An Oracle White Paper June 2010 Consolidating Oracle Siebel CRM Environments with High Availability on Sun SPARC Enterprise Servers
Executive Overview... 1! Introduction... 2! Key Solution Technologies... 4! An Overview of Oracle s Siebel CRM Application Architecture... 5! Workload Description... 6! Business Transaction Types... 8! Test Environments... 9! Phase 1 Test Environment... 9! Phase 2 Test Environment... 10! Phase 1 Testing Consolidating Tiers Using Containers and Domains... 11! Performance and Scalability Results with Oracle Solaris Containers... 12! Performance and Scalability Results with Oracle VM Server for SPARC... 18! Phase 2 Testing Implementing HA... 22! Configuring for HA Using Oracle Solaris Cluster Software... 23! Phase 2 Testing Scenarios... 26! Performance and Scalability Results with Oracle Solaris Cluster. 26! Failover Testing with Oracle Solaris Cluster... 32! Best Practices and Recommendations... 34! Server/Operating System Optimizations... 34! I/O Best Practices... 35! Web Tier Best Practices... 36! Siebel Application Tier Best Practices... 37! Oracle Database Tier Best Practices... 37! Best Practices for High Availability Configurations... 38!
Sizing Guidelines... 39! Baseline Configurations... 41! Small HA Configuration up to 3,500 users... 41! Medium HA Configuration up to 7,000 users... 41! Large HA Configuration up to 14,000 users... 41! Conclusion... 42! Appendix A Phase 1 Configuration of Containers... 43! Web Server... 43! Application Server... 44! Database Server... 44! Appendix B Phase 1 Configuration of Oracle VM Server for SPARC... 45! Primary Domain... 45! Siebel Application Server Domain... 45! Siebel Web Server Domain... 46! Appendix C Phase 2 Configuration of Zone Clusters... 46! Web Server... 47! Gateway Server... 49! Application Server... 51! Database Server... 52! About the Authors... 55! Acknowledgements... 55! References... 56!
Executive Overview Founded on a service-oriented architecture, Oracle! Siebel Customer Relationship Management (CRM) software allows businesses to build scalable standards-based applications that can help to attract new business, increase customer loyalty, and improve profitability. As companies deliver more comprehensive and rich customer experiences through CRM tools, demand can scale rapidly, forcing datacenters to expand system resources quickly to meet increasing workloads. Datacenter resources can be scaled horizontally (with more servers added at each tier), vertically (by adding more powerful servers), or both. As servers are added at Siebel Web, Gateway, Application, and Database tiers, a frequent result is server sprawl. Over time, this can result in negative consequences greater complexity, poor utilization, increased maintenance fees, and skyrocketing power and cooling costs. Consolidating tiers is one approach that can help to contain server sprawl and reduce costs. Recognizing the need to grow efficiently while scaling Oracle Siebel CRM capabilities, Oracle created a proof-of-concept solution that consolidates Web, Gateway, Application, and Database tiers on a single Sun SPARC Enterprise server from Oracle, limiting the number of physical machines needed to effectively deploy applications and improving the bottom line. As shown in testing exercises using a well-known Siebel CRM workload and virtualization technologies built into Sun SPARC Enterprise servers, the solution scales easily to accommodate user load, even with workloads of up to 14,000 users. Because Oracle Siebel CRM applications support business profit centers, they often operate under stringent availability requirements and necessitate demanding service levels. For this reason, Oracle engineers conducted a second phase of proof-of-concept testing. In the second phase, software tiers 1
were again consolidated using built-in virtualization technologies but this time in a clustered server configuration that provided high availability (HA). The HA tests demonstrated near linear scalability while at the same time providing mission-critical levels of application availability. Introduction To safely and securely consolidate Siebel CRM application tiers, Sun SPARC Enterprise servers offer a choice of built-in, no-cost virtualization technologies: Oracle Solaris Containers. Containers are an integrated virtualization mechanism that can isolate application services within a single Oracle Solaris instance. Faults in one container have no impact on applications or service instances running in other containers. Oracle VM Server for SPARC (formerly known as Sun Logical Domains). Native to Sun CMT processors (like UltraSPARC T2 Plus processors), this technology allows multiple tiers to be consolidated within isolated domains, without imposing additional cost. Each domain runs an independent copy of Oracle Solaris, and there are no licensing fees for additional OS copies. Using one or both of these virtualization technologies, Siebel CRM services in each tier can run in isolation, without impacting service execution in other tiers. System resources can be allocated and reassigned to each tier as needed. Compared to other competitive and proprietary virtualization technologies, using Oracle Solaris Containers and/or Oracle VM Server for SPARC can provide significant cost savings when consolidating a Siebel CRM infrastructure. In addition, Oracle guarantees binary compatibility for applications running under Oracle Solaris, whether the OS runs natively as the host OS or as a guest OS in a virtualized environment. 2
