Mobile Agents for Network Fault Detection Based on The Wiener Filter
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Existing centralized based network managementapproaches suffer from problems such as insufficientscalability, availability and flexibility, asnetworks become more distributed. Mobile Agents (MA),upgraded with intelligence, can present a reasonablenew technology that will help to achieve distributedmanagement. These agents migrate from one node toanother, accessing an appropriate subset of MIBvariables from each node analysing them locally andretaining the results of this analysis during theirsubsequence migration. One of the network faultmanagement tasks is fault detection, and in this workwe ...
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.
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