- Title
- DQN approach for adaptive self-healing of VNFs in cloud-native network
- Creator
- Arulappan, Arunkumar; Mahanti, Aniket; Passi, Kalpdrum; Srinivasan, Thiruvenkadam; Naha, Ranesh; Raja, Gunasekaran
- Date
- 2024
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/199474
- Identifier
- vital:19202
- Identifier
-
https://doi.org/10.1109/ACCESS.2024.3365635
- Identifier
- ISSN:2169-3536 (ISSN)
- Abstract
- The transformation from physical network function to Virtual Network Function (VNF) requires a fundamental design change in how applications and services are tested and assured in a hybrid virtual network. Once the VNFs are onboarded in a cloud network infrastructure, operators need to test VNFs in real-time at the time of instantiation automatically. This paper explicitly analyses the problem of adaptive self-healing of a Virtual Machine (VM) allocated by the VNF with the Deep Reinforcement Learning (DRL) approach. The DRL-based big data collection and analytics engine performs aggregation to probe and analyze data for troubleshooting and performance management. This engine helps to determine corrective actions (self-healing), such as scaling or migrating VNFs. Hence, we proposed a Deep Queue Learning (DQL) based Deep Queue Networks (DQN) mechanism for self-healing VNFs in the virtualized infrastructure manager. Virtual network probes of closed-loop orchestration perform the automation of the VNF and provide analytics for real-time, policy-driven orchestration in an open networking automation platform through the stochastic gradient descent method for VNF service assurance and network reliability. The proposed DQN/DDQN mechanism optimizes the price and lowers the cost by 18% for resource usage without disrupting the Quality of Service (QoS) provided by the VNF. The outcome of adaptive self-healing of the VNFs enhances the computational performance by 27% compared to other state-of-the-art algorithms. © 2013 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 12, no. (2024), p. 34489-34504
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright @ 2024 The Authors
- Rights
- Open Access
- Subject
- 40 Engineering; 46 Information and computing sciences; Cloud-native deployment; Deep queue networks; Network intelligence; ONAP; Operational automation; Self-healing VNF
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