Andere Kunden interessierten sich auch für
![Evolution of Communication and Language in Embodied Agents]( "Evolution of Communication and Language in Embodied Agents")
Evolution of Communication and Language in Embodied Agents110,99 €![Uncertainty Reasoning in Network Fault Diagnosis]( "Uncertainty Reasoning in Network Fault Diagnosis")
Uncertainty Reasoning in Network Fault Diagnosis38,99 €![Multifractal Based Network Traffic Modeling]( "Multifractal Based Network Traffic Modeling")
Multifractal Based Network Traffic Modeling74,99 €![Fault-Tolerant Multiprocessor Networks]( "Fault-Tolerant Multiprocessor Networks")
Fault-Tolerant Multiprocessor Networks51,99 €![Fault Tolerance for High-Performance Applications]( "Fault Tolerance for High-Performance Applications")
Fault Tolerance for High-Performance Applications51,99 €![CDMA Techniques for Third Generation Mobile Systems]( "CDMA Techniques for Third Generation Mobile Systems")
CDMA Techniques for Third Generation Mobile Systems110,99 €![Security Issues,Aspects & Domain Based Security for Mobile Agents]( "Security Issues,Aspects & Domain Based Security for Mobile Agents")
Security Issues,Aspects & Domain Based Security for Mobile Agents32,99 €
Existing centralized based network management
approaches suffer from problems such as insufficient
scalability, availability and flexibility, as
networks become more distributed. Mobile Agents (MA),
upgraded with intelligence, can present a reasonable
new technology that will help to achieve distributed
management. These agents migrate from one node to
another, accessing an appropriate subset of MIB
variables from each node analysing them locally and
retaining the results of this analysis during their
subsequence migration. One of the network fault
management tasks is fault detection, and in this work
we purpose statistical method based on Wiener filter
to capture the abnormal changes in the behaviour of
the MIB variables. my algorithm was implemented on
data obtained from two different scenarios in the
laboratory, with four different fault case studies.
The purpose of this is to provide the manager node
with a high level of information, such as a set of
conclusions or recommendations, rather than large
volumes of data relating to each management task.
approaches suffer from problems such as insufficient
scalability, availability and flexibility, as
networks become more distributed. Mobile Agents (MA),
upgraded with intelligence, can present a reasonable
new technology that will help to achieve distributed
management. These agents migrate from one node to
another, accessing an appropriate subset of MIB
variables from each node analysing them locally and
retaining the results of this analysis during their
subsequence migration. One of the network fault
management tasks is fault detection, and in this work
we purpose statistical method based on Wiener filter
to capture the abnormal changes in the behaviour of
the MIB variables. my algorithm was implemented on
data obtained from two different scenarios in the
laboratory, with four different fault case studies.
The purpose of this is to provide the manager node
with a high level of information, such as a set of
conclusions or recommendations, rather than large
volumes of data relating to each management task.