In two phases of scalability testing, Oracle engineers configured different Siebel CRM tiers in virtualized environments on Sun SPARC Enterprise servers. In the first phase, engineers consolidated tiers on a single server, configuring each Siebel CRM tier in a separate container or domain. The initial phase of testing compared the scalability of the two different virtualization technologies. In a second phase of testing, engineers implemented Oracle Solaris Cluster (which supports both containers and domains) on two Sun SPARC Enterprise servers to simulate mission-critical Siebel CRM application workloads in a consolidated yet resilient virtualized environment. For both phases of testing, the test workload was extracted from the wellestablished Siebel Platform Sizing and Performance Program (PSPP) benchmark, which simulates real-world environments using some of the most popular Siebel CRM modules. Engineers looked at system resource utilization, response time, and throughput metrics as they scaled the number of users under typical application workloads. This paper shows the test results and clearly documents best practices, which can help system architects more effectively size and optimize the Siebel CRM application on Sun SPARC Enterprise servers. The test results demonstrate how no-cost virtualization technologies in Sun SPARC Enterprise servers combined with Oracle Solaris Cluster software can optimize scalability while reducing datacenter complexity, lowering operating costs and delivering high availability for business-critical CRM services. 3
Key Solution Technologies The tested solution was based on Oracle s massively scalable Sun SPARC Enterprise servers, the Oracle Solaris 10 operating system, and Oracle s open storage technologies, as shown in Figure 1. Built-in, no-cost virtualization technologies Oracle Solaris Containers or Oracle VM Server for SPARC reside at the heart of the solution architecture and enable a flexible infrastructure for consolidation. Oracle Solaris Cluster (and often third-party management tools) are typically added to enhance business continuity and simplify resource allocation tasks for virtualized environments. Figure 1. Using no-cost virtualization technologies, the proof-of-concept combined Siebel CRM tiers on a Sun SPARC Enterprise T5440 server. In the first phase of testing, Oracle engineers constructed a proof-of-concept solution based on a single Sun SPARC Enterprise T5440 server (see Figure 1), which features up to four UltraSPARC T2 Plus processors with up to 32 cores and up to 256 concurrently executing threads. With such advanced thread density, a single Sun SPARC Enterprise T5440 server is a powerhouse for consolidating a Siebel infrastructure. To demonstrate this point, Oracle engineers ran a series of scalability tests using both container and domain virtualization technologies. As the test results show, the consolidated solution on a single Sun SPARC Enterprise T5440 server exhibited good scalability, providing reasonable response times and high throughput rates for simulated user populations of up to 14,000 users. In Sun SPARC Enterprise servers, Chip Multi-Threading (CMT) technology in UltraSPARC T2 Plus processors enables effective scalability. CMT technology applies the available transistor budget to achieve up to eight cores within a single processor. Each core can switch between threads on a clock cycle, helping to keep the processor pipeline active while lowering power consumption and heat dissipation. Because of the advanced thread density, the Sun SPARC Enterprise T5440 server scales well to provide headroom to support growth while minimizing power use. In the second phase of testing (see Figure 2), Oracle engineers used a clustered configuration of two Sun SPARC Enterprise T5240 servers. Each Sun SPARC Enterprise T5240 server houses two UltraSPARC T2 Plus processors for a maximum of 128 threads per server. In an economical clustered configuration like that used in the Phase 2 testing, two servers support a total of 256. The clustered 4
configuration also demonstrated good scalability, reasonable response times, and high levels of throughput, at the same time enabling highly available Siebel CRM application services. Figure 2. The second phase of testing implemented Oracle Solaris Cluster on two Sun SPARC Enterprise T5240 servers in a consolidated, clustered HA configuration. An Overview of Oracle s Siebel CRM Application Architecture The Oracle Siebel CRM application suite includes the following tiers (see Figure 3): Web Clients. Web Clients provide user interface functionality and can encompass a variety of types (Siebel Web Client, Siebel Wireless Client, Siebel Mobile Web Client, Siebel Handheld Client, etc.). In both phases of testing, Mercury LoadRunner version 8.1 simulated the load generated by the different sized end-user populations. Web Server. This tier processes requests from Web Clients and interfaces to the Gateway/Application layer. In the scalability testing performed, Sun engineers installed the Siebel Web Server Extension and configured the Oracle iplanet Web Server (formerly Sun Java System Web Server) at this tier. Gateway/Application Server. This tier provides services on behalf of Siebel Web Clients. It consists of two sub-layers: the Siebel Enterprise Server and the Siebel Gateway Server. Database Server. While the Siebel File System stores data and physical files used by Siebel Clients and Siebel Enterprise Server, the Siebel Database Server stores Siebel CRM database tables, indexes, and seed data. 5
In a multiple server deployment, the Siebel Enterprise Server includes a logical grouping of Siebel Servers. (However, in a small configuration, the Siebel Enterprise Server might contain a single Siebel Server.) The Siebel Gateway coordinates the Siebel Enterprise Server and its set of Siebel Servers. It also provides a persistent backing store of Siebel Enterprise Server configuration information. Each Siebel Server is a flexible and scalable application server that supports a variety of services such as data integration, workflow, data replication, and synchronization services for mobile clients. The Siebel Server also includes logic and infrastructure for running different CRM modules, as well as providing connectivity to the Database Server. The Siebel Server consists of several multithreaded processes that are commonly known as Siebel Object Managers. Figure 3. This high-level overview of the Oracle Siebel CRM application architecture shows the tiered software architecture. To provide high availability to all three tiers of Oracle Siebel CRM 8, Oracle Solaris Cluster software is deployed to support mission-critical application availability (see Configuring for HA Using Oracle Solaris Cluster Software, page 23). In the second phase of testing, engineers analyzed performance and scalability with Siebel CRM workloads in an HA configuration, using clustered zones to support each software tier. Workload Description CRM systems often require customization typically more frequently than other business applications. Common changes include adding or removing certain application modules, modifying the function of existing modules, or integrating the CRM application with other business applications and processes. While application performance varies according to the particulars of any deployment, testing 6
