I was asked the other day how does Citrix improve response times. The simple answer is that it cuts down on the number of times the user has to wait for information to be transfered across the network. For example the diagram below shows an application on a client PC communicating with the DB. In this case it is Oracle forms 6i and this can take between 20-60 network hops to get the data needed to display a screen.
If you are on the same LAN as the DB then you may not notice the delay but it you access across a WAN then the time to cross the WAN all adds up to a slow response time.
With Citrix the citrix server is placed in the datacentre close to the DB. The client then makes one request to the citrix server, the citrix server makes all the requests to the DB and then when the screen is complete send a picture of the screen back to the user. As can be seen in the diagram below.
As the citrix server and DB are closely located then the network hops needed to get the data to build the screen happen quickly and the user has to suffer the delay across the WAN only once. Where applications cross the WAN many times and the delay from the users to the server is high then Citrix is likely to help improve performance. However, you need to also consider the bandwidth of the pipe between the client and the server.
To do this you can create a performance model. The following presentation contain I performance model I built for determining when to deploy citrix to various locations as part of a large upgrade project. In that project the application made 7 trips across the network to generate the display for the user. Pages 12-21 provide the model and some results.
You can download the presentation here.
I have just added to the website a presentation that looks at sizing and extrapolation techniques for people considering building a small scale performance test environment instead of a large full scale performance test environment. In the paper several approaches are considered.
Factoring – This is where the architecture is easily scaled and therefore the performance test can be undertaken on a subset of the hardware.
Dimensioning – The architecture has known bottlenecks that drive the performance such as a central DB. The performance test environment must contain the bottleneck component but other components may not need to be representative of a full sized environment.
Modelling – This examines the use of modelling to take results from a small scale environment and predict the results for a larger scale environment.
Flipping – This looks at creating test environment that can be have the correct amount of resources allocated to them for a “full scale” performance test for example during off hours and then revert to a smaller scale performance test environment at other times.
Full Scale – Finally the advantages and disadvantages of a full scale performance test environment are discussed.
Finally the caveat for these techniques is that for any testing on a small scale performance test environment does not guarantee that all performance problems will be discovered due to application/scalability constrains that may only appear in the full sized environment!
You can download the presentation from here.
So you are on a performance test engagement and your boss asks how many people concurrently executing certain transactions like buying a book or doing a search. He wants is a measure of active concurrency – how many people are doing certain transaction. This should not be confused with Passive concurrency like how many people are logged in. Before we go anyfuther lets clarify that in this example a transaction is a request to the test system and a response back it does not include any think time. Now before you start getting out the virtual terminal server and incrementing counters at the start of the transaction and decrementing counters at the end. There is an easier way.
You can work this all out from your performance test results, without the need for code. Using a mathematical formula (it’s very simple so don’t panic) called Littles Law. Littles Law was first used to analyse the performance of telephone exchanges in 1969 by John Little.
Little’s law allows us to relate the mean number of items in the system in our case concurrent users with the mean time in the system (response time) as follows:
Number of Items in the system = Arrival Rate x Response Time
There is one rule to remember before you use little law you must make sure the system is balanced. That is the arrival rate into the system is the same at the exit rate.
I will begin with a none computer example the “Black Horse Pub” has a mean arrival rate of 5 customers per hour that stay for on average half an hour. Using Little’s law we can calculate the mean number of customers in the pub as Arrival Rate x Response Time = 5 x 0.5 = 2.5 customers.
To apply little law to a performance test we must first make sure that we are taking measurements from when the system under test is balanced. Remember a balanced system the rate of work entering the system matches the rate of work leaving the system. This for a typical load testing tool is after the ramp up period and the number of virtual users remains constant and response times have stabilised and the transaction per second graph is level. To capture this period of time in LoadRunner for example you would need to select the time period in the Summary report filter or under the Tools -> Options.
So record the average response time for the transaction of interest and the number of times per second the transaction is executed.
So from the example above the response time is 43.613 seconds. The arrival rate is the number of transactions executed divided by the duration. The duration for this example was a 10 minute period as can be confirm by the LoadRunner summary below.
This gives you an arrival rate of 2.005 calculated by taking the count 1203 divided by the duration 600.
So the concurrent number of users waiting for a search to return is 87.44
There you go from your performance test results you can easily calculate the concurrency for a particular transaction.