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the logscale diagram are addressed, with references to examples from real traf c data and arti cially generated traces. De nitions, statistical performance, and pertinent features of the estimators for scaling parameters are then studied in detail. The logscale diagram, rst de ned with respect to second-order statistical quantities, is then extended to statistics of other orders. It is also indicated how the tool allows for and deals with situations=processes departing from pure scaling, such as superimposed deterministic nonstationarities. Finally, clear connections between the wavelet tool and a number of more classical statistical tools dedicated to the analysis of scaling are drawn, showing how the latter can be pro tably generalized in their wavelet incarnations. Section 2.4, Wavelet and Scaling: Synthesis, proposes a wavelet-based synthesis of the fractional Brownian motion. It shows how this process can be naturally and ef ciently expanded in a wavelet basis, allowing, provided that the wavelets are suitably designed, its accurate and computationally ef cient implementation. Finally, in Section 2.5, Wavelets and Scaling: Perspectives, a brief indication is given of what may lay ahead in the broad land of scaling and wavelets. 2.2 2.2.1 WAVELET AND SCALING: THEORY Wavelet Analysis: A Brief Introduction



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It is very likely that SANs will become much easier to build and administer over the coming years, but they have a very long way to go before they are as easy to manage as a filer, which, as we said earlier, is little more than a black box with few, if any, buttons and knobs NAS tends to be a much less expensive solution to deploy than SANs, since it employs older and more standardized technology While a single HBA for a SAN can cost around $1,000 (a number that is sure to drop significantly over time), a 100Mbit Ethernet card can cost less than $20 In a large network with lots of hosts, the cost savings for NAS will be quite significant But the performance with NAS can be significantly slower.





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2.2.1.1 The (Continuous) Wavelet Transform The continuous wavelet decomposition (CWT) consists of the collection of coef cients fTX a; t hX ; ca;t i; a P R ; t P Rg that compares (by means of inner products) the signal X to be analyzed with a set of analyzing functions & ' u t 1 ca;t u p c0 ;a P R ;t P R : a a

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NAS can be implemented on top of your existing Ethernet-based networking infrastructure (assuming the Ethernet has additional capacity, of course), while SANs require an entire new infrastructure based on Fibre Channel optical cabling, further increasing their cost SANs can stretch over greater distances than NAS While it s difficult to say what the upper limit is for the size of a SAN (it seems to change every 45 minutes), 50 80 kilometers is a sure bet If you need a SAN to stretch beyond that limit, it may be possible; speak with your SAN vendors NASs can be stretched over several kilometers with the inclusion of repeaters and other expensive hardware, which will also slow down the performance of the network If you wish to stretch a NAS over unusually long distances, be sure you work closely with your vendor and you benchmark everything very carefully..

This set of analyzing functions is constructed from a reference pattern c0 , called the mother wavelet, by the action of a time-shift operator tt c0 t c0 t t and a dilation (change of scale) operator p da c0 t 1= ac0 t=a : c0 is chosen such that both its spread in time and frequency are relatively limited. It consists of a small wave de ned on a support, which is almost limited in time and having most of its energy within a limited frequency band. While the time support and frequency band cannot both be nite, there is an interval on which they are effectively limited. The time-shift operator enables the selection of the time instant around which one wishes to analyze the signal, while the dilation operator de nes

Host routes are suitable for small client/server computing groups, because they grow linearly in size as more clients and servers are added to the mix. Host routes have the advantage of putting the client in control of its path to the server, and they don t require any router reconfiguration. They support multiple routers and can be easily automated on the client side by testing connectivity, making them resistant to a variety of configuration and naming errors. The downside, again, is complexity. For larger computing groups, use redundant routers and fully redundant networks and avoid making changes on a per-client basis. In all cases, redundant network rails consume a large number of IP addresses, so ensure that you have enough network numbers or subnets and a sufficiently large number of free network addresses to accommodate your numbering and failover system. You can conserve IP addresses by using host routes to connect servers directly to routers, eliminating networks of two or three servers. Since clients match the longest portion of an IP address possible when looking for a route, they ll find host routes to servers first in their search. In Figure 9.6, the two routers connect four servers to the client network, using only one IP network with four host routes. Without the host routes, two IP network numbers would be needed, one for each group of servers, creating a configuration that scales poorly as additional servers are brought online.

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