a configuration s scalability with a well-defined workload helps to provide a useful starting point for defining appropriate configurations and sizing. For the purposes of scalability testing, engineers used a workload extracted from the well-known Siebel Platform Sizing and Performance Program (PSPP) workload. This workload is based on scenarios derived from large Siebel customers and replicates real-world, concurrent, thin-client requirements of typical end-users. The PSPP 8.0 workload is based on user populations who repeatedly perform the following types of tasks and functions: Siebel Financial Services Call Center. The Siebel Financial Services Call Center software provides a comprehensive solution for sales and service, helping customer service and telesales representatives to provide world-class customer support, improve customer loyalty, and increase revenues through cross-selling and up-selling opportunities. Siebel Partner Relationship Management. Representing echannel users in partner organizations, the Siebel Partner Relationship Management application enables organizations to effectively and strategically manage relationships with partners, distributors, resellers, agents, brokers, and dealers. Siebel Workflow. This business process management engine automates user interaction, business processes, and integration. A graphical drag-and-drop user interface allows simple administration and customization. Administrators can add custom or pre-defined business services, specify logical branching, updates, inserts, and subprocesses to create a workflow process tailored to specific business requirements. Siebel escript. escript is a programming language that application developers use to write simple scripts to extend Siebel applications. The JavaScript programming language (a popular scripting language used extensively to deploy Web sites) is the core language underlying the Siebel escript language. Siebel Enterprise Application Integration (EAI). EAI software allows organizations to integrate legacy applications with Siebel CRM applications and to integrate Web Service support. This capability enables organizations to extend the functionality of existing applications to provide up-to-the-minute information through standard Web portals and other Web Service-enabled environments. In Phase 1 of the testing, the PSPP workload simulates the following task mix for the functions listed above: Financial Services Call Center - 30% of active concurrent users Partner Relationship Management, escript, and Workflow - 10% of active concurrent users Enterprise Application Integration with Web Services - 60% of active concurrent users In Phase 2, the test configuration used version 8.1.1 of the Oracle Siebel CRM 8.1.1 software instead of version 8.0. Because of changes to the PSPP workload for 8.1.1 testing (e.g., Partner Relationship is not included in 8.1.1 version of the PSPP), the task mix for Phase 2 changed as follows: Financial Services Call Center - 40% of active concurrent users Enterprise Application Integration with Web Services - 60% of active concurrent users 7
Business Transaction Types Based on the Siebel PSPP benchmark workload described above, Mercury LoadRunner 8.1 generated loads to simulate different user populations simultaneously executing complex business transactions. Between each user operation, think time (a synthetic delay simulating the typical pause between a user s actions) averaged approximately 15 seconds. The following paragraphs characterize core business transaction types used in the testing. Siebel Financial Services Call Center Incoming Call Creates Opportunity, Quote, and Order This transaction simulates the pattern of activity in a typical call center transaction: Create a new contact Create a new Opportunity for that contact Add two products to Opportunity Navigate to Opportunities Quote View Click AutoQuote button to generate quote Enter Quote Name, and Price List Drill down on the quote name to go to Quote Line Items View and specify discount Click Reprice All button Update Opportunity Navigate to Quotes Order View Click on AutoOrder button to automatically generate the order Navigate back to Opportunity Siebel Partner Relationship Management, escript, and Workflow Sales and Service This transaction simulates the steps that occur when entering a partner service request: Partner creates a new service request with appropriate detail A service request is automatically assigned The saving service request invokes scripting that brings the user to the appropriate opportunity screen A new opportunity with detail is created and saved The saving opportunity invokes scripting that brings the user back to the service request screen Web Services Find, Submit a New Service Request, and Update the Service Request This transaction simulates a Web Service that interfaces to a hypothetical legacy application to find or create a service request. The Web Service acts as a delivery mechanism for integrating heterogeneous applications through Internet protocols. A Web Service can be specified using Web Services 8
