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Also, in a data center rack, there is a single best path between two components, say, a system and a disk array If the system and disk array are joined by two parallel cables for added availability, the physical arrangement of the equipment may make it difficult or impossible to run the cables along separate paths (More on cabling later in the chapter) Tipping or collapsing hazards Racks are extremely heavy, often weighing 500 or 1,000 pounds or more after they are fully loaded If the weight is poorly distributed (that is, the rack is top-heavy), someone leaning on it could cause it to topple over.



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simply by considering the slope in a plot of log2 mj against j Here it is essential to understand that, although log log plots are a natural and familiar tool whenever exponents of power laws are at issue, using them as a basis for semiparametric estimation of the exponent is only effective statistically if properties equivalent to P1 P2 hold This is typically not the case For example, for the correlogram a time domain semiparametric estimator [17] based on direct estimation of the covariance function covariance estimates at xed lag are biased, resulting in bias in the exponent estimate Furthermore, across lags the covariance estimates are strongly correlated, resulting in misleadingly impressive ``straight lines'' in the log log plot, which in reality are symptomatic of high variance in the resulting estimates.





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feel the product is easy to use. The real value comes from drilling down and reading the actual verbatim comments. For diagnosing a particular issue or uncovering a sore spot, it s a great tool.

In addition to these issues, the complication that in general E log T log E is overlooked in the correlogram and in many other estimators based on log log plots For simplicity of presentation we set yj log mj for the moment but address this re nement in the estimation section below We now introduce a wavelet-based anlaysis tool, the logscale diagram, which exploits the key properties P1 and P2 and serves as an effective and intuitive central starting point for the analysis of scaling De nition 231 The (second-order) logscale diagram (LD) consists of the graph of yj against j, together with con dence intervals about the yj Examples of logscale diagrams analyzing synthesized scaling data are given in Fig 22, where the plot on the left is of a LRD series, and that on the right side of a self-similar series.

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While the effects of toppling racks can be pretty serious for the systems contained in the racks and those nearby, they pale in significance when compared to the impact of the racks striking or landing on an unfortunate person who happens to be wandering past the rack at just the wrong moment on his way to visit his own systems Rack wheels Most data center cabinets have casters on the bottom to make it easy to move them around the data center They usually also.

It follows from the nature of the dilation operator generating the wavelet basis that the number nj of detail coef cients at octave j halves with each increase in j (in practice the presence of border effects results in slightly lower values) Con dence intervals about the yj therefore increase monotonically with j as one moves to larger and larger scales, as seen in each of the diagrams in Fig 22 The exact sizes of these intervals depend on details of the process and in practice are calculated using additional distributional and quasi-decorrelation assumptions If necessary they could also be estimated from data Generalizations to the qth-order logscale diagrams can be de ned, q > 0, where the second moment of the details in Eq (219) is replaced by the qth.

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