Optimizing Traffic Engineering for Resilient Services in NFV-Based Connected Autonomous Vehicles
The massive amount of data generated daily by various sensors equipped with connected autonomous vehicles (CAVs) can lead to a significant performance issue of data processing and transfer. Network Function Virtualization (NFV) is a promising approach to improving the performance of a CAV system. In...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/24/8446 |
| _version_ | 1827669609278341120 |
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| author | Tuan-Minh Pham Thi-Minh Nguyen |
| author_facet | Tuan-Minh Pham Thi-Minh Nguyen |
| author_sort | Tuan-Minh Pham |
| collection | DOAJ |
| description | The massive amount of data generated daily by various sensors equipped with connected autonomous vehicles (CAVs) can lead to a significant performance issue of data processing and transfer. Network Function Virtualization (NFV) is a promising approach to improving the performance of a CAV system. In an NFV framework, Virtual Network Function (VNF) instances can be placed in edge and cloud servers and connected together to enable a flexible CAV service with low latency. However, protecting a service function chain composed of several VNFs from a failure is challenging in an NFV-based CAV system (VCAV). We propose an integer linear programming (ILP) model and two approximation algorithms for resilient services to minimize the service disruption cost in a VCAV system when a failure occurs. The ILP model, referred to as TERO, allows us to obtain the optimal solution for traffic engineering, including the VNF placement and routing for resilient services with regard to dynamic routing. Our proposed algorithms based on heuristics (i.e., TERH) and reinforcement learning (i.e., TERA) provide an approximation solution for resilient services in a large-scale VCAV system. Evaluation results with real datasets and generated network topologies show that TERH and TERA can provide a solution close to the optimal result. It also suggests that TERA should be used in a highly dynamic VCAV system. |
| first_indexed | 2024-03-10T03:08:31Z |
| format | Article |
| id | doaj.art-44b259b96dff4aa28fb120d0a87d3b81 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:08:31Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-44b259b96dff4aa28fb120d0a87d3b812023-11-23T10:31:34ZengMDPI AGSensors1424-82202021-12-012124844610.3390/s21248446Optimizing Traffic Engineering for Resilient Services in NFV-Based Connected Autonomous VehiclesTuan-Minh Pham0Thi-Minh Nguyen1Faculty of Computer Science, Phenikaa University, Hanoi 12116, VietnamDepartment of Technology, Dong Nai Technology University, Bien Hoa City 760000, VietnamThe massive amount of data generated daily by various sensors equipped with connected autonomous vehicles (CAVs) can lead to a significant performance issue of data processing and transfer. Network Function Virtualization (NFV) is a promising approach to improving the performance of a CAV system. In an NFV framework, Virtual Network Function (VNF) instances can be placed in edge and cloud servers and connected together to enable a flexible CAV service with low latency. However, protecting a service function chain composed of several VNFs from a failure is challenging in an NFV-based CAV system (VCAV). We propose an integer linear programming (ILP) model and two approximation algorithms for resilient services to minimize the service disruption cost in a VCAV system when a failure occurs. The ILP model, referred to as TERO, allows us to obtain the optimal solution for traffic engineering, including the VNF placement and routing for resilient services with regard to dynamic routing. Our proposed algorithms based on heuristics (i.e., TERH) and reinforcement learning (i.e., TERA) provide an approximation solution for resilient services in a large-scale VCAV system. Evaluation results with real datasets and generated network topologies show that TERH and TERA can provide a solution close to the optimal result. It also suggests that TERA should be used in a highly dynamic VCAV system.https://www.mdpi.com/1424-8220/21/24/8446NFVVCAVresilient serviceoptimizationreinforcement learningconnected autonomous vehicles |
| spellingShingle | Tuan-Minh Pham Thi-Minh Nguyen Optimizing Traffic Engineering for Resilient Services in NFV-Based Connected Autonomous Vehicles Sensors NFV VCAV resilient service optimization reinforcement learning connected autonomous vehicles |
| title | Optimizing Traffic Engineering for Resilient Services in NFV-Based Connected Autonomous Vehicles |
| title_full | Optimizing Traffic Engineering for Resilient Services in NFV-Based Connected Autonomous Vehicles |
| title_fullStr | Optimizing Traffic Engineering for Resilient Services in NFV-Based Connected Autonomous Vehicles |
| title_full_unstemmed | Optimizing Traffic Engineering for Resilient Services in NFV-Based Connected Autonomous Vehicles |
| title_short | Optimizing Traffic Engineering for Resilient Services in NFV-Based Connected Autonomous Vehicles |
| title_sort | optimizing traffic engineering for resilient services in nfv based connected autonomous vehicles |
| topic | NFV VCAV resilient service optimization reinforcement learning connected autonomous vehicles |
| url | https://www.mdpi.com/1424-8220/21/24/8446 |
| work_keys_str_mv | AT tuanminhpham optimizingtrafficengineeringforresilientservicesinnfvbasedconnectedautonomousvehicles AT thiminhnguyen optimizingtrafficengineeringforresilientservicesinnfvbasedconnectedautonomousvehicles |