31.1 About Capacity Planning for Oracle Fusion Middleware While performance tuning can be defined as optimizing your existing system for better performance, capacity planning determines what your system needs (and when it needs it) to maintain performance in both steady-state and peak usage periods. Capacity Planning involves designing your solution and testing the configuration, as well as identifying business expectations, periodic fluctuations in demand, and application constraints. You need to plan carefully, test methodically, and incorporate design principles that focus on performance.
Before deploying any application into a production environment, the application should be put through a rigorous performance testing cycle. Creating an effective Capacity Management plan includes some of the same steps as performance planning:. Step 1:. Step 2:. Step 3:. Step 4. Table 31-1 Capacity Planning Factors to Consider Capacity Planning Questions For more information see, What are your performance goals and objectives?
How many users need to run simultaneously (concurrently?) Is the simulated workload adequate? (Is the workload likely to increase?) Is the Oracle Fusion Middleware deployment configured to support clustering and other high availability factors? Does the hardware meet the configuration requirements? Do you have adequate JVMs to support your users? Is the database a limiting factor? 31.2 Determining Performance Goals and Objectives The first step in creating an effective capacity management plan is to determine your network load and performance objectives.
You need to understand the applications deployed and the environmental constraints placed on the system. Ideally you have information about the levels of activity that components of the application are expected to meet, such as:. The anticipated number of users.
The number of concurrent sessions. The number of SSL connections required. The number and size of requests. Libro administracion una perspectiva global 12 edicion pdf gratis.
The amount of data and its consistency. Determining your target CPU utilization. Performance objectives are limited by constraints, such as.
The configuration of hardware and software such as CPU type, disk size versus disk speed, sufficient memory. The ability to interoperate between domains, use legacy systems, support legacy data. The security requirements and use of SSL. SSL involves intensive computing operations and supporting the cryptography operations in the SSL protocol can impact the performance of the WebLogic Server.
Development, implementation, and maintenance costs. You can use this information to set realistic performance objectives for your application environment, such as response times, throughput, and load on specific hardware. 31.4 Identifying Bottlenecks in Your System Bottlenecks, or areas of marked performance degradation, should be addressed while developing your capacity management plan. If possible, profile your applications to pinpoint bottlenecks and improve application performance. Oracle provides the following profilers:.
Oracle Jrockit Mission Control provides profiling capabilities for processes using Jrockit JVM. Oracle Application Diagnostics provides profiling capabilities for java processing using SUN JDK. The objective of identifying bottlenecks is to meet your performance goals, not eliminate all bottlenecks. Resources within a system are finite. By definition, at least one resource (CPU, memory, or I/O) can be a bottleneck in the system. Planning for anticipated peak usage, for example, may help minimize the impact of bottlenecks on your performance objectives. There are several ways to address system bottlenecks.
Some common solutions include:. 31.4.4 Increasing Memory or CPU Aggregating more memory and/or CPU on a single hardware resource allows localized communication between the instances sharing the same hardware. More physical memory and processing power on a single machine enables the JVMs to scale and run much larger and more powerful instances, especially 64-bit JVMs. Large JVMs tend to use the memory more efficiently, and Garbage Collections tend to occur less frequently. In some cases, adding more CPU means that the machine can have more instruction and data cache available to the processing units, which means even higher processing efficiency. See for more information.
31.4.6 Segregation of Processes and Hardware Interrupt Handlers When planning for the capacity that a specific hardware resource can handle, it is important to understand that the operating system may not be able to efficiently schedule the JVM processes as well as other system processes and hardware interrupt handlers. The JVM may experience performance impacts if it shares even a few of its CPU cores with the hardware interrupt handlers. For example, disk and network-intensive applications may induce performance impacts that are disproportionate to the load experienced by the CPU.
In addition, hardware interrupts can prevent the active Java threads from reaching a 'GC-safe point' efficiently. Separating frequent hardware interrupt handlers from the CPUs running the JVM process can reduce the wait for Garbage Collections to start. It may also be beneficial to dedicate sibling CPUs on a multi-core machine to a single JVM to increase the efficiency of its CPU cache. If multiple processes have to share the CPU, the data and instruction cache can be contaminated with the data and instructions from both processes, thus reducing the amount of the cache used effectively. Assigning the processes to specific CPU cores, however, can make it impossible to use other CPU cores during peak load bursts. The capacity management plan should include a determination on whether the CPUs should be used more efficiently for the nominal load, or should there be some extra capacity for a burst of activity.
