Performance Tuning Siebel Software on the Sun Platform

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5.2 Server Resource Utilization Figure 5.2 Node Functional Use % CPU Utilization 1 x Sun Fire E2900 (4CPU,16GB) Oracle Database Server 51 1690 1 x Sun Fire V480 (4CPU,16GB) <strong>Siebel</strong> App Server – EAI-HTTP + WorkFlow 65 2075 1 x Sun Fire V440 (4CPU, 16GB) <strong>Siebel</strong> App Server – AM + EAI MQ series 37 1742 1 x Sun Fire V440 (4CPU, 16GB) <strong>Siebel</strong> App Server – 1600 end users 91 10,666 1 x Sun Fire E2900 (12CPU, 48GB) <strong>Siebel</strong> App Server – 4800 end users 66 38,956 1x Sun Fire V890 (8CPU,32GB) <strong>Siebel</strong> App Server – 3600 end users 68 25,871 Sun Java System Web Server – HTTP Adapter, 8 1x Sun Fire V440 (4CPU, 16GB) WorkFlow 126 1 x Sun Fire V880 (4CPU,16GB) Sun Java System Web Server – Application requests 49 186 1 x Sun Fire V440 (4CPU, 16GB) Sun Java System Web Server – Application requests 54 225 1 x Sun Fire V440 (4CPU, 16GB) <strong>Siebel</strong> Gateway Server/Sun Java System Directory 11 81 Server Table 5.2 Memory Utilization (MB) <strong>Performance</strong> <strong>Tuning</strong> <strong>Siebel</strong> <strong>Software</strong> on the Sun Platform Page 16
6 <strong>Siebel</strong> Scalability on the Sun Platform On the Sun platform the <strong>Siebel</strong> application scales to high concurrent users extremely well. With <strong>Siebel</strong>’s flexible distributed architecture and Sun’s large server product line, an optimal <strong>Siebel</strong>-on-Sun deployment can be achieved either with several small (one to four CPU) machines or with a single large Sun machine (such as a E15K, E6900, and so on). The following graphs depict <strong>Siebel</strong> scalability on different Sun machine types. All the tuning that was applied to achieve these results (as documented in the next chapter in this article) can be applied on production systems. Figure 6.1 <strong>Performance</strong> <strong>Tuning</strong> <strong>Siebel</strong> <strong>Software</strong> on the Sun Platform Page 17
Page 1 and 2: Performance <stron
Page 3 and 4: Abstract This paper discusses the p
Page 5 and 6: 7.6.6 Oracle Parameters Tun
Page 7 and 8: 1 Tuning for Price
Page 9 and 10: 2 Siebel Applicati
Page 11 and 12: 3 Optimal Sun/Siebel</stron
Page 13 and 14: 3.1 Hardware and Software</
Page 15: 5 10,000 Concurrent Users: Test Res
Page 19 and 20: Cost per Siebel Us
Page 21 and 22: 7.1 Tuning Solaris
Page 23 and 24: 7.1.3 The Solaris Kernel and TCP/IP
Page 25 and 26: In the file $SIEBEL_ROOT/bin/enu/ua
Page 27 and 28: 7.2.3 Assignment Manager Tu
Page 29 and 30: MQ series parameter tuning: PERFQMG
Page 31 and 32: Investigating the Reason for the Fa
Page 33 and 34: let a Siebel proce
Page 35 and 36: Description The reason for this sym
Page 37 and 38: Parameter Scope Default Tuned Value
Page 39 and 40: 7.6 Tuning <strong
Page 41 and 42: 86 MB/second 57 MB/second Figure 7.
Page 43 and 44: DATA_51200K INDX_512K DATA_5120K TE
Page 45 and 46: 7.6.4 Solaris MPSS Tuning</
Page 47 and 48: Siebel Object Name
Page 49 and 50: instance_name=oramst job_queue_proc
Page 51 and 52: ORAPERF.S_PRI_LST T4 WHERE T4.PAYME
Page 53 and 54: the database during peak load. In t
Page 55 and 56: use connection pooling, so you woul
Page 57 and 58: Oracle Database Server 1. Larger th
Page 59 and 60: 160 siebelapp2_1 eSalesObjMgr_enu 1
Page 61 and 62: 9.10 To Produce a Stack Trace for t
Page 63 and 64: Count indv cuml rcnt nsec Lock Call
Page 65 and 66: 5. Once the user has stopped at the
Page 67 and 68:
10 Appendix A: Transaction Response
Page 69 and 70:
eSls1_115DDSubCategory1.3 0.125 0.1
Page 71 and 72:
CC3_2newSolution 0.047 0.136 3.281
Page 73 and 74:
11 Appendix B: Database Objects Gro
Page 75 and 76:
S_OPTY_BU_M8 INDEX 2,334,720 S_ORDE
Page 77 and 78:
log file parallel write 550,101 515
Page 79:
Acknowledgements Thanks to
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