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Operating Systems Solaris How to predict system performance? Post 302320616 by Neo on Thursday 28th of May 2009 12:14:43 PM
Old 05-28-2009
One way to predict system performance, is by running a statistical package, like a linear regression package, against your historical data and current data.

Another way is to look for leading indicators, or precursors, in the same data.

Either way, prediction is "hard stuff" ...... in many ways, it is the "holy grail" of systems monitoring.

What we do, in the absence of good predictive analytics, is to make sure we have very good visualization (charting) capabilities.

Visualization (charting) and the human mind, along with some basic event triggers, is the first step toward predictive systems analysis.

What visualization package are you using?

Cheers.
 

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mlib_SignalLPCCovariance_F32(3MLIB)			    mediaLib Library Functions			       mlib_SignalLPCCovariance_F32(3MLIB)

NAME
mlib_SignalLPCCovariance_F32 - perform linear predictive coding with covariance method SYNOPSIS
cc [ flag... ] file... -lmlib [ library... ] #include <mlib.h> mlib_status mlib_SignalLPCCovariance_F32(mlib_f32 *coeff, const mlib_f32 *signal, void *state); DESCRIPTION
The mlib_SignalLPCCovariance_F32() function performs linear predictive coding with covariance method. In linear predictive coding (LPC) model, each speech sample is represented as a linear combination of the past M samples. M s(n) = SUM a(i) * s(n-i) + G * u(n) i=1 where s(*) is the speech signal, u(*) is the excitation signal, and G is the gain constants, M is the order of the linear prediction fil- ter. Given s(*), the goal is to find a set of coefficient a(*) that minimizes the prediction error e(*). M e(n) = s(n) - SUM a(i) * s(n-i) i=1 In covariance method, the coefficients can be obtained by solving following set of linear equations. M SUM a(i) * c(i,k) = c(0,k), k=1,...,M i=1 where N-k-1 c(i,k) = SUM s(j) * s(j+k-i) j=0 are the covariance coefficients of s(*), N is the length of the input speech vector. Note that the covariance matrix R is a symmetric matrix, and the equations can be solved efficiently with Cholesky decomposition method. See Fundamentals of Speech Recognition by Lawrence Rabiner and Biing-Hwang Juang, Prentice Hall, 1993. PARAMETERS
The function takes the following arguments: coeff The linear prediction coefficients. signal The input signal vector. state Pointer to the internal state structure. RETURN VALUES
The function returns MLIB_SUCCESS if successful. Otherwise it returns MLIB_FAILURE. ATTRIBUTES
See attributes(5) for descriptions of the following attributes: +-----------------------------+-----------------------------+ | ATTRIBUTE TYPE | ATTRIBUTE VALUE | +-----------------------------+-----------------------------+ |Interface Stability |Evolving | +-----------------------------+-----------------------------+ |MT-Level |MT-Safe | +-----------------------------+-----------------------------+ SEE ALSO
mlib_SignalLPCCovarianceInit_F32(3MLIB), mlib_SignalLPCCovarianceFree_F32(3MLIB), attributes(5) SunOS 5.10 10 Nov 2004 mlib_SignalLPCCovariance_F32(3MLIB)
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