31.5 Implementing a Capacity Management Plan Once you have defined your performance objectives, measured your workload, and identified any bottlenecks, you must create and implement a capacity management plan. The goal of your plan should be to meet or exceed your performance objectives (especially during peak usage periods) and to allow for future workload increases. To achieve your performance objectives, you must implement your management plan and then continuously monitor the performance metrics as discussed in. Since no two deployments are identical, its virtually impossible to illustrate how a capacity management plan would be implemented for all configurations.
Capacity planning is an iterative process and your plan must be calibrated as changes in your workload or environment change. The following section provides key factors that should be addressed in the plan. 31.5.1 Hardware Configuration Requirements There is no single formula for determining your hardware requirements. The process of determining what type of hardware and software configuration involves assessment of your system performance goals and an understanding of your application.
Capacity planning for server hardware should focus on maximum performance requirements. The hardware requirements you have today are likely to change.
Your plan should allow for workload increases, environment changes (such as added servers or 3rd party services), software upgrades (operating systems, middleware or other applications), network connectivity and network protocols. 31.5.1.1 CPU Requirements Your target CPU usage should not be 100%, you should determine a target CPU utilization based on your application needs, including CPU cycles for peak usage. If your CPU utilization is optimized at 100% during normal load hours, you have no capacity to handle a peak load.
In applications that are latency sensitive and maintaining the ability for a fast response time is important, high CPU usage (approaching 100% utilization) can reduce response times while throughput stays constant or even increases because of work queuing up in the server. For such applications, a 70% - 80% CPU utilization recommended. A good target for non-latency sensitive applications is about 90%. 31.5.1.2 Memory Requirements Memory requirements are determined by the optimal heap size for the applications you are going to use, for each JVM co-located on the same hardware. Each JVM needs up to 500MB in addition to the optimal heap size; the actual impact to performance depends on the JVM brand, and on the type of application being run.
Infrastructure Capacity Planning Spreadsheet
For example, applications with more Java classes loaded need more space for compiled classes. 32-bit JVMs normally cannot exceed a limit of approximately 3GB on some architecture when a limit is imposed by the hardware architecture and the Operating System. It is recommended to reserve some memory for the Operating System, IO buffers and shared-memory devices. 31.5.4 Database Configuration To maintain sustained performance, you must ensure that your existing database can scale with the increases in capacity planned for the application server tier. Tuning the database parameters and monitoring database metrics during peak usage, can help you determine if the existing database resources can scale to handle increased loads.
You may need to add additional memory or upgrade the database hardware configuration. For more information on tuning an Oracle database, see the.
In some cases, however, you may find that the database is still not able to effectively manage increases in load, even after increasing the memory or upgrading the CPU. In these situations, consider deploying an Oracle Real Application Cluster (Oracle RAC) environment to handle the increases. Oracle RAC configurations not only provide enhanced performance, but they can also improve reliability and scalability. For more information on Oracle RAC, see. Scripting on this page enhances content navigation, but does not change the content in any way.
Project Management Template for Excel's name says it all: It adds a basic but flexible project management tool to Microsoft Excel, which is exactly where you need it. This Excel template from Business Spreadsheets is free to try for 30 days. It runs in Windows NT to 8 (including Server) and requires Microsoft Excel 97 or better. Installing Project Management Template is extremely easy: Extract the zipped download and click it to open it in Excel. The first sheet, Resources, displays four modules: Project Parameter, Project Resources, Human Resources, and Control Panel. We entered a name, date, and preliminary deadline for our yet-undefined project.
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The script content on this page is for navigation purposes only and does not alter the content in any way. 2 Capacity Planning Capacity planning is the process of determining which server hardware will best support an Oracle Access Manager deployment based on anticipated usage. The information in this chapter provides a basis for capacity planning that helps ensure that the server hardware in an Oracle Access Manager deployment is adequate for handling peak loads. This chapter includes the following topics:. For a general overview of Oracle Access Manager deployments, see.
