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]