Description Language (WSDL) and is then transported via Simple Object Access Protocol (SOAP), a transport protocol based on XML. Since the PSPP benchmark suite has no UI presentation layer, the load generator simulates a Java Platform, Enterprise Edition (Java EE) Web application to send a Web Service request to a Siebel Server (EAIObjMgr_enu) to invoke Siebel business services. The Siebel Web Services framework generates WSDL files to describe the Web Services hosted by the Siebel application. Also, this framework can call external Web Services by importing a WSDL document as an external Web Service (using the WSDL import wizard in Siebel Tools). Each Web Service exposes multiple methods, such as Query Service Request, Create Service Request, and Update Service Request. Web Service authentication is done through a session token. The ServerDetermine session type is used and a session token is maintained to avoid a Login process for each request. To use the ServerDetermine session type, a login Web Service call (SessionAccessPing) retrieves the session token before calling other Web Services. At the end of the transaction, a logout call (SessionAccessPing) makes the session token unavailable. Test Environments As noted previously, there were two phases of testing: one to determine scalability on a single Sun SPARC Enterprise T5440 server and another using a clustered configuration of two Sun SPARC Enterprise T5240 servers. These test environments are not representative of typical production deployments but are simplified proof-of-concept configurations designed for test and development. Phase 1 Test Environment Figure 4 depicts the Phase 1 test environment. Figure 4. The Phase 1 test environment consolidated Siebel CRM tiers on a single Sun SPARC Enterprise T5440 server. 9
The Phase 1 test environment consisted of the following hardware and software components: Hardware One Sun SPARC Enterprise T5440 server with four 1.4GHz UltraSPARC T2 Plus processors and 128 GB of RAM Two Oracle Sun Storage J4200 arrays (with SAS drives) or two Oracle StorageTek 2540 arrays Nine Sun Fire X4200 servers from Oracle for load generation Software Oracle Solaris 10 5/08 s10s_u5wos_10 SPARC and Oracle Solaris 10 10/08 s10s_u5wos_10 SPARC Oracle 10g R2 Database Server v10.2.0.3.0 Siebel CRM Release 8.0 Industry Applications Oracle iplanet Web Server (formerly Sun Java" System Web Server) 6.1 SP10 Note that the testing was performed once with two StorageTek 2540 arrays and once with two Sun Storage J4200 arrays. Generally, the workload imposed such a low amount of I/O that the difference in results was negligible. Phase 2 Test Environment Figure 5 shows the Phase 2 test environment. Figure 5. The HA test environment implemented Siebel CRM tiers on two clustered Sun SPARC Enterprise T5240 servers. 10
The second phase of testing used the following hardware and software components: Hardware Two Sun SPARC Enterprise T5240 servers, each with two UltraSPARC T2 Plus processors and 128 GB of RAM Two Oracle Sun Storage 6140 arrays (with SAS drives) Four Sun Fire X2270 servers from Oracle for load generation Software Oracle Solaris 10 u8 SPARC Oracle 11g R2 Database Server Siebel CRM Release 8.1.1 Industry Applications Oracle iplanet Web Server (formerly Sun Java System Web Server) 7.0 Oracle Solaris Cluster 3.2u3 Phase 1 Testing Consolidating Tiers Using Containers and Domains In the first phase of testing, engineers executed three test scenarios: once each with 3,500, 7,000 and 14,000 active users respectively. Table 1 shows the Siebel CRM server configuration for the three user population scenarios. TABLE 1. CONFIGURATION OF SERVICES FOR EACH TEST SCENARIO NUMBER OF NUMBER OF WEB NUMBER OF SIEBEL TOTAL NUMBER OF NUMBER OF ORACLE CONCURRENT SERVERS SERVERS SIEBEL OBJECT DATABASE INSTANCES USERS MANAGERS 3,500 1 1 12 1 7,000 1 1 24 1 14,000 2 2 48 1 During the execution of each scenario, data was collected from the following sources: Unix performance metrics Load Runner (the workload generator software) Oracle Automatic Workload Repository (AWR) A power measurement tool 11
Engineers repeated the same testing scenarios on a single server using different built-in, no-cost virtualization technologies: Oracle Solaris Containers and Oracle VM Server for SPARC (previously known as Sun Logical Domains). The following pages include Phase 1 testing results for configurations using containers (see page 12) and domains (see page 18). Performance and Scalability Results with Oracle Solaris Containers In Phase 1 testing on the Sun SPARC Enterprise T5440 server, Oracle Solaris 10 was first configured with three containers (zones) in addition to the global zone. Each zone was used to isolate a different Siebel CRM tier Web, Gateway/Application, or Database. System resources were dedicated to each tier as indicated in Table 2 (for more information on resource allocation for zones, see Sizing Recommendations, page 39 and Appendix A, Configuration of Containers, page 43). TABLE 2. RESOURCES ALLOCATED TO EACH TIER AND CONTAINER TIER AND CONTAINER VCPUS 1 MEMORY Web tier 22 vcpus 8GB Gateway/Application tier 196 vcpus 88GB Database tier 38 vcpus 32GB 1 A vcpu (virtual CPU) correlates to a processing thread. Since the Sun SPARC Enterprise T5440 server has four UltraSPARC T2 Plus processors with 8 cores and 8 threads per core, there is a maximum possible 256 vcpus per system. The following pages summarize test results for user populations of 3,500, 7,000 and 14,000 active concurrent users with Oracle Solaris Containers, including these metrics: CPU utilization (as a percentage) Memory utilization (in GB) Business transaction throughput (in number of transactions per hour) Average transaction response time (in seconds) Power consumption 12
CPU Utilization (Containers) Figure 6 shows CPU utilization for Siebel CRM Web, Gateway/Application, and Database tiers in separate containers. Table 3 gives the CPU utilization percentage for each tier under each user population load. As shown, additional CPU processing capacity is available, especially for the 3,500 and 7,000 user scenarios. For these user populations in an actual deployment, a single Sun SPARC Enterprise T5440 server can potentially support additional applications using this excess processing capacity, or a smaller server (such as Oracle s Sun SPARC Enterprise T5240 server) could be used. Figure 6. CPU utilization percentage is shown for each tested user population. TABLE 3. CPU UTILIZATION (%) SIEBEL CRM TIER 3,500 USERS 7,000 USERS 14,000 USERS Web Server 10,106 19,866 39,715 Gateway/Application Server 8,306 16,645 33,105 Database Server 31,478 62,845 125,475 13
Memory Utilization (Containers) Figure 7 shows memory utilization for the Siebel CRM tiers running in different containers. Table 4 lists corresponding utilization (in gigabytes). As the data and graph illustrate, in all three population scenarios, memory utilization remains low, which indicates that more than adequate memory resources are configured. Figure 7. Percentage of memory utilization is shown for each tested user population. TABLE 4. MEMORY UTILIZATION (GB) SIEBEL CRM TIER 3,500 USERS 7,000 USERS 14,000 USERS Web Server 1.13 2.05 4.53 Gateway/Application Server 19.00 36.00 73.00 Database Server 12.00 15.00 20.00 14
