End-to-end performance-based autonomous vnf placement with adopted reinforcement learning

HIGHLIGHTS

• who: End-to-End Performance-Based Autonomous VNF and colleagues from the ) Real testbed and use case-based evaluation and validation: Unlike most past studies on VNF placement that use simulation environments, we present real-life experimental results with all performance evaluation and validation experiments conducted over GinFIRE testbed at the University of Bristol (UNIVBRIS) [8]We use the OSM MANO and take a use case-driven approach by adopting the dynamic environment scenario defined in the SCS [9] use case, which involves an e2e application running VNF video transcoding. Note though, that the applicability of our . . .
  

   

  

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