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The second type of traf c we consider consists of digital objects or ``documents,'' which must be transferred from one place to another These documents might be data les, texts, pictures, or video sequences transferred for local storage before viewing This traf c is elastic in that the ow rate can vary due to external causes (eg, bandwidth availability) without detrimental effect on quality of service Users may or may not have quality of service requirements with respect to throughput They do for real-time information retrieval sessions, where it is important for documents to appear rapidly on the user's screen They do not for e-mail or le transfers where deferred delivery, within a loose time limit, is perfectly acceptable The essential characteristics of elastic traf c are the arrival process of transfer requests and the distribution of object sizes.

EXPORT_C void AsicInitialise() \ { E32Dll(KModuleEntryReasonExtensionInit1); }

Observations on Web traf c provide useful pointers to the nature of these characteristics [2, 5] The average arrival intensity of transfer requests varies depending on underlying user activity patterns As for stream traf c, it should be possible to identify representative busy periods, where the arrival process can be considered to be stationary Measurements on Web sites reported by Arlitt and Williamson [2] suggest the possibility of modeling the arrivals as a Poisson process A Poisson process indeed results naturally when members of a very large population of users independently make relatively widely spaced demands Note, however, that more recent and thorough measurements suggest that the Poisson assumption may be too optimistic (see 15) Statistics on the size of Web documents reveal that they are extremely variable, exhibiting a heavy-tailed probability distribution.

Thus reducing the entry point for the ASSP to:

Most objects are very small: measurements on Web document sizes reported by Arlitt and Williamson reveal that some 70% are less than 1 kbyte and only around 5% exceed 10 kbytes The presence of a few extremely long documents has a signi cant impact on the overall traf c volume, however..

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It is possible to de ne a notion of traf c demand for elastic ows, in analogy with the de nition given above for stream traf c, as the product of an average arrival rate in a representative busy period and the average object size.

DECLARE_STANDARD_ASSP()

Another category of traf c arises when individual ows and transactions are grouped together in an aggregate traf c stream This occurs currently, for example, when the ow between remotely located LANs must be treated as a traf c entity by a wide area network Proposed evolutions to the Internet service model such as differentiated services and multiprotocol label switching (MPLS) also rely heavily on the notion of traf c aggregation Through aggregation, quality of service requirements are satis ed in a two-step process: the network guarantees that an aggregate has access to a given bandwidth between designated end points; this bandwidth is then shared by ows within the aggregate according to mechanisms like those described in the rest of this chapter.

Some choices between subclassing and Adapters are just a matter of style Others reflect differences between delegation and inheritance

Typically, the network provider has the simple traf c management task of reserving the guaranteed bandwidth while the responsibility for sharing this bandwidth between individual stream and elastic ows devolves to the customer This division of responsibilities alleviates the so-called scalability problem, where the capacity of network elements to maintain state on individual ows cannot keep up with the growth in traf c The situation would be clear if the guarantee provided by the network to the customer were for a xed constant bandwidth throughout a given time interval In practice, because traf c in an aggregation is generally extremely variable (and even self-similar), a constant rate is not usually a good match to user requirements Some burstiness can be accounted for through a leaky bucket based traf c descriptor, although this is not a very satisfactory solution, especially for self-similar traf c (see Section 1632).

Since the emulator is based on the Windows DLL model and there is no ROMBUILD stage involved, we are not able to specify the order in which extensions are loaded at build time. To solve this problem, the Symbian OS emulator has its own mechanism for loading extensions, which ensures that the behavior of the emulated platform is identical to that of target hardware. The kernel loads the emulator s variant extension explicitly by name (ECUST.DLL) and invokes the exported VariantInitialise() function. This registers the extensions to be loaded by publishing a Publish/Subscribe key named Extension :