Business Transaction Throughput (Containers) Figure 8 shows the number of business transactions per hour for the three transaction types under each user population load. Table 5 lists the throughput rates. As the data indicates, as the user population doubles from 3,500 to 7,000 to 14,000 users, throughput increases almost linearly. Figure 8. Business transaction throughput is shown for each user population. TABLE 5. TRANSACTION THROUGHPUT (TRANSACTIONS/HOUR) BUSINESS TRANSACTION TYPE 3,500 USERS 7,000 USERS 14,000 USERS Financial Services Call Center 1.13 2.05 4.53 Partner Relationship Management 19.00 36.00 73.00 EAI Web Services 12.00 15.00 20.00 Average Transaction Response Time (Containers) Figure 9 depicts the average transaction response time for the three transaction types under each user population using containers. Table 6 lists the average response time in seconds for each transaction type. For purposes of the testing exercise, response times are measured at the Web server instead of at the end user. (This is because response times at the end user depend on a number of other variables such as network latency, the bandwidth between Web server and browser, and the time for content rendering by the browser.) 15
Figure 9. Average transaction response time is shown for different workload types and each user population. TABLE 6. AVERAGE TRANSACTION RESPONSE TIME (SECONDS) SIEBEL CRM TIER 3,500 USERS 7,000 USERS 14,000 USERS Financial Services Call Center 0.19 0.23 0.34 Partner Relationship Management 0.30 0.36 0.53 EAI Web Services 0.10 0.12 0.17 Transaction Throughput and Response Time (Containers) Performance and scalability are inextricably linked. For this reason it is important to examine throughput and response time metrics together when analyzing application performance and configuration scalability. As application load increases, response time must remain within acceptable bounds. As a rule of thumb, as the number of concurrent users increases, if there is a linear increase in throughput, then the increase in response times should also be within an acceptable limit. Figure 10 combines transaction throughput and response time for the three transaction types and user population loads. Table 7 lists the corresponding data values. As the data indicates, increases in throughput remain almost linear as user load increases, and response times continue to remain within reasonable, sub-second bounds. 16
Figure 10. Transaction Throughput and Response Times are shown for different workload types and each user population. TABLE 7. TRANSACTION THROUGHPUT (TPH, TRANSACTIONS PER HOUR) AND RESPONSE TIME (RT, IN SECONDS) SIEBEL CRM TIER 3,500 USERS 7,000 USERS 14,000 USERS Financial Services Call Center TPH 10,106 19,866 39,715 Partner Relationship Management - TPH 8,306 16,645 33,105 EAI Web Services -TPH 31,478 62,845 125,475 Financial Services Call Center -RT 0.19 0.23 0.34 Partner Relationship Management - RT 0.30 0.36 0.53 EAI Web Services RT 0.10 0.12 0.17 Power Consumption During the 14,000 concurrent user test at a steady state, the Sun SPARC Enterprise T5440 server consumed an average of 1,276 watts. This translates to one watt of energy spent for every 10.97 users. Given that the Sun SPARC Enterprise T5440 server occupies a total of 4 rack units, the server supports about 3,500 users per rack unit. 17
Performance and Scalability Results with Oracle VM Server for SPARC Like Oracle Solaris Containers, Oracle VM Server for SPARC (formerly known as Logical Domains) allows multiple physical servers to be consolidated into isolated domains on a single server. (For more information about this technology, see the paper Oracle VM Server for SPARC: Enabling A Flexible, Efficient IT Infrastructure. ). In the Phase 1 testing, engineers repeated the testing scenarios on a single server using domains instead of using containers. Each domain was configured with the same system resources that had been configured for each container (resource allocations are summarized below in Table 8 and presented in Appendix B, Configuration of Oracle VM Server for SPARC Domains, page 45). TABLE 8. DOMAIN CONFIGURATIONS TIER AND DOMAIN VCPUS MEMORY Web tier 22 vcpus 8GB Gateway/Application tier 196 vcpus 87.5GB Database tier 38 vcpus 32GB While the Web tier and Gateway/Application tiers resided in separate Guest Domains, the Database tier resided in the Primary Domain. If the database tier had instead been deployed in a Guest Domain, then a minimum of 1 virtual cpu (vcpu) and 512 MB of memory should have been allocated to the Primary Domain. Results for testing Siebel CRM on a single server with domains and user populations of 3,500, 7,000, and 14,000 users are shown in Figure 11 through Figure 14. Table 9 through Table 12 list corresponding data values. The results indicate that, depending on the workload, there can be some additional resource utilization needed to run Siebel CRM applications using domains rather than using Oracle Solaris Containers. CPU Utilization (Domains vs. Containers) Figure 11 shows CPU utilization for Siebel CRM Web, Gateway/Application, and Database tiers in separate domains. Table 9 gives the CPU utilization percentage for each tier under populations of 3,500, 7,000, and 14,000 users. As the data indicates, additional CPU processing capacity is available and tracks closely with utilization with Oracle Solaris Containers, with the exception of the Web tier under 14,000 users. Excess processing capacity on a single Sun SPARC Enterprise T5440 server can potentially support other applications, or a smaller server such as the Sun SPARC Enterprise T5240 server could be used. 18
Figure 11. CPU utilization (%) results compare domains and containers for each tier and user population. TABLE 9. CPU UTILIZATION (%) COMPARING DOMAINS AND CONTAINERS NUMBER OF 3,500 USERS 3,500 USERS 7,000 USERS 7,000 USERS 14,000 USERS 14,000 USERS CONCURRENT USERS (CONTAINER) (DOMAIN) (CONTAINER) (DOMAIN) (CONTAINER) (DOMAIN) Web Server 13.67 15.27 30.65 32.98 78.21 84.64 Gateway/Application Server 10.75 10.94 26.80 24.30 76.29 67.53 Database Server 14.22 9.45 29.60 27.10 71.73 63.66 Memory Utilization (Domains vs. Containers) Figure 12 shows memory utilization with each tier running in a different domain, and Table 10 lists the amount of memory (in gigabytes). As shown, memory utilization with domains is comparable to that with containers. Overall, the testing shows the system is configured with adequate memory resources. 19
