Smart Edges 79
own knowledge base and forwards it on to its neighbors. This
knowledge, however, is less fresh than if the situated view were a little
larger.
Thus, it is necessary to optimize the situated view depending on the
knowledge needed by the control algorithms. With this in mind, we
need to answer the questions: which, where and why? “Which” refers
to the knowledge necessary to optimize an algorithm; “where” relates
to the span of the situated view; and “when” indicates
the refreshes needed for that knowledge to be usable. On the basis of
these different parameters, it is possible to generate situated views that
are a little more complex than the simple one-hop definition, including
the information of links and all the seconds.
Until 2008, networks were remarkably static, and a network
engineer often needed to be present to deal with things when a
problem arose. In the case of very large networks, tens of network
engineers could be needed to perform maintenance and deal with the
diverse problems that emerged.
The objective of self-adjusting networks is to offer automated
piloting of the network by a program capable of managing the control
algorithms in a coordinated manner, and thereby optimize the
network’s operation.
At the start of the 2000s, an initial attempt was made, consisting of
using programmable networks and active networks. The research was
not entirely conclusive, for reasons of security and cost. A new
generation was launched in 2005, with the autonomic networks we
have just presented.
3.14. Conclusion
The Cloud requires centralization, with its huge datacenters
capable of handling enormous masses of data and of computing
forwarding tables for a very high number of data streams.
Unfortunately, this solution is not entirely satisfactory, because the
reaction times are long, and reliability and security pose numerous
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own knowledge base and forwards it on to its neighbors. This
knowledge, however, is less fresh than if the situated view were a little
larger.
Thus, it is necessary to optimize the situated view depending on the
knowledge needed by the control algorithms. With this in mind, we
need to answer the questions: which, where and why? “Which” refers
to the knowledge necessary to optimize an algorithm; “where” relates
to the span of the situated view; and “when” indicates
the refreshes needed for that knowledge to be usable. On the basis of
these different parameters, it is possible to generate situated views that
are a little more complex than the simple one-hop definition, including
the information of links and all the seconds.
Until 2008, networks were remarkably static, and a network
engineer often needed to be present to deal with things when a
problem arose. In the case of very large networks, tens of network
engineers could be needed to perform maintenance and deal with the
diverse problems that emerged.
The objective of self-adjusting networks is to offer automated
piloting of the network by a program capable of managing the control
algorithms in a coordinated manner, and thereby optimize the
network’s operation.
At the start of the 2000s, an initial attempt was made, consisting of
using programmable networks and active networks. The research was
not entirely conclusive, for reasons of security and cost. A new
generation was launched in 2005, with the autonomic networks we
have just presented.
3.14. Conclusion
The Cloud requires centralization, with its huge datacenters
capable of handling enormous masses of data and of computing
forwarding tables for a very high number of data streams.
Unfortunately, this solution is not entirely satisfactory, because the
reaction times are long, and reliability and security pose numerous
www.it-ebooks.info