It is quite common for both parents in a family to work. Sometimes this is because they need two salaries to survive whereas other times it is their choice. What are the problems you see in this situation? How does this affect the children and the family life?

The main objective of applying self-structuring artificial intelligence for Software Defined Networks (SDN) in
Data
Centres is to minimise the human intervention on managing SDNs effectively to optimise the
network
performance, service orchestration, troubleshooting and proactive fault mitigation. Nowadays,
data
centres are an essential resource due to the progressive digitalization of organizations and the technologically advancing world. Modern
data
centres are driven by SDNs to facilitate the dynamically changing traffic demand and modern
network
application requirements
such
as high volatile resource provisioning which needs flexible and agile control of the
Data
Center
network
infrastructure. The resources within an SDN are becoming more sophisticated and heterogeneous,
such
that it is challenging to effectively organise, manage, secure and optimise the SDN manually. Even though conventional Artificial intelligence (AI) algorithms have been applied to overcome some of these challenges,
however
, the need for a human operator still exists in either the design, application or deployment of
such
algorithms and models.
This
research aims to apply Self-Structuring Artificial Intelligence (SS-AI), a new type of AI that continuously adapts to the environment, to SDNs which would mitigate difficulties of pre-determining a suitable AI architecture in conventional AI for more complex, non-deterministic and dynamic environments like
data
centre
networks. Metrics and
data
about the
Data
Centre
network
will be captured, transformed, and streamed in real-time(near real-time) into the AI layer using a
data
streaming platform. Different SS-AI models could be created to represent different domains which could be used to detect anomalies, patterns, trends and other useful features.
Moreover
, these models can be fused together to
further
identify correlations and relationships.
This
research would produce a minimal viable product (MVP) that could be deployed in a real-world
data
centre
which have features
such
as comprehensive visualization of the operational metrics of the
data
centre
, proactive fault detection and root cause analysis, resource requirement prediction based on historical trends, real-time (near real-time) automated and optimal
network
management and service orchestration.
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