Figure 12. Memory utilization (GB) results compare domains and containers for different tiers and user populations. TABLE 10. MEMORY UTILIZATION (GB) COMPARING DOMAINS AND CONTAINERS NUMBER OF 3,500 USERS 3,500 USERS 7,000 USERS 7,000 USERS 14,000 USERS 14,000 USERS CONCURRENT USERS (CONTAINER) (DOMAIN) (CONTAINER) (DOMAIN) (CONTAINER) (DOMAIN) Web Server 1.13 1.52 2.05 2.66 4.53 5.72 Gateway/Application Server 19.00 16.73 36.00 35.15 73.00 69.93 Database Server 12.00 12.02 15.00 15.12 20.00 20.60 Throughput (Domains vs. Containers) Figure 13 illustrates throughput using domains in comparison to containers. Table 11 lists the number of business transactions per hour for the three transaction types under population loads of 3,500, 7,000, and 14,000 users using either containers or domains. With either built-in, no-cost virtualization technology, throughput increases almost linearly as the user population increases. 20
Figure 13. Throughput results compare domains and containers for different workloads and user populations. TABLE 11. THROUGHPUT USING DOMAINS AND SOLARIS CONTAINERS (TRANSACTIONS/HOUR) NUMBER OF 3,500 USERS 3,500 USERS 7,000 USERS 7,000 USERS 14,000 USERS 14,000 USERS CONCURRENT USERS (CONTAINER) (DOMAIN) (CONTAINER) (DOMAIN) (CONTAINER) (DOMAIN) Financial Call Center Services 10,106 10,091 19,866 20,131 39,715 39,004 Partner Relationship Management 8,306 8,325 16,645 16,633 33,105 33,076 EAI Web Services 31,478 31,538 62,845 62,882 125,475 125,219 Response Times (Domains vs. Containers) Figure 14 depicts the average transaction response time for the three transaction types and compares response times with domains and containers. Table 12 lists the average response time in seconds. With either built-in, no-cost virtualization technology, response times remained in the subsecond range. 21
Figure 14. Response time results compare domains and containers for various workloads and populations. TABLE 12. RESPONSE TIMES USING DOMAINS AND CONTAINERS (SECONDS) NUMBER OF 3,500 USERS 3,500 USERS 7,000 USERS 7,000 USERS 14,000 USERS 14,000 USERS CONCURRENT USERS (CONTAINER) (DOMAIN) (CONTAINER) (DOMAIN) (CONTAINER) (DOMAIN) Financial Call Center Services 0.19 0.19 0.23 0.23 0.34 0.36 Partner Relationship Management 0.30 0.28 0.36 0.32 0.53 0.52 EAI Web Services 0.10 0.09 0.12 0.10 0.17 0.16 Phase 2 Testing Implementing HA Highly available (HA) clusters provide nearly continuous access to data and applications by keeping systems running through failures that would normally bring down a single server. In mission-critical clustered systems, no single failure whether it is a hardware, software, or network failure can cause a cluster to fail. Recognizing the need to keep business-critical Siebel CRM applications up and running (and to support disaster planning scenarios), Oracle conducted a second phase of testing using a clustered HA configuration for Siebel CRM 8.1.1 workloads. Oracle s clustering products in particular Oracle Solaris Cluster software enable highly available solutions that can meet stringent business continuity requirements for Siebel CRM deployments. 22
Configuring for HA Using Oracle Solaris Cluster Software A cluster is two or more servers (or nodes) that work together as a single, continuously available system to provide applications, system resources, and data to users. Each cluster node is a fully functional standalone system. However, in a clustered environment, an interconnect bridges the nodes, which work together as a single entity to provide increased availability and performance. The interconnect carries important cluster information (data as well as a heartbeat) that allows cluster nodes to monitor the health of other cluster nodes. High availability using clustered systems is achieved through a combination of both hardware and software. Oracle Solaris Cluster software enables business continuity and global disaster recovery solutions to meet evolving datacenter needs. In a nutshell, the clustering software: Makes use of proven availability and virtualization features in Oracle Solaris 10 and in UltraSPARC processor-based systems, including those in Sun SPARC Enterprise servers Supports an industry-leading portfolio of commercial applications, including Oracle RDBMS, Oracle Siebel CRM, and Web server technologies Is certified with a broad range of storage arrays and SPARC and x64/x86 platforms The most recent release of Oracle Solaris Cluster software implements high availability for consolidated environments that use container or domain virtualization technologies, such as the Siebel CRM proof-of-concept solution described in this paper. Oracle Solaris Cluster software supports Oracle Solaris Containers for fault isolation, security isolation, and resource management. Oracle Solaris Cluster can also help to protect virtualized environments that use Oracle VM Server for SPARC domains, lowering risk for servers that provide multiple application services. When consolidating Siebel CRM tiers in this way, Oracle Solaris Cluster provides high availability agents to monitor components running in different virtualized environments (see Table 13). Available Oracle Solaris Cluster agents include software to support services such as Oracle RDBMS, Siebel services, NFS, DNS, the Oracle iplanet Web Server, the Apache Web Server, and so forth. Oracle Solaris Cluster software provides configuration files and management methods to start, stop, and monitor these application services. TABLE 13. ORACLE SOLARIS CLUSTER AGENTS SOLUTION COMPONENT PROTECTED BY Web Server Oracle Solaris Cluster HA for Oracle iplanet Web Server Siebel Gateway Oracle Solaris Cluster HA for Siebel (resource type: SUNW.sblgtwy) Siebel Server Oracle Solaris Cluster HA for Siebel (resource type: SUNW.sblsrvr) Oracle Database Oracle Solaris Cluster HA for Oracle Database 23