2.2 Estimating the Anticipated Peak System Load for Server Sizing Appropriate server sizing should ensure that your server hardware can handle the maximum number of operations that can be expected in a particular time interval. Put another way, the server hardware in your Oracle Access Manager deployment should accommodate all users during times of peak load. Information about the peak load for a given time interval can usually be obtained from:. Measurements from live systems in use or historical data. Calculations and projections Oracle Access Manager is a stateless system. Therefore, the estimated maximum transaction throughput and network traffic are critical factors in capacity planning.
This section includes the following topics:. 2.2.1 Measuring the Load This discussion describes two methods that you can use to measure the load during peak hours in an Oracle Access Manager deployment. From this information, you can estimate your overall system-capacity requirements. You can compare your load estimates (transactions-per-user-per-second) with your equipment manufacturer's specifications for server hardware. Based on these comparisons, you can determine if the machines you already have are adequate for supporting the estimated load. If existing machines are not adequate, you can base your equipment choices in part on your own throughput requirements. There are numerous network traffic and Web site usage monitoring tools available for use with the methods described here.
However, use of third-party tools is outside the scope of this book. This discussion includes the following topics:. 2.2.1.1 Measuring the Load in a Deployment Measuring the load includes establishing the highest number of pages and requests per second over a given time interval. This provides you with a good idea about your overall system-capacity requirements. While you can measure usage over as little as a 24-hour period, Oracle recommends that you measure usage over a period of several weeks. If usage tends to spike during particular weeks of the year, try to obtain measurements from the busiest weeks. From this, you can better extract system-capacity requirements that will hold true even in the busiest period.
To estimate a typical busy load, you multiply the value of an average heavy load by a small integer such as 2 or 3. This allows for usage patterns that are two or three standard deviations higher than an average heavy load, assuming a Gaussian distribution (bell curve) of loads.
To base your estimate on the peak load for the deployment. Measure usage over a significant period of time to obtain measurements from the busiest period. Choose the highest value seen in a production deployment to use during the next step. Estimate the parameters of a typical busy load by multiplying the value of an average heavy load by a small integer such as 2 or 3. Another method that you can use is to measure the active user sessions in a multi-site deployment, as described next.
Note: To ensure accuracy using this method, the actual user request rate during peak hours should come from either monitoring the live system in use, or from historical data. A table such as allows you to estimate the times when the majority of the users on each site are busiest (the shaded area).
Each column reflects an hour of the day (local time) that is recorded based on Greenwich Mean Time (GMT). Each row represents the number of logged-in users that were monitored at that hour. According to the example, usage in Mexico City typically starts at GMT 12 and continues through GMT 24.
The peak in Mexico City occurs between GMT 16 through 19. Note: Mexico City, Mexico is 6 hours behind GMT. Therefore, when it is 6:00:00 PM on Tuesday, February 6, 2007 in Mexico City, GMT is 00:00:00 on Wednesday, February 7, 2007. Greenwich Mean Time (GMT) or World Time is also known as Universal Time Coordinated (UTC). GMT, World, and UTC time reflects the mean solar time along the Earth's prime meridian. The prime meridian is arbitrarily based on the meridian that runs through the Greenwich Observatory outside of London, England, where the present system originated.
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UTC is also known as Coordinated Universal Time and sometimes as Universal Coordinated Time; all are abbreviated as UTC and refer to the standard time common to every place in the world (formerly and still widely referred to as Greenwich Mean Time or World Time. Table 2-1 Peak Load Based on Estimated Usage Across Sites Peak hours GMT 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Mexico 0 0 0 3 75 150 175 225 225 225 225 225 225 225 225 175 Spain 35 50 75 50 25 35 50 75 75 75 75 35 20 0 0 0 Egypt 45 45 50 50 50 50 50 55 55 45 30 10 0 0 0 0 U.S. 2.2.2 Projecting System Usage When there is no simple way to measure usage, or when there is no historical data available, you can use the method described here to project the system usage in an Oracle Access Manager deployment.
