this specification can refer to among other things any technique outlined in gribok including ordinary least squares ols support vector machines svm artificial neural networks anns mset or regularized mset rmset once these correlations have been determined by mset device 108 they can be used to determine which combinations of performance parameters are efficient predictors of failure sequences that are likely to lead to undesirable system behavior as is described below with reference to fig3 and 4 fig3 and 4 illustrate a process monitors system performance parameters to detect failure sequences in accordance with an embodiment of the present invention the process starts when a real workload 302 is executed on server 104 step 402 during this execution the process gathers instrumentation signals 307 from possibly hundreds of sensors within server 104 step 404 these instrumentation signals feed into mset device 108 which uses previously determined correlations between instrumentation signals 110 to generate a set of estimated signals 309 step 406 note that this process generates an estimated signal for each instrumentation signal also note that each estimated signal can be generated by applying predetermined correlations with other signals to the actual measured values for the other signals next the instrumentation signals 307 and the estimated signals 309 feed into difference function generator 312 which compares the signals by computing pairwise differences 314 between each instrumentation signal and its corresponding estimated signal step 408 next the set of differences 314 feeds into a sequential probability ratio test sprt module 316 which examines the differences 314 to determine if the system is in a failure sequence that is likely to.