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Step 3: Assessing the performance and reliabilityof the workow architectureAfter the generation of the input models for the SURE/ASSIST and the QNAP2tool-sets, what is left to be done is to use those tool-sets to actually assess thereliability and performance of the target workow architecture.For the case of reliability, the user must rst feed the ASSIST tool with thecorresponding model. The result is a complete Markov model, which is usedas input to the SURE tool. The latter calculates upper and lower bounds ofthe probability that the workow reaches a death state. Those bounds can becalculated for the sum of all state transition paths (resp. each individual path)leading to a death state.Getting to our example, the Markov model generated for the workow thatrealizes the billing service consists of 118 states and 482 transitions. Part of theoutput produced is given in Figure 5. More specically, the probability that theexecution of the workow fails is between 3.56422e-06 and 3.75204e-06.For the case of performance, the model generated is used as input to theQNAP2 tool-set, which simulates its execution. In particular, multiple instancesof the workow are initiated according to a given statistical pattern. The resultsproduced comprise, among others, the number of workow instances executed,the number of individual task instances executed, the mean time to execute theworkow, the mean time to execute the individual task instances that constitutethe workow.MODEL FILE = dingue.mod SURE V7.9.8 20 Dec 00 11:28:53MISSION TIME = 1.00000000000000e+01DEATHSTATE LOWERBOUND UPPERBOUND COMMENTS RUN #1---------- ----------- ----------- ---------------------------------1 3.56422e-06 3.75204e-06sure prune 0.00000e+00 5.08162e-09___________________________________________________________________________324 PATH(S) TO DEATH STATES, 173 PATH(S) PRUNEDHIGHEST PRUNE LEVEL = 4.40255e-100.034 SECS. CPU TIME UTILIZEDFigure 4: Results produced by the SURE/ASSIST tool-set.