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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Chien, Hsu-TungSubject: search.filters.subject.Edge computing

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    Transparent AAA Security Design for Low-Latency MEC-Integrated Cellular Networks
    (2020-03-01)
    Li, Chi-Yu
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    Lin, Ying-Dar
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    Lai, Yuan-Cheng
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    Chien, Hsu-Tung
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    Huang, Yu-Sheng
    Multi-access edge computing (MEC) is a key enabler for low-latency services in the cellular network. It enables service requests to be served at the edge without reaching the Internet. However, this service model allows data traffic to bypass conventional security functions deployed at the core network, and may pose security threats. To examine its security impact, we analyze current security functions that span authentication, authorization, accounting (AAA), and access control, and then identify two major issues. First, conventional user authentication methods prevent MEC applications from achieving low-latency service offering. Second, current cellular authorization, accounting, and access control mechanisms hardly secure MEC traffic. We thus propose a transparent security design called MECsec to secure the MEC with low latency in the cellular network. It contains three main components: cellular-based OpenID Connect (OIDC) authentication, bitmap-based authorization/accounting, and two-tier hash-based access control. Especially, its transparent design does not need any changes on current cellular operations, and is standard-compliant. We implement and evaluate the MECsec prototype on an MEC-integrated LTE network architecture developed based on the OpenAirInterface (OAI) cellular platform. Our results show that the cellular-based OIDC can reduce delays of current authentication methods by up to 88.3%, and the other components can successfully defend against possible threats with negligible overhead.
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    Workload and Capacity Optimization for Cloud-Edge Computing Systems with Vertical and Horizontal Offloading
    (2020-03-01)
    Thai, Minh-Tuan
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    Lin, Ying-Dar
    ;
    Lai, Yuan-Cheng
    ;
    Chien, Hsu-Tung
    ;
    交大名義發表
    A collaborative integration between cloud and edge computing is proposed to be able to exploit the advantages of both technologies. However, most of the existing studies have only considered two-tier cloud-edge computing systems which merely support vertical offloading between local edge nodes and remote cloud servers. This paper thus proposes a generic architecture of cloud-edge computing with the aim of providing both vertical and horizontal offloading between service nodes. To investigate the effectiveness of the design for different operational scenarios, we formulate it as a workload and capacity optimization problem with the objective of minimizing the system computation and communication costs. Because such a mixed-integer nonlinear programming (MINLP) problem is NP-hard, we further develop an approximation algorithm which applies a branch-and-bound method to obtain optimal solutions iteratively. Experimental results show that such a cloud-edge computing architecture can significantly reduce total system costs by about 34%, compared to traditional designs which only support vertical offloading. Our results also indicate that, to accommodate the same number of input workloads, a heterogeneous service allocation scenario requires about a 23% higher system costs than a homogeneous scenario.