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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Lai, Yuan-ChengSubject: search.filters.subject.OpenFlow

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Now showing 1 - 4 of 4
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    Generic Validation Criteria and Methodologies for SDN Applications
    (2019-12-01)
    Lin, Ying-Dar
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    Lai, Yu-Kuen
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    Tsou, Yung-Liang
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    Lai, Yuan-Cheng
    ;
    Liou, En-Cheng
    Programmable control plane in software-defined networking (SDN), plays an essential role in the SDN architecture. The network function provided by the specialized hardware in a legacy network can be created in the form of software-based "SDN application" running on the controllers to manipulate entire network configurations. Therefore, the risk of having software bugs and errors in the SDN applications may threaten the normal operations of SDN networks. This paper presents systematic validation criteria and test cases based on the proposed novel methodologies for SDN application testing. The test framework can perform testbed build-up, generate desired packet sequences, and analyze results automatically. According to the results of a generic test suite, several issues are unveiled in the application under test (AUT). Some AUTs, which need to check all the incoming packets from Open-Flow switches, fail to meet the test criteria of burst packet-in and flow self-recycling. For most of the applications based on the Ryu controller, the evaluation results reveal that some are unable to recycle flow entries after they are unloaded. It is recommended that all flows populated by SDN applications must have timeout value specified to prevent unnecessary entries kept in the flow table.
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    Two-tier dynamic load balancing in SDN-enabled Wi-Fi networks
    (2018-11-01)
    Lin, Ying-Dar
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    Wang, Chih Chiang
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    Lu, Yi-Jen
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    Lai, Yuan-Cheng
    ;
    Yang, Hsi-Chang
    This work proposes a Software Defined Networking (SDN) solution to address Wi-Fi congestion due to an unevenly distributed load among access points (APs). The conventional methods generally let client stations learn of APs' load status and select APs distributively. However, such a client-driven approach lacks a global view to make precise load balancing decisions and may result in repeated changes in client-AP association. Although several studies proposed more efficient network-controlled methods to carry out Wi-Fi load balancing, some of them are distributed methods incurring excessive message exchange among customized APs, while the rest centralized methods are found to burden the central controller with unnecessary AP association decisions. In contrast, our solution adopts standardized OpenFlow protocol and SDN controller technology to Wi-Fi networks, organizing the SDN controller and the APs into a two-tier architecture so that the controller can evaluate the degree of load balancing among the APs and decide up to which load level the APs can accept association requests without consulting the controller. From our experiment results, our solution improves Wi-Fi's load balancing degree by 34-41%, and yields an improvement of 28-36% in Wi-Fi's re-association time over generic centralized load balancing methods with positive control.
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    Performance Modeling of SDN with NFV under or aside the Controller
    (2017-01-01)
    Fahmin, Ahmed
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    Lai, Yuan-Cheng
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    Hossain, Md. Shohrab
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    Lin, Ying-Dar
    ;
    Saha, Dipon
    Software Defined Networking (SDN) emphasizes the separation of data and control plane while network function virtualization (NFV) decouples network function from underlying hardware. Combining SDN with NFV would have many benefits, but the problem is how to integrate them. There are two possible architectures for such integration: the controller interacts with virtualized network functions (VNFs) or the switch interacts with VNFs. In this paper, the former is referred to as NFV under the controller (NFV_C) while the latter is called NFV aside the controller (NFV_AC). To the best of our knowledge, there is no analytical model for mathematically investigating the performance of such architectures. This paper therefore aims to carry out analytical modeling of SDN with NFV under or aside the controller. We model and analyze these two SDN+NFV architectures using M/M/1 queuing model and validate our analysis with various simulations. Results show that the analytical results well match the simulation results. Also a packet delay reduction of 54.67% can be achieved for NFV_AC over NFV_C, meaning that NFV_AC is a better architecture for integrating SDN with NFV.
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    Performance modeling and comparison of NFV integrated with SDN: Under or aside?
    (2018-07-01)
    Fahmin, Ahmed
    ;
    Lai, Yuan-Cheng
    ;
    Hossain, Md Shohrab
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    Lin, Ying-Dar
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    資訊工程學系
    Software Defined Networking (SDN) focuses on the separation of data and control plane, while network function virtualization (NFV) decouples network functions from underlying hardware. Combining SDN with NFV would have many benefits, but the problem is how to integrate them. There are two possible architectures for such integration: the controller interacts with virtualized network functions (VNFs), or the switch interacts with VNFs. In this paper, the former is referred to as NFV under the controller (NFV_C) while the latter is called NFV aside the controller (NFV_AC). To the best of our knowledge, there is no analytical model for mathematically investigating the performance of such architectures. This paper therefore reports on the analytical modeling of SDN with NFV under or aside the controller. We model and analyze these two SDN + NFV architectures using an M/M/1 queuing model and validate our analysis with various simulations. We show that the analytical results match the simulation results very well. Furthermore, a packet delay reduction of 68.83% can be achieved for NFVAC over NFVC, meaning that NFVAC is a better architecture for integrating SDN with NFV. We also consider feedback from the VNF to the switch. For low loads there is delay increase of 35.42% for high feedback (90%) in comparison with low feedback (10%). In the case of high loads we recorded a delay increase of 66.64%.