Projected Number of Users: Obtain data on the number of users per office from the Human Resources department, or some other authoritative source in your enterprise. From this you can estimate the total number of users accessing resources during peak hours. Be sure to include full-time employees, part-time employees, and contractors in your estimate. Projected Time Intervals in a Geographically Distributed Deployment: Rather than trying to gather statistics on who is logged in at each site, it may be more practical to identify peak usage time intervals in a geographically distributed deployment and estimate the number of users who are active during those intervals. For instance, suppose your company has offices in several countries worldwide.
You can create a chart of regular office hours plotted against Greenwich Mean Time (GMT). A table such as allows you to estimate the times when the majority of the offices are busiest (the shaded area), which is a good indication of peak load hours. In, the number 1 within each row indicates 12:00 00AM local time as well as its relationship to GMT.
For example, with daylight savings time in effect 12:00AM in Mexico City occurs at 07:00 AM GMT. Table 2-2 Chart of Office Hours Plotted Against GMT with Daylight Savings Time GMT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Mexico 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Spain 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 Egypt 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 U.S.
20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Columbia 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Costa Rica 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Indonesia 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 Taiwan 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 A table such as provides user community statistics per office, from which you can estimate the total number of users accessing resources protected by Oracle Access Manager during peak hours. Table 2-3 Employee and Contractor Data from an Authoritative Source in Your Company Country Full time Part time Contract Total Mexico 3021 4 Spain 755 356 5 1116 Egypt 329 275 0 604 U.S. 134 25 55 214 Columbia 1290 2 Costa Rica 175 130 0 305 Indonesia 250 97 18 365 Taiwan 286 88 44 418 You can create a simple throughput projection based on:. An estimate of the total maximum number of users that you expect to be logged in at the same time. The estimated number of transactions per user for a given time period.
The estimated transactions-per-second that need to be supported You can compare your transactions-per-second estimates with claims made by your hardware vendors. For example, using information in the and as a sample, you can project the total users and peak load as follows:. Total Maximum Users: Add together the total number of users on each site. Using the data in as a sample: 3552+1116+604+214+1546+305+365+418=8120 total users. Peak Load: This calculation is similar to the one described in where you multiply the total users by the estimated requests per minute (4 is a good number unless you have better data) then divide the requests per minute by 60 seconds to establish the requests per second. Total maximum users.
Requests per minute / 60 seconds = Total requests per second Using the data in as a sample:, you can project: 8120 total users. 4 requests per minute = 32,480 / 60 seconds = 541 requests per second Based on your human resources data, this is the maximum possible load. Therefore, you do not need to multiply the peak load to take account into the Gaussian distribution of loads.
However, you may want consider some safety factors that project the future growth in the system usage. To project system usage.
Create a chart of hours plotted against GMT for your deployment, using the sample as a guide. List each GMT hour in a separate column across the top of your table.
Create a row for each office site in your deployment in the left column of the table. For each site, add a number from 1-24 to indicate local time as it corresponds to the site's offset from GMT using as a guide.
Determine the hours of peak usage for each site in the table and highlight those. Create a table template for employee and contractor data for each site using the sample as a guide. Obtain the number of users per site from your Human Resources department or another authoritative source in your enterprise.
For each site in your table, enter the number of: Full time employees Part-time employees Contractors. Calculate and enter the total number of users for each site. Add the total for each site to estimate of the maximum number of users that could be logged in at the same time. Estimate of the maximum load using the calculation below: Maximum total users.
Requests per minute / 60 seconds = Total requests per second. Consider adding in some safety factors to project future growth in system usage. 2.3.1 Identity and Access Server Recommendations As discussed in, Oracle recommends installing only one Identity or Access server instance per dedicated machine because 85% utilization is considered a fairly standard load. If you anticipate that the combined utilization for a pair of primary Identity and Access Servers is below 85%, you could consider the following option to help reduce the required hardware estimate:. One host is installed with a primary Identity Server and a secondary Access Server.
A different host is installed with both a primary Access Server and a secondary Identity Server The primary server handles the load unless there is a failover situation. During failover, the secondary server handles the load. If the secondary server becomes activate the utilization of both servers should add up within 85%.