Figure 15 depicts the HA proof-of-concept configuration used as the basis of the Phase 2 testing. The HA configuration uses Oracle Solaris Cluster's Zone Cluster feature to consolidate the entire solution stack on two physical machines by deploying the Web server, Gateway, Application and Database tiers in four separate virtual clusters. Figure 15. Oracle Solaris Cluster can help to deliver highly available Siebel CRM services. Designed as a failover environment, the Web server and Database are deployed on one machine, and the Gateway and Siebel Servers are deployed on the other. This distributes the workload across the two machines. If one machine fails, all services are hosted on the surviving machine. When the failed machine is restored, Oracle Solaris Cluster can automatically restore application distribution across the two machines, or an operator can do it manually. This HA configuration is intended to retain operational capability during any single failure, including hardware faults, with as little user impact as possible. As a result, optimization of the servers is biased for maximum concurrent user performance with sufficient computing power kept in reserve to elegantly facilitate transition to a single server with full operational capability. Using the GUI management tool shown in Figure 16, each virtual cluster is assigned appropriate system resources, and each environment operates independently of the others. Appendix C (see page 46) includes configuration information for the zone clusters. Note that the proof-of-concept 24
configuration, while useful for purposes of this testing, is not necessarily typical of a production Siebel CRM environment. Figure 16. Oracle s Sun Cluster Manager is used to configure and monitor clustered resources for each zone cluster. In conjunction with highly reliable solution components (such as Sun SPARC Enterprise servers, Sun Storage and StorageTek products, and Oracle Solaris), Oracle Solaris Cluster helps to construct HA solutions that can deliver reliable and resilient Siebel CRM application services. Figure 17 illustrates a large-scale deployment environment Gateway and Database services are clustered and redundant Web and Siebel Servers are deployed to achieve high levels of availability. 25
Figure 17. A typical large-scale deployment of clustered servers creates a reliable environment for Oracle Siebel CRM services. Phase 2 Testing Scenarios In the second phase of testing, engineers executed three test scenarios: once each with 2,500, 5,000 and 7,000 active users using an HA configuration and clustered zones defined on the two Sun SPARC Enterprise T5240 servers. Table 14 shows the Siebel CRM server configurations for the three user population scenarios. TABLE 14. CONFIGURATION OF SERVICES FOR HA TESTING NUMBER OF NUMBER OF WEB NUMBER OF SIEBEL TOTAL NUMBER OF SIEBEL NUMBER OF ORACLE CONCURRENT USERS SERVERS SERVERS OBJECT MANAGERS DATABASE INSTANCES 2,500 1 1 10 1 5,000 1 1 20 1 7,000 1 1 28 1 Performance and Scalability Results with Oracle Solaris Cluster In Phase 2 testing, Oracle Solaris 10 on each server was configured with four clustered containers (zones) in addition to the global zone. Each clustered zone isolated a different Siebel CRM tier Web, Gateway, Application, or Database. Table 15 shows how system resources were dedicated to each tier. This design represents a reasonable and likely deployment scenario. 26
TABLE 15. RESOURCES ALLOCATED TO EACH TIER AND CONTAINER IN PHASE 2 TESTING TIER AND CONTAINER VCPUS 2 MEMORY Web tier 16 vcpus 3GB Application tier 70 vcpus 34GB Gateway tier 2 vcpus 1GB Database tier 32 vcpus 24GB 2 Since the Sun SPARC Enterprise T5240 server has two UltraSPARC T2 Plus processors with 8 cores and 8 threads per core, there is a maximum possible 128 vcpus per system, for a total of 256 vcpus in this configuration. (Thus the tested clustered configuration has the same number of vcpus as the single Sun SPARC Enterprise T5440 server used in Phase 1 testing.) 27
In this round of testing, data was also collected from Unix system performance tools, Load Runner (the workload generator software), and Oracle Automatic Workload Repository (AWR). The following pages contain metrics for Phase 2 testing of the HA configuration, including: CPU utilization (as a percentage) Memory utilization (in GB) Business transaction throughput (in number of transactions per hour) Average transaction response time (in seconds) CPU Utilization (Clustered Configuration) Figure 18 shows CPU utilization for Web, Gateway/Application, and Database tiers in clustered zones. Table 16 gives the CPU utilization percentage for each tier under each user population. As shown, CPU utilization scales as the number of users increases, and there is additional compute capacity available to handle peaks in utilization, especially in the small and medium configurations. Figure 18. CPU utilization percentage with an HA configuration is shown for each tested user population. TABLE 16. CPU UTILIZATION (%) SIEBEL CRM TIER 2,500 USERS 5,000 USERS 7,000 USERS Web Server 13 26 39 Gateway/Application Server 18 43 71 Database Server 20 48 71 28
Memory Utilization (Clustered Configuration) Figure 19 shows memory utilization for Siebel CRM tiers deployed in clustered zones. Table 17 lists corresponding utilization (in gigabytes). As the data and graph illustrate, in all three population scenarios, memory utilization remains low, indicating that more than adequate memory resources are configured. (Note that each Sun SPARC Enterprise T5240 server can support up to a maximum of 256GB.) Figure 19. Memory utilization in the HA configuration is given in gigabytes for each tested user population. TABLE 17. MEMORY UTILIZATION (GB) SIEBEL CRM TIER 2,500 USERS 5,000 USERS 7,000 USERS Web Server 0.58 0.88 1.11 Gateway/Application Server 11.4 21.9 29.77 Database Server 12.5 17 23 Business Transaction Throughput (Clustered Configuration) Figure 20 shows the number of business transactions per hour for the three transaction types under each user population load. Table 18 lists the throughput rates. As the user population increases from 2,500 to 5,000 to 7,000 users, throughput increases almost linearly. 29
