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RAFALE: Rethinking the provisioning of virtuAl network services using a Fast and scAlable machine LEarning approach

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Abstract

Network Function Virtualization (NFV) has been identified to revamp the provisioning of next-generation network services. This new paradigm allows cloud and network/service providers to compose their network services, also known as service function chains (SFCs), in an agile way since the software of the network function is decoupled from the legacy hardware. To reap the benefits of this new technology, there is a need for novel mechanisms that help cloud and network/service providers deploy the increasingly complex virtual network services seamlessly, efficiently, and in a time-efficient way. Existing state-of-the-art techniques often rely on the Integer Linear Programming framework, heuristics/metaheuristics, and greedy methods to deploy the services function chains. However, these techniques although reasonable and acceptable, still suffer from several key limitations: convergence time and scalability. To this end, we propose RAFALE, a suite of solution techniques, to tame this complexity by leveraging the concept of similarity from machine learning and skip-gram modeling framework. To the best of our knowledge, we are the first to tackle these key limitations and propose a suite of solutions to them. RAFALE, a novel approach proposed to find the similarity between the new incoming virtual network service request and all the already-deployed services to learn from the previous experience of deploying techniques and use the same or close similar provisioning techniques. RAFALE is the first and the only method that develops the idea of detecting the similarity between virtual network services. Experimental results show that RAFALE reduces greatly the convergence time needed for provisioning virtual network services and can scale to 100 virtual network functions per virtual network service compared to the state-of-the-art. The Experimental results prove that RAFALE accomplished the NFV promises; decreasing the time and complexity of managing and deploying the virtual services, and providing a solution that is agile, faster, and scalable to deploy the new service requests by skipping one or more service provisioning steps (i.e., detecting and resolving the conflicts among policies, placement, and chaining) while satisfying the validated NFV policies.

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Acknowledgements

This work has been supported by Ericsson Canada and the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Authors and Affiliations

  1. Department of Software Engineering and IT, École de Technologie Supérieure, University of Quebec, Montreal, QC, H3C 1K3, Canada

    Hanan Suwi, Laaziz Lahlou & Nadjia Kara

  2. Ericsson Canada Inc., Montreal, QC, H4S 0B6, Canada

    Claes Edstrom

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  1. Hanan Suwi
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  2. Laaziz Lahlou
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Correspondence to Hanan Suwi.

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Suwi, H., Lahlou, L., Kara, N. et al. RAFALE: Rethinking the provisioning of virtuAl network services using a Fast and scAlable machine LEarning approach. J Supercomput 78, 15786–15819 (2022). https://doi.org/10.1007/s11227-022-04492-6

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