Easy. The most important, least redundant, and single points of failure will fail next. On Sep 9, 2011 8:33 PM, "Mike Nute" <[email protected]> wrote: > IMO, the best approach would depend on your beliefs about the survival curve of the server. If you believe the general hazard rate is relatively constant (i.e. time-since-startup is not a huge factor) you could make it into a basic time series logistic regression problem: Let Y_i_t be 1 if server i fails at time t, 0 if it does not. Let X_i_(t-1) be the vector of measurements on server i at time (t-1). Then do logistic regression of X on Y. You could then add X_i_(t-2) to your predictors and see if it adds accuracy, and so on with previous time periods until they stop being predictive. > > That would also facilitate experimenting with transformations like the change in certain measurements at (t-1), (t-2), etc..., or interactions between certain measurements. > > If different failure classes are important, you could similarly apply that to multinomial logistic regression. > > If the failure rate depends heavily on time since startup, you could apply some kind of survival modeling technique like a Cox Proportional Hazard model or incorporating some prior belief about the shape of the survival curve. That could end up being technically similar to the logistic regression above, but with a more exotic link function and/or offset term. (I have a good brief chapter on the CPH model from an old actuarial exam study guide in pdf if you want it. Survival models are actuary staples :-).)
> > Hope that helps. > > Mike Nute > > > ------Original Message------ > From: Lance Norskog > To: user > ReplyTo: [email protected] > Subject: Predictive analysis problem > Sent: Sep 9, 2011 10:45 PM > > Let's say you manage 2000 servers in a huge datacenter. You have regularly > sampled stats, with uniform methods: aka, they are all sampled the same way > across all servers across the full time series This data is a cube of > (server X time X measurement type), with a measurement in each cell. > > You also have a time series of system failures, a matrix of server X failure > class. What algorithm will predict which server will fail next, and when and > how? > > -- > Lance Norskog > [email protected] > >