Figure 20. Business transaction throughput with an HA configuration is shown for each user population. TABLE 18. TRANSACTION THROUGHPUT (TRANSACTIONS/HOUR) BUSINESS TRANSACTION TYPE 2,500 USERS 5,000 USERS 7,000 USERS Financial Services Call Center 9477 18993 26242 EAI Web Services 22469 45395 62687 Average Transaction Response Time (Clustered Configuration) Figure 21 depicts the average transaction response time for the three transaction types under each user population using containers. Table 19 lists the average response time in seconds for each transaction type. For purposes of the testing exercise, response times are measured at the Web server instead of at the end user. (This is because response times at the end user depend on a number of other variables such as network latency, the bandwidth between Web server and browser, and the time for content rendering by the browser.) 30
Figure 21. Average transaction response time (given an HA configuration) is shown for different workload types and each user population. TABLE 19. AVERAGE TRANSACTION RESPONSE TIME (SECONDS) SIEBEL CRM TIER 2,500 USERS 5,000 USERS 7,000 USERS Financial Services Call Center 0.22 0.26 0.32 EAI Web Services 0.12 0.14 0.16 Transaction Throughput and Response Time (Clustered Configuration) Performance and scalability are inextricably linked. For this reason it is important to examine throughput and response time metrics together when analyzing application performance and configuration scalability. As application load increases, response time must remain within acceptable bounds. As a rule of thumb, as the number of concurrent users increases, if there is a linear increase in throughput, then the increase in response times should also be within an acceptable limit. Figure 22 combines transaction throughput and response time for the three transaction types and user population loads. Table 20 lists the corresponding data values. As the data indicates, increases in throughput remain almost linear as user load increases, and response times continue to remain within reasonable, sub-second bounds. 31
Figure 22. Throughput and response times (given an HA configuration) are shown for different workload types and user populations. TABLE 20. TRANSACTION THROUGHPUT (TPH, TRANSACTIONS PER HOUR) AND RESPONSE TIME (RT, IN SECONDS) SIEBEL CRM TIER 2,500 USERS 5,000 USERS 7,000 USERS Financial Services Call Center TPH 9477 18993 26242 EAI Web Services TPH 22469 45395 62687 Financial Services Call Center RT 0.22 0.26 0.32 EAI Web Services RT 0.12 0.14 0.16 Power Consumption (Clustered Configuration) During the Phase 2 testing of the HA configuration, power consumption was not explicitly measured. Estimated power consumption for a Sun SPARC Enterprise T5240 server supporting 7000 concurrent Siebel users is around 778 watts, which is approximately 8.9 users per watt. Failover Testing with Oracle Solaris Cluster In addition to performance and scalability testing, Oracle engineers conducted failover testing. Using the same Phase 2 test configuration shown in Figure 15 (page 24), in which one server node hosts primary instances of Web and Database services while a second node hosts primary instances of Gateway and Seibel servers, Oracle engineers conducted four separate failover tests. The failover tests executed under a workload simulating 1000 concurrent users (40% Financial and 60% EAI) and consisted of these four scenarios: Failover of the primary Gateway server on node 2. After the simulated workload reached 1000 active users, engineers killed all processes associated with the Gateway server on node 1. As a result, Oracle 32
Solaris Cluster restarted the Gateway resource group on node 2. Once the Gateway server came online, workload generation resumed. Throughput and response time were measured to examine whether these metrics were consistent both before and after the failover. Reboot of the primary Web server on node 1. With 1000 simulated concurrent users, engineers rebooted the zone cluster on node 1 supporting the Web server. Oracle Solaris Cluster then failed over the Web server resource group to the second node. Once the Web server came online, the workload simulator resumed load generation and engineers measured throughput and response time to determine consistency before and after the fault. Reboot of the Database server instance on node 1. After the simulated workload reached 1000 active users, engineers rebooted the zone cluster on node 1 with the Database server. Oracle Solaris Cluster failed over the Database server resource group to the second node. Once the Database server came online, workload generation resumed. Throughput and response time were measured to determine consistency before and after the failover. Complete power loss of node 2. In this scenario, after the simulated workload reached 1000 users, engineers powered off node 2 via the server s built-in service processor. In response, Oracle Solaris Cluster restarted the Gateway and Siebel Server resource groups on node 1. Again, throughput and response time were measured for consistency before and after the node failure. For purposes of this test, a simplified 1000-user workload in a single pass was used rather than a more burdensome load. This was done to produce a cogent, representative sample of results rather than a range of results that would differ very little (if at all) with increased workload. Concurrent users in this configuration have very little impact on failure detection or recovery times. In all four scenarios, throughput and response times were consistent before and after failover. Table 21 shows metrics for the 1000-user workload, including baseline values measured prior to testing. TABLE 21. TRANSACTION THROUGHPUT AND RESPONSE TIME IN FAILOVER SCENARIOS FAILOVER TEST SCENARIO # USERS THROUGHPUT (TPH) RESPONSE TIME (SEC) DETECTION (D) AND RECOVERY (R) TIMES Baseline 400 Financial 3791 0.21 N/A (All tiers, nodes 1 and 2) 600 EAI 8999 0.11 Failover of primary Gateway 400 Financial 3793 0.21 Gateway: D = 1s, R = 1mn17s server on node 2 600 EAI 8980 0.11 Siebel: R = 26s Total stack: D+R = 1mn44s Failover of primary Web server 400 Financial 3777 0.21 Web: D = 14s, R = 1mn57s on node 1 600 EAI 9052 0.11 Total: D+R = 2mn11s Failover of primary Database 400 Financial 3793 0.21 Database: D = 17s, R = 1mn1s server on node 1 600 EAI 8971 0.12 Total: D+R = 1mn18s 33