3 research outputs found
Traffic-Profile and Machine Learning Based Regional Data Center Design and Operation for 5G Network
Data center in the fifth generation (5G) network will
serve as a facilitator to move the wireless communication industry
from a proprietary hardware based approach to a more software
oriented environment. Techniques such as Software defined
networking (SDN) and network function virtualization (NFV)
would be able to deploy network functionalities such as service
and packet gateways as software. These virtual functionalities
however would require computational power from data centers.
Therefore, these data centers need to be properly placed and
carefully designed based on the volume of traffic they are meant
to serve. In this work, we first divide the city of Milan, Italy
into different zones using K-means clustering algorithm. We then
analyse the traffic profiles of these zones in the city using a
network operator’s Open Big Data set. We identify the optimal
placement of data centers as a facility location problem and
propose the use of Weiszfeld’s algorithm to solve it. Furthermore,
based on our analysis of traffic profiles in different zones, we
heuristically determine the ideal dimension of the data center in
each zone. Additionally, to aid operation and facilitate dynamic
utilization of data center resources, we use the state of the art
recurrent neural network models to predict the future traffic
demands according to past demand profiles of each area