The first metric is the end user’s page response time. This is a measure of the time required for the original request to be processed. It could be measured by placing a probe near the client to measure the turn time and validate the processing of the request. An example would be the duration from a request for a website and when the content is displayed on the user’s client.
Next is the total number of transactions processed. During a specified time period, measuring the volume of transactions is useful to see if they are too high, which results in transactions being caught in the queue. This in turn leads to errors and causes issues such as the client processing the request again. Excessive transactions can materially impact individual user experience.
Server query response time is also an important metric. This is the application-server side counterpart to page response time. Application-specific visibility is necessary in order to assess detailed response times and view transactions. Continuing with the website example, this might be the time the web server waits until it is able to construct the entire URL contents.
Another advantage of having application-specific visibility is being able to measure traffic flow data. Data can be collected about each conversation showing the flow by application, including such details as packets, bytes, connections and request details. Understanding the traffic flow mix and amount on a network can provide invaluable indirect information into how distinct users may currently perceive usability. Perhaps more important, it can provide insight into trends that may eventually impact users if remedial action is not completed.
Finally, server errors themselves provide useful information. Details of the history of packet captures and application transaction details show server conditions, and this visibility shows when errors result from a higher number of requests than the server can handle. Too many server errors can ultimately impact users’ overall service delivery experience.
Network latency (RTT), server utilization, network availability and bandwidth utilization are additional metrics that provide insight into the user experience. Leveraging to many of any of these or in combination with the above discussed parameters can impact the delivery of services to users.
Next is the total number of transactions processed. During a specified time period, measuring the volume of transactions is useful to see if they are too high, which results in transactions being caught in the queue. This in turn leads to errors and causes issues such as the client processing the request again. Excessive transactions can materially impact individual user experience.
Server query response time is also an important metric. This is the application-server side counterpart to page response time. Application-specific visibility is necessary in order to assess detailed response times and view transactions. Continuing with the website example, this might be the time the web server waits until it is able to construct the entire URL contents.
Another advantage of having application-specific visibility is being able to measure traffic flow data. Data can be collected about each conversation showing the flow by application, including such details as packets, bytes, connections and request details. Understanding the traffic flow mix and amount on a network can provide invaluable indirect information into how distinct users may currently perceive usability. Perhaps more important, it can provide insight into trends that may eventually impact users if remedial action is not completed.
Finally, server errors themselves provide useful information. Details of the history of packet captures and application transaction details show server conditions, and this visibility shows when errors result from a higher number of requests than the server can handle. Too many server errors can ultimately impact users’ overall service delivery experience.
Network latency (RTT), server utilization, network availability and bandwidth utilization are additional metrics that provide insight into the user experience. Leveraging to many of any of these or in combination with the above discussed parameters can impact the delivery of services to users.
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