2 Capacity Planning. The goal of capacity planning for an Oracle Access Manager deployment is to. To help you determine the capacity and sizing needs of. Found 7 results for Oracle Capacity Planning And Sizing Spreadsheets. Full version downloads available, all hosted on high speed servers! Download servers online: 7. Found 7 results for Oracle Capacity Planning And Sizing Spreadsheets. Full version downloads available, all hosted on high speed servers! Download servers online: 7.

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. Xex Menu Live.

This discussion includes the following topics: • •. 2.2. Free Program Fifa Manager 2008 Kit. 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.