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Intelligent Resource Orchest ration for Service Function Chaining and Reactive Traffic Steering in Software Defined Network

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Abstract
In the network softwarization, Network Function Virtualisation (NFV) has shifted the standard of network services deployment and management for telecommunication. However, the allocation of physical resources to the Virtualised Network Functions (VNFs) dynamically, efficiently, and autonomously is one of the key tasks to achieve this objective. Network designing is a serious factor for the deployment of VNFs to create a Service Function Chaining (SFC) with efficient resource utilization and optimal path in the edge computing environment. The development of a self-driven Software Defined Networking (SDN) also requires an intelligent network design, especially to find optimum routing patterns for traffic steering that meet the goals set by administrators. But unfortunately, the current network designing methods do not fulfill the desired requirement for precise estimations of related performance metrics. Recently, the application of Artificial Intelligence (AI) is considered by the research community to operate and control the network. The Graph Neural Network (GNN) is adopted as an AI solution in the field of the network because GNN can understand the complex relationship between network traffic features, routing, and topology to produce an accurate estimation of relevant performance metrics. In this thesis, we propose the implementation of the Knowledge Defined Networking (KDN) system using well-known open-source technologies. GNN is incorporated into the system to predict the optimal path for SFC deployment and traffic steering. Two major applications; an SFC deployment application and a Traffic Steering application are developed for the knowledge plane to achieve SFC deployment in the edge clouds and traffic steering. An algorithm path selection module is developed to assist the SFC deployment application while deciding the best path selection and a policy configurator module is developed to translate a selected path into an SFC for the Open Source MANO (OSM). Similarly, a path selection module is developed to assist traffic steering applications while deciding the best path selection and a policy creation module is developed to translate a selected path into a flow rule policy for the Open Network Operating System (ONOS). Both applications interact with the GNN model for predicting the Key Performance Indicator (KPI) of paths in the network and provide the optimal path for SFC as well as traffic steering using the algorithm mentioned above. Most importantly, the OSM policy configurator and ONOS policy creation module reduce human error while configuring a desired service on the network. Monitoring modules are developed for both applications to fetch the network statistics after a few intervals of time and store them in the database which is required by the GNN module for path prediction. Lastly, ONOS and OSM clients are added in both applications to establish the communication between ONOS and OSM respectively. Another application, Traffic Flow Manager (TFM) is developed for the ONOS controller to perform as a broker between the knowledge plane and control plane. TFM is not only a broker but also performs real-time monitoring for network topology and deploy flow rules in network devices using the SDN controller. With the help of results, the proposed system is evaluated using the complete virtualized environment which is composed of ONOS, OpenStack, OSM. During the evaluation of the proposed system, we have achieved efficient resource utilization for SFC deployment and maximum throughput while steering traffic in the distributed edge clouds based on the optimal path selection considering less latency among the source and destination. Also, the proposed system reduces CPU utilization's load while steering the network traffic in a dynamic network topology. As compared to the existing state of the art SFC deployment in the edge cloud and traffic steering system, the proposed system is improved efficient resource utilization for SFC deployment up to 20%. Also, maximum throughput up to 5% and CPU load up to 13% while steering traffic in the distributed edge clouds.
Author(s)
Adeel Rafiq
Issued Date
2021
Awarded Date
2021. 2
Type
Dissertation
URI
https://oak.jejunu.ac.kr/handle/2020.oak/23494
Affiliation
제주대학교 대학원
Department
대학원 컴퓨터공학과
Advisor
Song, Wang Cheol
Table Of Contents
I. Introduction . 1
1.1 Network Softwarization. 2
1.2 Software Defined Networking . 3
1.3 Traffic Steering . 5
1.4 Service Function Chaining 6
1.5 Machine Learning . 7
1.5.1 Graphical Neural Network . 8
1.5.2 RouteNet Model 9
1.5.2.1 Message Passing . 9
1.5.2.4 Conceptual Prototype . 13
1.6 Knowledge Plane 14
1.7 Knowledge Defined Networking . 15
1.8 Trends in Knowledge Defined Networking 17
1.6.1 MILA ODL . 17
1.6.2 SDN + ML: HUAWEI . 18
1.6.3 ATHENA 19
1.9 Research Problems and Objectives 19
1.10 Thesis Organization. 21
II. Related Work . 22
2.1 Service Function Chaining 22
2.2 Traffic Steering 25
III. Major Components of Proposed Framework for SFC and Traffic Steering 28
3.1 Proposed System Architecture . 29
3.1.1 SFC Deployment Workflow 30
3.1.1 Traffic Steering Work Flow . 31
3.2 Knowledge Plane 33
3.2.1 Database 33
3.2.2 Optimal Path Prediction GNN Model . 35
3.2.2.2 Dataset 37
3.2.3 Traffic Flow Steering . 38
3.2.3.1 Path Selection . 39
3.2.3.2 ONOS Policy Configuration 40
3.2.3.3 ONOS Policy Creation 41
3.2.4 SFC Deployment . 44
3.2.4.1 Northbound Interface (NBI) Module . 44
3.2.4.2 Path Selection . 45
3.2.4.3 VNFD Configuration 48
3.2.4.4 NSD Configuration . 49
3.2.4.5 OSM Policy Configurator 50
3.3 Management Plane 52
3.3.1 ETSI NFV MANO 52
3.3.2 Open Source MANO (OSM) 53
3.4 Data Plane . 55
3.4.1 Network Function Virtualization Infrastructure . 55
3.4.2 Edge Clouds Network . 56
3.5 Control Plane 58
3.5.1 ONOS Controller 58
3.5.2 Traffic Flow Manager . 60
IV. Experiment Environment, Results and Analysis . 62
4.1 Implementation Environment 63
4.1.1 Application in Knowledge Plane 63
4.1.2 SDN Controller and Traffic Flow Manager 64
4.1.3 OpenStack Edge Clouds and Controller . 65
4.1.4 Deployment and Configuration of OSM . 66
4.2 Accuracy Evaluation of RouteNet Model. 67
4.2.1 Training and Evaluation . 67
4.2.2 Generalization Capabilities Evaluation . 70
4.2.3 Performance evaluation against link failure 72
4.3 SFC Deployment Demo 73
4.4 Network Topology in OSM . 78
4.5 Network Topology Characteristic 79
4.6 Dynamic Length SFC and Resources Demand . 80
4.7 Throughput Comparison 82
V. Conclusions. 85
Bibliography 88
Degree
Doctor
Publisher
제주대학교 대학원
Citation
Adeel Rafiq. (2021). Intelligent Resource Orchest ration for Service Function Chaining and Reactive Traffic Steering in Software Defined Network
Appears in Collections:
General Graduate School > Computer Engineering
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