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5 reasonable value, say 5 times the mean. For our example workload, we define an inter-arrival time of 1 second with a negative exponential distribution truncating at 10 seconds. Think Times In benchmarks that model applications involving direct user interaction, it may make sense to model think times instead of arrival rates, the idea being that the user takes some time to respond to what is being presented to him. Think times can vary based on the type of operation. If an operation presents lots of data, it might take the user more time to respond. Think times are also typically modeled using a negative exponential distribution with some targeted mean. The distribution is truncated at a fairly large value, say 5-10 times the mean. Operation Mix We need to decide on the frequency with which each of the operations will be exercised, the goal being to execute operations that test most frequently used code paths. This is often expressed as a operation mix% for each type of operation, the total adding up to 100%. The mix for any one type of operation should not be very small (say < 1%) as it may require a large number of samples to achieve that probability. Mixes can be specified in different ways : Flat Mix: The flat mix is the simplest and is used when the operations are independent of each other and each operation has the same probability of occurrence. The mix selects one of the operations at random. Flat Sequence Mix: This mix specifies a set of a sequence of operations. For example, one set could be the sequence {operation1, operation2} and another could be {operation1, operation3} with a probability assigned to each set. This is often referred to as scenarios. Matrix Mix: Also called a Transition Mix, this mix describes the transition probabilities in a Morkov model [Menascè04] as a right stochastic matrix. This must be a square matrix. Each row represents a probability vector for transitioning from an operation (row operations index) to an operation (column operations index). Most web applications would typically use a matrix mix as transitions between various pages can be represented by a state diagram. For our example workload, we choose a matrix mix with the following probabilities : From To To To Page 1 Page 2 Page 3 Page % 80.00% 20.00% Page % 39.00% 41.00% Page % 19.00% 21.00% Operation Data Depending on the operation, various pieces of data may have to be generated as input to the request. It is essential to vary the data used in order to test realistic scenarios and also vary the load on the SUT. For example, if an operation accesses a varying number of items ranging from [1..100], it is a good idea to test the entire range instead of always selecting a fixed number of items. It is also good practice to inject a small number of errors by using invalid data as input to

9 [SPR02] SPECjAppServer2002 Run Rules [TRIV82] Kishor Shridharbhai Trivedi, "Probability & Statistics with Reliability, Queueing, and Computer Science Applications," pp (1982). [Menascè04] Daniel A. Menascè, Performance by Design, pp (2004). [PARK88] S.K. Park and K.W. Miller, "Random Number Generators: Good Ones Are Hard To Find", Communications of the ACM, pp (October 1988) [MARS91] George Marsaglia and Arif Zaman, "Toward a Universal Random Number Generator", Annals of Applied Probability, no. 3, vol. 3, pp (1991). [SPECj04] SPECjAppServer2004 Database Scaling Rules [OLIO] A Web2.0 Toolkit and benchmark, All Rights Reserved. See Terms and Conditions.

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