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Currently, telecommunication research communities are striving towards the adoption of Zero-touch network and Service Management (ZSM) in Network Function Virtualisation (NFV) orchestration. Contemporary efforts on adopting Machine Learning (ML) and Artificial Intelligence (AI) have caused an upsurge of ZSM application in the VNF space. While ML and AI complement the ZSM goals for building the intelligent NFV orchestration, a deep knowledge about the resource consumption by Network Services (NSs) and its constituent Virtual Network Functions (VNFs) is required, which would enable AI and ML models to manage the available resources better and enhance user experience. In this paper, we propose a Novel Autonomous Profiling (NAP) method that not only predicts the optimum network load a VNF can support but also estimates the required resources in terms of CPU, Memory, and Network, to meet the performance targets and workload by utilising ML techniques. Our performance evaluation results on real datasets show that the output of NAP can be used in the next generation of NFV orchestration.
|Number of pages||14|
|Journal||IEEE Transactions on Network and Service Management|
|Early online date||15 Dec 2020|
|Publication status||Published - 1 Mar 2021|
Bibliographical noteFunding Information:
This work has received funding from the U.K.: EPSRC projects INITIATE (EP/P003974/1) and TOUCAN (EP/L020009/1) and from the EU: H2020 projects 5G-VICTORI and MATILDA (grant agreements 857201, 761898). The associate editor coordinating the review of this article and approving it for publication was J.-M. Kang.
© 2021 IEEE.
- Load modeling
- multi-objective resource configuration.
- Next generation networking
- Performance KPIs
- Prediction algorithms
- Time measurement
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- 2 Finished
1/02/17 → 31/01/21