Performance Tuning Siebel Software on the Sun Platform

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Figure 6.2 The V440 server uses UltraSPARC III chips while the V890 and E2900 use UltraSPARC IV chips. Figure 6.2 shows the difference in scalability between the classes of Sun machines. Customers can this data for the capacity planning and sizing of their real world deployments. Keep in mind that the workload used for these results should be identical to the customer real-world deployment or appropriate adjustments need to be made to the server sizing. For more workload details, please see Section 4. <strong>Performance</strong> <strong>Tuning</strong> <strong>Siebel</strong> <strong>Software</strong> on the Sun Platform Page 18
Cost per <strong>Siebel</strong> User on Sun Platform Figure 6.3 *Note:$$/user number is based purely on hardware cost and does not include environmental factors, facilities, service, or management. Sun servers provide the best price/performance for <strong>Siebel</strong> applications. Figure 6.3 depicts the costs of the typical <strong>Siebel</strong> user on various models of Sun servers tested. Price/<strong>Performance</strong> Summary Per Tier of the Sun-<strong>Siebel</strong> Deployment Application tier: Sun Fire V440: 440 users/CPU ($17.57/user) Sun Fire V890: 662 users/CPU ($23.42/user) Sun Fire E2900: 606 users/CPU ($37.54/user) Database tier (Sun Fire E2900): 4902 users/CPU ($5.46/user) Web tier (Sun Fire V440): 2453 users/CPU ($3.15/user) Average response time: from 0.126 to 0.303 sec (component-specific) Success rate: > 99.999% (8 failures out of ~1.2 million transactions) <strong>Performance</strong> <strong>Tuning</strong> <strong>Siebel</strong> <strong>Software</strong> on the Sun Platform Page 19
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 and 16: 5 10,000 Concurrent Users: Test Res
Page 17: 6 Siebel Scalabili
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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Performance Tuning Siebel Software on the Sun Platform

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