DQN Approach for Adaptive Self-Healing of VNFs in Cloud-Native Network
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...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10433492/ |
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| author | Arunkumar Arulappan Aniket Mahanti Kalpdrum Passi Thiruvenkadam Srinivasan Ranesh Naha Gunasekaran Raja |
| author_facet | Arunkumar Arulappan Aniket Mahanti Kalpdrum Passi Thiruvenkadam Srinivasan Ranesh Naha Gunasekaran Raja |
| author_sort | Arunkumar Arulappan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:54:01Z |
| format | Article |
| id | doaj.art-7d9ee52f14f84b819429d7d6bfd35ffd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:54:01Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7d9ee52f14f84b819429d7d6bfd35ffd2024-03-26T17:46:36ZengIEEEIEEE Access2169-35362024-01-0112344893450410.1109/ACCESS.2024.336563510433492DQN Approach for Adaptive Self-Healing of VNFs in Cloud-Native NetworkArunkumar Arulappan0https://orcid.org/0000-0002-6583-2292Aniket Mahanti1https://orcid.org/0000-0002-6545-3073Kalpdrum Passi2https://orcid.org/0000-0002-7155-7901Thiruvenkadam Srinivasan3https://orcid.org/0000-0003-3761-9360Ranesh Naha4https://orcid.org/0000-0003-4165-9349Gunasekaran Raja5https://orcid.org/0000-0002-2253-7648School of Computer Science Engineering and Information Systems, VIT University, Vellore, IndiaSchool of Computer Science, The University of Auckland, Auckland, New ZealandSchool of Engineering and Computer Science, Laurentian University, Sudbury, ON, CanadaSchool of Electrical Engineering, VIT University, Vellore, IndiaCentre for Smart Analytics, Federation University Australia, Melbourne, VIC, AustraliaDepartment of Computer Technology, NGNLab, Anna University, MIT Campus, Chennai, IndiaThe 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.https://ieeexplore.ieee.org/document/10433492/Self-healing VNFdeep queue networksoperational automationcloud-native deploymentONAPnetwork intelligence |
| spellingShingle | Arunkumar Arulappan Aniket Mahanti Kalpdrum Passi Thiruvenkadam Srinivasan Ranesh Naha Gunasekaran Raja DQN Approach for Adaptive Self-Healing of VNFs in Cloud-Native Network IEEE Access Self-healing VNF deep queue networks operational automation cloud-native deployment ONAP network intelligence |
| title | DQN Approach for Adaptive Self-Healing of VNFs in Cloud-Native Network |
| title_full | DQN Approach for Adaptive Self-Healing of VNFs in Cloud-Native Network |
| title_fullStr | DQN Approach for Adaptive Self-Healing of VNFs in Cloud-Native Network |
| title_full_unstemmed | DQN Approach for Adaptive Self-Healing of VNFs in Cloud-Native Network |
| title_short | DQN Approach for Adaptive Self-Healing of VNFs in Cloud-Native Network |
| title_sort | dqn approach for adaptive self healing of vnfs in cloud native network |
| topic | Self-healing VNF deep queue networks operational automation cloud-native deployment ONAP network intelligence |
| url | https://ieeexplore.ieee.org/document/10433492/ |
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