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Examples of Information Providers are: Application Service Provider: leases to customers the use of software applications that he owns or rents Examples of such applications are www-servers for web hosting, databases, and the complete outsourcing of business IT operations An ASP rents space from a Data Centre Provider, and often these two types of service are offered by the same business entity Content Provider: produces, organizes, manages and manipulates content such as video, news, advertisements and music When such services are more advanced, including the ability for easily searching and purchasing a broad category of goods and services, they are called portal services Content Distributor: manages content provided by Content Providers in network caches located near the End-Users An End-User who accesses the content of a remote web site will receive the same content from the local cache, instead of having to go through the whole Internet.



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The above relation can be proven almost by inspection. We now substitute x = r sin cos u = k0 sin cos and then compute du dv = u v





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The module test will establish the increase in the noise in the forward wave vok that is required to reproduce the observed module noise in a noiseless module This will be the same whether the module is being tested or is in a cascade Once this is established, the effective increase of the available noise in the source can be related to the noise in vok by the gain from the source to vok in the cascade Because vok is the variable we have used in our standard-cascade calculations, the gains employed there also apply to noise gure calculations While R0 is usually the same for all modules and the cascade, this is not necessary There can be a change in the standard impedance along the cascade Where this occurs, the input and output of some module (and their interconnects) would have different standard impedances.

Each module would be tested with its standard input impedance (in switch positions 3 and 1), and the cascade would be tested with its standard input impedance (in switch positions 2 and 1) We now show how the contribution from lossy interconnects is appropriately incorporated in our model..

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Such services improve the performance of web sites, specially when users access multimedia information that requires high bandwidth, or they access large les Caches are located as near as possible to the access network, so to avoid bottlenecks and guarantee good performance A Content Distributor is responsible for regularly updating the information stored in the caches to re ect accurately the content of the primary web site The quality of a content distribution service improves with the number of cache locations the provider uses More locations imply a lower average distance from an End-User to such a cache Note that Content Distributors allow for information to be accessed locally instead of using the Internet backbone In this respect they are in direct competition to Backbone Service Providers A local ISP buying services from a large Content Distributor may worry less about transport quality through the backbone.

Life After Outsourcing A Sourcing Management Model approach to problems and solutions, strive for fair treatment of individuals and their interests, and aim at a positive overall experience. However, partner relationship management is also counterintuitive. Although your company may be paying good money for the services provided, at the end of the day, you are still competing for the attention and the best resources and services your outsourcer can provide to all its other customers. A good rule of thumb to gauge effectiveness of partner relationship management is to ask: Would you put the same people you have in this role in a customer relationship role If so, you are probably bringing the right skills and aptitudes to the function; if not, you are most likely still practicing vendor management rather than relationship management.

Release is the process of deployment to the production environment. The reporting system data and the calculated measures should provide a comprehensive view of the system under development and facilitate release-readiness decisions. Release readiness should be determined by tracking and assessing these multiple indicators over an extended period, evaluating their correlation, and obtaining a thorough understanding of the application s functionality and quality status. (See Figure 9.18.) In addition to the previously discussed metrics, one of the most important measures in determining release readiness is the product status con dence factor (CF). The con dence factor is a derived measure calculated as an aggregate of the individual metrics. A sample list of such metrics contributing to the value of the con dence factor is shown in Figure 9.19. Each of the indicators pi is scaled from 0 to 100 and assigned a weight, wi, which represents the indicator s signi cance. Using this notation, and assuming that there are n indicators, the con dence factor (CF) is expressed in percentile form by the following formula: CF = wi pi ,

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