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Backbone of Backbone Methods An alternative approach, especially useful if demand OSPF is not available, is to split Area 0.0.0.0 and create two OSPF domains. Each domain has a static route that points to the address range in the other domain. This route is advertised into the local domain as a Type 1 external. True, OSPF s area structure has two levels, Area 0.0.0.0 and everything else. A routing architecture for an enterprise that uses OSPF, however, can be more than a simple hierarchy of backbone and nonbackbone areas. We can extend its notion of hierarchy both above Area 0.0.0.0 and below the nonzero areas. Even inside an area, there are some forms of hierarchy. Such an architecture also lends itself to evolution to a high-performance layer 2 or multiprotocol label switching (MPLS) service in the core. It can also be reasonable to have interior routers that concentrate traffic, or act as collapsed backbones within an area. It is also perfectly reasonable to have a broader OSPF routing environment, in which some routers do not speak OSPF but cooperate with those that do. At the high end, this involves redistribution of external routes into OSPF. At the low end, this involves static and default

Figure 3.5 shows how a 3-bit PCM scheme works, and the picture may be extended to any number of bits. The picture shows amplitude versus time. The input to the scheme is an analog sample, and one sample arrives each T seconds, three altogether

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Queuing delay. Packet-switching delay may include queuing delay when a packet switch needs to queue data because an output buffer or interface is busy. The number of packets in a queue on a packet-switching device increases exponentially as utilization on an output port increases. EXPONENTIAL AVERAGES Cisco uses the vague phrase "exponential average over 5 minutes" when describing load, reliability, 5-minute input and output rates, and 5-minute packets per second statistics. Actually, the software calculates a sample every 5 seconds and provides a moving average over a 5-minute period. A moving average incorporates feedback from previous samples into the current result. The most recent sample is weighted slightly more than previous samples, but because there are so many more previous samples, they contribute heavily to the result also. The weighting decays exponentially for each previous sample, but in 5 minuets there are 60 previous samples, so they are still significant. The goal is to avoid skewing the result too much if the 5-second sample happens to take place when someone is downloading a huge MP3 file, for example, or when nobody was doing anything on the network for whatever reason. The details of the exponential average calculation are buried in a Cisco document titled "Definition of 'bits/sec' from 'sh int.'" The URL for the document is www.cisco.com/warp/public/66/3.html.

The theory used here is based on the theory of automata but was partly enhanced by Rumpe [9] to describe a form of automata, called I/O -automata, where transitions are triggered by one incoming event and the effect of this event: a sequence of possible outputs is the output of the same transition. In contrast to I/O-automata [6], this form

TABLE 8.1 WBS Elements and Related Life Cycle Phases WBS Elements Air vehicle Systems engineering/Program management System test and evaluation Training Data Peculiar support equipment Common support equipment Operational/site activation Industrial facilities Initial spares and repair parts Life Cycle Phase Operational Development Development Training Manufacturing and Re nement Operational Operational Deployment Manufacturing Operational

natural language community, for instance, uses ontologies to map word meaning and sense. The AI community is concerned with knowledge-based systems, where ontologies capture domain knowledge, and problem-solving methods capture the task knowledge. The database community, when concerned with semantic interoperability of heterogeneous databases, uses ontologies as conceptual schemata. In semantic-based information retrieval, in particular, ontologies directly specify the meaning of the concepts to be searched for. By contrast, XML-based systems in this context are severely limited in utility unless the independent site-content authors happen to agree on the semantics of the terms they embed in the source metadata. Ontologies reduce such semantic ambiguity by providing a single interpretation resource, and therefore ll an important role in collaborations (human and machine), interoperation contexts, education, and modeling. Ontologies can also enable software to map and transform information stored using variant terminologies, thus enabling Information Broker Architecture to satisfy human or agent queries by searching a far wider selection of online sources.

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