If the combined load is projected to be much greater, Oracle recommends that you install the secondary Identity or Access Server on a different machine. 2.3.3.1 Access Server Recommendations Depending on the load, Access Servers can reach both high CPU and memory utilization. This is critical when planning for the number of Access Servers in your deployment. System configuration and deployment scenarios also affect performance significantly and are critical factors in capacity planning. For example, the following items are will impact your capacity planning and sizing calculations, as well as your performance tuning decisions. Whether you have the following functions configured to be on versus off will depend on the requirements of your enterprise:.
The transport security mode between each tier (whether open or SSL-enabled). Auditing on. User and group caches. The policy cache.
User credential caching. Password policy The describes transport security modes. For performance tuning recommendations, see. For more information about the features above, see and. 2.3.3.3 WebGate Impact on Web Server Performance A WebGate may be installed against an existing Web server instance or a new Web server instance.
The number Web server/WebGate pairs depends on usage patterns within the deployment. There is some degradation to baseline Web server performance (about 10% to 20%) when hosting a WebGate. Based on this estimate, Oracle recommends that you add one additional Web server/WebGate pair for every five Web server/WebGate pairs in the deployment. Put another way, to provide the same Web server performance, you should add a sixth Web server/WebGate pair for every five Web server/WebGate pairs. For details about Web server requirements, see the. 2.4.1 Scale-Up Characteristics Scaling up refers to adding additional processors to your deployment to increase capacity. The results of this test show a fairly nice linear scale up of throughput versus the number of processors.
Shows the Oracle Access Manager scale-up characteristics in a 4-CPU Wintel system. Oracle Access Manager benefits from Hyper-Threading (simultaneous multithreading on the Pentium 4 microarchitecture).
With Hyper-Threading enabled, a Pentium 4 processor is treated by the operating system as two processors instead of one. In, you can plot the throughput ratio of a scale-up test in a 4 CPU system.
Figure 2-1 Oracle Access Manager Scale Up (4 x 2.2 Ghz CPU) In the bar chart shown in, throughput-ratio data values are shown along the vertical y-axis. The horizontal x-axis provides the following category data:. P represents the number of processors. L represents the number of logical processors for the Hyper-Threading equipped processors. For the same Hyper-Threading equipped processors, with Hyper-Threading turned on the system shows 2 logical processors; if Hyper-Threading is turned off, the system shows 1 logical processor. 1P+1L and 2P+2L data show the performance with Hyper-Threading turned off. 1P+2L, 2P+4L, 3P+6L, and 4P+8L data show the performance with Hyper-Threading turned on.
The throughput ratio of from 1 to 3.4 is calculated by dividing the throughput of each test by the throughput of the 1P+1L results. Therefore, the 1P+1L data is 1. 2.4.2 Scale-Out Characteristics Scaling out, also known as scaling out horizontally, refers to adding more physical servers to increase the capacity in your deployment. Shows Oracle Access Manager scale-out characteristics based on a series of tests that run with from one to four identical Wintel servers. In this series of tests each Oracle Access Manager server was driven to 100% CPU, and then the throughput was measured. You can see an almost straight linear increase ion throughput as the number of servers increase. Table 2-4 Configuration Settings for Performance Tests Config # Transport Security: Web Components to Identity and Access Servers Identity Server: Password and Lost Password Policy Auditing of Identity and Access Servers User Login User Credential Caching on Access Server Config 1 Simple or Third-Party Certificate Enabled On Form-based On Config 2 Simple or Third-Party Certificate Enabled Off Basic-Over-LDAP On Config 3 Simple or Third-Party Certificate Disabled Off Basic-Over-LDAP On Config 4 Open Disabled Off Basic-Over-LDAP On.
2.4.4 Baseline Performance for Identity and Access Servers This discussion illustrates the results of a baseline performance test. You can use the baseline ratios here in combination with details in and to estimate possible server (or server cluster) throughput and capacity. For this test, 1 million users were loaded into a Sun Java System directory server v 5.2 in a joint Oracle Access Manager Identity and Access System deployment. During this test there were 100,000 active user sessions on the Identity and Access Servers. This deployment configuration includes:.
