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Step 3 - Create a Workload Model
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When defining workload distribution, consider the following key points for determining the characteristics for user scenarios: A user scenario is defined as a navigational path, including intermediate steps or activities, taken by the user to complete a task. This can also be thought of as a user session. A user will typically pause between pages during a session. This is known as user delay or think time. A session will have an average duration when viewed across multiple users. It is important to account for this when defining the load levels that will translate into concurrent usage, overlapping users, or user sessions per unit of time. Not all scenarios can be performed by a new user, a returning user, or either; know who you expect your primary users to be and test accordingly.
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Step 4 - Identify Target Load Levels
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Identify the load levels to be applied to the workload distribution(s) identified during the previous step. The purpose of identifying target load levels is to ensure that your tests can be used to predict or compare a variety of production load conditions. The following are common inputs used for determining target load levels: Business volume (both current and projected) as it relates to your performance objectives Key scenarios Distribution of work Session characteristics (navigational path, duration, percentage of new users) By combining the items above, you can determine the remaining details necessary to implement the workload model under a particular target load.
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Step 5 - Identify Metrics
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There is a virtually unlimited number of metrics that can be collected during a performance test execution. However, collecting too many metrics can make analysis unwieldy as well as negatively impact the application s actual performance. For these reasons, it is important to identify the metrics that are most relevant to your performance objectives and those that you anticipate will help you to identify bottlenecks. Only wellselected metrics that are analyzed correctly and contextually provide information of value. The following are a few suggestions for identifying the metrics that will provide the most valuable information to your project: Define questions related to your application performance that can be easily tested. For example, what is the checkout response time when placing an order How many orders are placed in a minute These questions have definite answers. With the answers to these questions, determine quality goals for comparison against external expectations. For example, checkout response time should be 30
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seconds, and a maximum of 10 orders should be placed in a minute. The answers are based on market research, historical data, market trends, and so on. Identify the metrics. Using your list of performance-related questions and answers, identify the metrics that provide information related to those questions and answers. Identify supporting metrics. Using the same approach, you can identify lower-level metrics that focus on measuring the performance and identifying the bottlenecks in the system. When identifying low-level metrics, most teams find it valuable to determine a baseline for those metrics under single-user and/or normal load conditions. This helps you determine the acceptable load levels for your application. Baseline values help you analyze your application performance at varying load levels and serve as a starting point for trend analysis across builds or releases. Reevaluate the metrics to be collected regularly. Goals, priorities, risks, and current issues are bound to change over the course of a project. With each of these changes, different metrics may provide more value than the ones that have previously been identified.
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Additionally, to evaluate the performance of your application in more detail and to identify potential bottlenecks, it is frequently useful to monitor metrics in the following categories: Network-specific metrics. This set of metrics provides information about the overall health and efficiency of your network, including routers, switches, and gateways. System-related metrics. This set of metrics helps you identify the resource utilization on your server. The resources being utilized are processor, memory, disk I/O, and network I/O. Platform-specific metrics. Platform-specific metrics are related to software that is used to host your application, such as the Microsoft .NET Framework common language runtime (CLR) and ASP.NET-related metrics. Application-specific metrics. These include custom performance counters inserted in your application code to monitor application health and identify performance issues. You might use custom counters to determine the number of concurrent threads waiting to acquire a particular lock, or the number of requests queued to make an outbound call to a Web service. Service-level metrics. These metrics can help to measure overall application throughput and latency, or they might be tied to specific business scenarios. Business metrics. These metrics are indicators of business-related information, such as the number of orders placed in a given timeframe.
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