Optimizing Telematics Network Performance through Resource Virtualization in a Disruptive Environment: The Case of the IP/MPLS Core Network
- Patrick Dany Bavoua Kenfack
- Alphonse Binele Abana
- Emmanuel Tonye
- Paul Salomon Ngohe Ekam
- Gilles Herve J. Ngotty Mbang
Abstract
We offer a security solution to considerably reduce latency in an IP network by virtualizing the IP/MPLS core network. It consists of adapting a virtualization method to a complex IP network, presenting the simulation of the implementation of this virtualization and the modifications to be made to certain aspects of the code of the solution. These modifications would take into account the key performance indicators of the network in order to guarantee its security and the transmission through very wide bands of data. To do this, we use Software Defined Network (SDN) technology. It allows us to have an emergent, scalable, dynamic, secure, laudable and adaptable network architecture, making it suitable for today's high bandwidth applications and IT services. This architecture decouples network control and digital data transfer functions, making network control directly programmable and the underlying infrastructure abstracted to network applications and services. After describing the soft failover to a virtualized network, we present the new architecture that describes the separation of the control and data planes of the core of the IP/MPLS network of the Autonomous Port of Kribi (PAK) in Cameroon, as part of our research work. We will then present the aspects in which modifying the code would contribute to improving one of the key qualities of service, namely latency in the heart of the network. We go from latencies above 100 ms to latencies below 1 ms; finally we recommend the approach for a continuous modification of the code with a view to optimizing the performance of the network in a continuous process for the reduction of its latency.
- Full Text: PDF
- DOI:10.5539/nct.v8n2p1
Journal Metrics
(The data was calculated based on Google Scholar Citations)
1. Google-based Impact Factor (2021): 0.35
2. h-index (December 2021): 11
3. i10-index (December 2021): 11
4. h5-index (December 2021): N/A
5. h5-median (December 2021): N/A
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