Either Simple or third party certificate to secure communication between Web server components and Identity and Access Servers. On the Identity Server, password and lost password policy are configured and enabled as follows:.
Password policy requires uppercase, number, and lowercase with account lock out after three wrong tries. Lost password challenge is set, password histories are stored for three tries. Auditing to file is turned on for the Identity and Access Servers.
Form-based login is configured; required credentials are Username and password. User credential caching in the Access Server is turned on. Baseline throughput numbers were obtained from the test setup described above. The results of this test are shown in. The numbers are normalized, and equivalent to the 1P+2L server data shown in. 2.5 Oracle Access Manager Reference Server Footprint This discussion provides details based on use cases created by Oracle to establish a point of reference for Oracle Access Manager components in live deployments of varying size.
A small scale deployment can be estimated as up to 20,000 users. A small-to-medium-sized deployment typically includes up to 100,000 users.
Large scale deployments include from 100,000 to 2,000,000 users. Oracle Access Manager components perform well on standard hardware.
Based on tests with various configurations (Config4 shows the highest memory consumption), you may find that:. 100,000 active user sessions may consume from 600 MB to 1 GB of memory on an Access Server. With 32-bit systems, the Operating System can usually support up to 2 GB of memory per process The following topics provide more information about the hardware for Oracle Access Manager deployment sizes:. 2.5.1 Hardware for Small-to-Medium Deployments For small to medium-sized deployments with between 20,000 and 100,000 users, any supported server-class computer and operating system should be adequate for the Identity and Access Server. The following items should be taken into account:. Failover requirements double the number of machines needed.
For example, expect to use a minimum of two Identity Servers and two Access Servers for redundancy. When the load is light, these servers may be deployed in a cross-over configuration to optimize hardware utilization. For more information, see. Sever Sizing.
Oracle Capacity Planning And Sizing Spreadsheets Cracking
A minimum of 2 GB of memory is needed for each Identity and Access System process. A minimum of a 2 x 2 GHz CPU system with 4 to 6 GB of memory for each server. A single dedicated Web server is required for the System Console. This may be deployed on either of the two main Access and Identity Servers to eliminate additional hardware requirements. For more information about Oracle Access Manager requirements, Web server requirements, and how to prepare for and install components, see the. 2.5.2 Hardware for Large Deployments For deployments of 100,000-2,000,000 users, Oracle recommends the following hardware for Identity and Access Servers:. Each server should be a 4 x 2 GHz CPU system, or a 2 x 2 GHz system, with 6-8 GB of memory and 2 x 17 GB of mirrored storage.
A cluster setup is preferred. Deplopy the Identity Server in a Delegated Administration model as described in the For a multi-million user deployment, Oracle recommends that you scale-out with multi-servers using guidelines. Use to establish your sizing requirements. For more information about Oracle Access Manager requirements, Web server requirements, and how to prepare for and install components, see the. 2.6 Considerations for the LDAP Directory Server In addition to the Lightweight Directory Access Protocol (LDAP) directory server considerations discussed in, Oracle recommends that you consider using multiple LDAP directory servers to balance the load if there are significant update operations that involve the Identity Server (or Identity and Access Servers in a joint deployment). This is especially true for password policy-related operations.
In such cases, a multi-master and replica for the LDAP server allows you to configure load balancing between the Identity Server and the LDAP replica (as well as between the Access Server and LDAP replica in a joint Identity and Access System deployment). When authenticating against the LDAP directory, consider enabling Access Server's user credential caching. This can significantly lower the load on the LDAP directory server during authentication, and improve throughput. For more information, see.
Disk Space Sizing: A general rule of thumb is to multiply your LDIF file size by 5 to establish the actual LDAP disk space needed for entries and the index. During LDAP initialization, the host computer needs at least the same amount temporary disk space as the LDIF file size multiplied by 5. Each backup copy of your LDAP content requires the same amount of disk space as that of the live LDAP server.
