and clarify unapparent connections and correlations among workloads, hardware architecture, and software parameters.
Acknowledgments ix
Preface xi
1 Queuing Networks as Applications Models
1(24)
1.1 Enterprise Applications---What Do They Have in Common?
1(5)
1.2 Key Performance Indicator---Transaction Time
6(2)
1.3 What Is Application Tuning and Sizing?
8(1)
1.4 Queuing Models of Enterprise Application
9(10)
1.5 Transaction Response Time and Transaction Profile
19(3)
1.6 Network of Highways as an Analogy of the Queuing Model
22(3)
Take Away from the
Chapter
24(1)
2 Building and Solving Application Models
25(32)
2.1 Building Models
25(9)
Hardware Specification
26(2)
Model Topology
28(1)
A Model's Input Data
29(2)
Model Calibration
31(3)
2.2 Essentials of Queuing Networks Theory
34(5)
2.3 Solving Models
39(8)
2.4 Interpretation of Modeling Results
47(10)
Hardware Utilization
47(4)
Server Queue Length, Transaction Time, System Throughput
51(3)
Take Away from the
Chapter
54(3)
3 Workload Characterization and Transaction Profiling
57(37)
3.1 What Is Application Workload?
57(3)
3.2 Workload Characterization
60(21)
Transaction Rate and User Think Time
61(4)
Think Time Model
65(3)
Take Away from the Think Time Model
68(1)
Workload Deviations
68(1)
"Garbage in, Garbage out" Models
68(1)
Realistic Workload
69(3)
Users' Redistribution
72(1)
Changing Number of Users
72(3)
Transaction Rate Variation
75(3)
Take Away from "Garbage in, Garbage out" Models
78(1)
Number of Application Users
78(2)
User Concurrency Model
80(1)
Take Away from User Concurrency Model
81(1)
3.3 Business Process Analysis
81(7)
3.4 Mining Transactional Data from Production Applications
88(6)
Profiling Transactions Using Operating System Monitors and Utilities
88(2)
Application Log Files
90(1)
Transaction Monitors
91(2)
Take Away from the
Chapter
93(1)
4 Servers, CPUs, and Other Building Blocks of Application Scalability
94(20)
4.1 Application Scalability
94(1)
4.2 Bottleneck Identification
95(19)
CPU Bottleneck
97(1)
CPU Bottleneck Models
97(1)
CPU Bottleneck Identification
97(3)
Additional CPUs
100(1)
Additional Servers
100(1)
Faster CPUs
100(4)
Take Away from the CPU Bottleneck Model
104(1)
I/O Bottleneck
105(1)
I/O Bottleneck Models
106(1)
I/O Bottleneck Identification
106(1)
Additional Disks
107(1)
Faster Disks
108(3)
Take Away from the I/O Bottleneck Model
111(2)
Take Away from the
Chapter
113(1)
5 Operating System Overhead
114(13)
5.1 Components of an Operating System
114(4)
5.2 Operating System Overhead
118(9)
System Time Models
122(1)
Impact of System Overhead on Transaction Time
123(1)
Impact of System Overhead on Hardware Utilization
124(1)
Take Away from the
Chapter
125(2)
6 Software Bottlenecks
127(30)
6.1 What Is a Software Bottleneck?
127(4)
6.2 Memory Bottleneck
131(13)
Memory Bottleneck Models
133(1)
Preset Upper Memory Limit
133(5)
Paging Effect
138(5)
Take Away from the Memory Bottleneck Model
143(1)
6.3 Thread Optimization
144(8)
Thread Optimization Models
145(1)
Thread Bottleneck Identification
145(3)
Correlation Among Transaction Time, CPU Utilization, and the Number of Threads
148(2)
Optimal Number of Threads
150(1)
Take Away from Thread Optimization Model
151(1)
6.4 Other Causes of Software Bottlenecks
152(5)
Transaction Affinity
152(1)
Connections to Database; User Sessions
152(2)
Limited Wait Time and Limited Wait Space
154(1)
Software Locks
155(1)
Take Away from the
Chapter
155(2)
7 Performance and Capacity of Virtual Systems
157(16)
7.1 What Is Virtualization?
157(3)
7.2 Hardware Virtualization
160(11)
Non-Virtualized Hosts
161(4)
Virtualized Hosts
165(2)
Queuing Theory Explains It All
167(2)
Virtualized Hosts Sizing After Lesson Learned
169(2)
7.3 Methodology of Virtual Machines Sizing
171(2)
Take Away from the
Chapter
172(1)
8 Model-Based Application Sizing: Say Good-Bye to Guessing
173(21)
8.1 Why Model-Based Sizing?
173(4)
8.2 A Model's Input Data
177(9)
Workload and Expected Transaction Time
177(2)
How to Obtain a Transaction Profile
179(3)
Hardware Platform
182(4)
8.3 Mapping a System into a Model
186(2)
8.4 Model Deliverables and What-If Scenarios
188(6)
Take Away from the
Chapter
193(1)
9 Modeling Different Application Configurations
194(21)
9.1 Geographical Distribution of Users
194(4)
Remote Office Models
196(1)
Users' Locations
196(1)
Network Latency
197(1)
Take Away from Remote Office Models
198(1)
9.2 Accounting for the Time on End-User Computers
198(2)
9.3 Remote Terminal Services
200(1)
9.4 Cross-Platform Modeling
201(2)
9.5 Load Balancing and Server Farms
203(2)
9.6 Transaction Parallel Processing Models
205(10)
Concurrent Transaction Processing by a Few Servers
205(4)
Concurrent Transaction Processing by the Same Server
209(4)
Take Away from Transaction Parallel Processing Models
213(1)
Take Away from the
Chapter
214(1)
Glossary 215(5)
References 220(3)
Index 223
Leonid Grinshpan Ph.D. (Stamford, CT) has been working for Oracle Corporation for more than 14 years as Consulting Technical Director. His customers include Dell, Citibank, Verizon, Clorox, Bank of America, AT&T, Best Buy, Aetna and many other major enterprises. He obtained Ph.D. in queuing models of computer systems from Russian Academy of Science.