Patent ID: 8538740

Claim:
In a performance modeling computer having a processor, a method for real time determination of a performance model of a transaction processing system that processes multi-class workloads, the method comprising: representing a multi-class workload processed by the transaction processing system, and represented by arrival rates, λ a , λ b and λ c , of transaction request classes, a, b and c; representing the performance model of the transaction processing system, by a state of the s transaction processing system that includes service time, s a , s b , s c , and network delay, d a , d b , d c ), the performance model being represented by a state vector, x, given by: x = [ s a s b s c d a d b d c ] T ; representing a measurement datum z that is at least one of gathered and sampled from the transaction processing system, by response times, R a , R b and R c , of a request completion and CPU utilization, u, averaged over all CPUs of the of the transaction processing system, the measurement datum z, given by: z = [ R a R b R c u ] ; representing a measurement model that relates the performance model x to the measurement datum z at current time step k, by: z k =H k x k +v k ; wherein z k is a measurement vector representing the measurement datum, z, H k is a measurement model matrix, x k is the performance model's state vector and v k is an observation noise vector; representing a process of real-time determination of the performance model, by the process of estimation of state vector x at current time step k using measurement datum gathered at a past and a current time steps 0 to k via a modified extended Kalman filter, representing a time window by a continuous series of time steps prior to current time step k over which the measurement datum z has been gathered in the past time; for a set of measurement data received by the processor: collecting measurement datum z 0 to z k at the past and current times 0 to k; selecting any N time windows prior to a current time step k; processing the measurement datum z 0 to z k over the N time windows through a set of mathematical operations of summation and averaging to compute a new vector d; processing a set of measurement model matrices H 0 to H k over the N time windows through a set of mathematical operations of summation and averaging to compute a new matrix D; forming a set of constraints on x k using the newly computed vector d and matrix D, the set of constraints being given by: Dx k =d; at least one of augmenting and appending the set of constraints to the measurement model to obtain an augmented measurement model given by; [ z k d ] = [ H k D ] ⁢ ⁢ x k + [ v k 0 ] , wherein the set of constraints are augmented to the measurement model as measurements with zero noise covariance, running a modified extended Kalman filter with the augmented measurement model to obtain an estimate of the performance model's state vector x k at time step k; incrementing the time step k.