Edge View SNDlib Database formatted for VNE_CRS

Citation Author(s):
Cristiano L.
Moreira
Centro Universitário FEI
Reinaldo A. C.
Bianchi
Centro Universitário FEI
Submitted by:
Reinaldo Bianchi
Last updated:
Tue, 03/30/2021 - 18:09
DOI:
10.21227/500q-9485
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Abstract 

5G technologies have enabled new applications on a heterogeneous and distributed infrastructure edge which unifies hardware, network and software aimed at digital enabling. Based on the requirements of Industry 4.0, this infrastructure is developed using the cloud and fog computing sharing model, which should meet the needs of service level agreements in a convenient and optimized way, requiring an orchestration mechanism for the dynamic resource allocation. Among these mechanisms, virtual networks embedding (VNE) and dynamic resource management (DRM) have shown a way to define where and how edge technology should be used. This paper proposes a resource allocation algorithm, VNE_CRS, which uses an artificial intelligence technique called reinforcement learning to orchestrate multiple domains, benefiting from its characteristic of considering the whole problem, end-to-end, using different aspects of 5G Quality of Service Indicator (5QIs). Experiments were carried out in simulation comparing VNE_CRS with state-of-the-art algorithms for the multi domains Edge environment. Results have shown that the usage of reinforcement learning techniques to VNE resource allocation has shown performance gains. It can not only simplify the VNE architecture but also act as a full orchestration system that aims to the strategic long run results of whole infrastructure usage.

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