For example, an LDIF file for 1 million users is approximately 2 GB in size. When loaded, a 2 GB LDIF file expands to approximately 10 GB. When you take into account the temporary space during initialization, and space required for three full backup copies, you need at least 2 x 5 x 5 = 50 GB disk space: LDIF size. 5 = Expanded LDIF size Total LDIF. 2 = LDIF and Temporary space Total LDIF. 3 = Backup space Expanded LDIF. 2 + Backup space = Total Disk Space For example: 2 GB.
5 = 10 GB. 2 = 20 GB + 30 = 50 GB Memory Sizing: This depends on the LDAP server and the deployment configuration you choose. Oracle recommends that you review the LDAP documentation for your specific directory server and Operating System. For 32-bit systems, the Operating System may impose a 2 GB limitation per process. For large-scale deployment, a 64-bit system and Operating System are preferred. For more information, see:.
2.6.1 LDAP Server Requirements For Small to Medium Deployments Most currently available directory servers with 2 GB of memory may be adequate for small to medium sized deployments of up to 100,000 users. In addition, Oracle recommends that you:. Use disks that are RAID 01 (striped and mirrored) for the directory server. Use two replicated directory servers for failover. Have enough memory to hold two times the LDAP data in memory. Have an LDAP cache that is large enough to hold the entire database.
Have available 5 GB of disk storage per directory server. 2.6.2 LDAP Server Requirements For Large Deployments For deployments of 100,000 and more users Oracle makes the following recommendations:. Allow 100 GB of disk storage for 2 million users. Segregate physical drives for directory server access and error logs from the attribute data (indexed or not) volume drives For example, Oracle Internet Directory I/O Subsystem Requirements include arrays of disk drives controlled by disk controllers. It is important to consider performance requirements when you size the I/O subsystem rather than using sizing estimates based only on storage requirements. For more information, see the Oracle Application Server Best Practices Guide. Allow CPUs 2 or 4 x 2 GHz: If Access Server user credential caching is configured and on, you can significantly lower the LDAP server load and boost performance.
Allocate at least 2 GB of RAM for a 100,000 user directory; at least 4 GB for a very large directory. Table 2-6 Server Installation Details Number of Servers Installed With 5 Oracle HTTP Server/WebPass/WebGate 2 Access & Identity Server 2 LDAP Server 2 LR Test Load Drivers For the baseline test deployment quoted in this chapter:. The servers (Sun Java System Directory Server, Oracle HTTP Server and Oracle Access Manager), are running on a Windows 2003 Server (SP1).
Each server has 2x2.8 GHz CPUs and 6 GB of RAM, with 140 GB of disk space. Access Server and Identity Servers are co-located and configured as a primary-secondary setup. One LDAP directory server is used. WebPass and WebGate are installed on each of five Oracle HTTP Servers. The Policy Manager is installed on one of the Oracle HTTP Server systems You can scale this deployment as follows:. Increase the number of Oracle Access Manager server hosts to spread the load and offer higher redundancy. Increase capacity by increasing the amount of CPUs, memory, and disk space to improve server performance.
The LDAP directory server supports the addition of new servers for cases where this is desired to improve performance and reliability. For more information on scaling a deployment, see and.
2.8.1.2 Lost Password Test Case For this test case, Oracle configured a password policy that accounts for the syntax and history of the password. 15% of logged in users will try to change their password. Changing a password should succeed approximately 80% of the time. In the remaining 20% of cases, the password may be either too short, too weak, or repeated. For example after a user incorrectly enters his or her password when asked for his or her challenges, and then they go into the password change screen.
For the lost password screen, 10% of wrong user ID entered errors, and 10% of wrong challenges/response, and then 80% of the cases could make it to the next screen (change password). 2.8.3 Integrated Baseline Performance Test Case All above test scenarios were run together with the mix ratios specified in each scenario.
This is a more realistic test case that represents the real-life usage of Oracle Access Manager. If both the Access Server and Identity Server are to be deployed, Oracle recommendeds that you size the system starting with this use case. Footnote Legend Footnote 1: Wintel is an industry term for personal computers that are based on the Intel microprocessor with one of the Microsoft Windows operating systems.
Footnote 2: Intel's trademark for their implementation of this technology is officially called Hyper-Threading Technology (HTT).
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