AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The results of the investigation show that the P4 switch’s programmability has a significant impact on the SDN’s QoS and routing performance, and the P4QT was 94.9% more successful in optimized routing and avoiding the congestion.
Results show that live-metric adaptive path selection yields large throughput gains where path diversity can be exploited and behaves as a safe drop-in replacement for shortest-path forwarding otherwise.
This paper proposes SDN-based traffic engineering approach that combines in one control-plane link utilization monitoring, delay-aware path selection and proactive failure recovery, and proposes a greedy congestion and latency aware routing algorithm and a mechanism of pre-computation of backup paths.
Software Defined Networking (SDN) plays a major role in today’s modern cloud and 5G systems. It separates control from forwarding and allows operators to manage the network through programmable controllers. The actual performance of an SDN deployment is dependent on the selected controller and underlying topology. This...
A proactive congestion control algorithm based on a bucket-sort approach that groups packets that share the same destination network Internet Protocol (IP) address into a single bucket and transmits them as one packet is proposed.
Findings affirm that the suggested scalable control plane is practical in supporting large scale SDN implementation and is therefore applicable in future carrier grade, data center and wide area network deployments at realistic workloads with varying topological setups in the modern programmable networks in the world.
ConFlet is presented, a congestion-aware load balancing with Flowlet switching based on the cooperation of the data and control plane, where the control plane monitors the global network status to perform the optimized routing decision, and the data plane implements the flow splitting and path switching.
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