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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.
Smoothed Graphic User Interaction on Smartphones With Motion Prediction
(2020-04-01)
Lin, Ying-Dar
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Chu, Edward T-H
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Chang, Evan
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Lai, Yuan-Cheng
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資訊工程學系
The smoothness of human-smartphone interaction directly influences users experience and affects their purchase decisions. A commonly used method to improve user interaction of smartphones is to optimize the CPU scheduler. However, optimizing the CPU scheduler requires a modification of operating system. In addition, the improvement of the smoothness of human-smartphone interaction may be limited because the display subsystem is not optimized. Therefore, in this paper, we design a motion prediction queuing system, named MPQS, to improve the smoothness of human-smartphone interaction. For this, we use the information of vector, speed, movement, provided by the queuing mechanism of Android, to predict the movement of user-smartphone interaction. Based on the prediction, we then utilize available execution time between frames to perform image processing. We conducted a set of experiments on beagleboard-xM to evaluate the performance of MPQS. Our experiment results show that the proposed method can reduce the number of jank by up to 21.75%.
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
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Lai, Yuan-Cheng
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Chien, Hsu-Tung
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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.
Cost minimization in placing service chains for virtualized network functions
(2020-03-01)
Wang, Chien Ting
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Lin, Ying-Dar
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Wang, Chih-Chiang
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Lai, Yuan-Cheng
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交大名義發表
Network function virtualization (NFV) places network functions onto the virtual machines (VMs) of physical machines (PMs) located in data centers. In practice, a data flow may pass through multiple network functions, which collectively form a service chain across multiple VMs residing on the same or different PMs. Given a set of service chains, network operators have two options for placing them: (a) minimizing the number of VMs and PMs so as to reduce the server rental cost or (b) placing VMs running network functions belonging to the same service chain on the same or nearby PMs so as to reduce the network delay. In determining the optimal service chain placement, operators face the problem of minimizing the server cost while still satisfying the end-to-end delay constraint. The present study proposes an optimization model to solve this problem using a nonlinear programming (NLP) approach. The proposed model is used to explore various operational problems in the service chain placement field. The results suggest that the optimal cost ratio for PMs with high, hybrid, and low capacity, respectively, is equal to 4:2:1. Meanwhile, the maximum operating utilization rate should be limited to 55% in order to minimize the rental cost. Regarding quality of service (QoS) relaxation, the server cost reduces by 20%, 30%, and 32% as the end-to-end delay constraint is relaxed from 40 to 60, 80, and 100 ms, respectively. For the server location, the cost decreases by 25% when the high-capacity PMs are decentralized rather than centralized. Finally, the cost reduces by 40% as the repetition rate in the service chain increases from 0 to 2. A heuristic algorithm, designated as common sub chain placement first (CPF), is proposed to solve the service chain placement problem for large-scale problems (eg, 256 PMs). It is shown that the proposed algorithm reduces the solution time by up to 86% compared with the NLP optimization model, with an accuracy reduction of just 8%.
Full encapsulation or internal buffering in OpenFlow based hardware switches?
(2020-02-11)
Singh, Deepak
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Ng, Bryan
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Lai, Yuan-Cheng
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Lin, Ying-Dar
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Seah, Winston K. G.
Software-Defined Networking (SDN) enables programmability in the network through a software dependent control function. OpenFlow is a de-facto protocol for communication between an SDN switch and the controller. OpenFlow specifications generally allow two methods for packet encapsulation of data packets at the switch that require decisions from the controller, (a) full encapsulation, and (b) internal buffering. However, full encapsulation of data packets has been the default choice for packet processing, and internal buffering has not been explored much, especially for hardware switching. In this paper, we model and analyse the effect of internal buffering on the performance of an OpenFlow hardware switch. We compare queueing models for the switch with full encapsulation and internal buffering for hardware switching. The results show that internal buffering significantly reduces the average packet transfer delay by 85% and packet loss probability by 60%. The results further provide guidelines to network operators that internal buffering for hardware switching is useful for controllers with lower processing rates, especially in delay-sensitive applications running over SDN. For loss-sensitive applications running over SDN, the full encapsulation method is more robust handling flows with a high table miss probability. (C) 2019 Elsevier B.V. All rights reserved.
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
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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.
Toward UAV-Based Airborne Computing
(2019-12-01)
Lin, Kate Ching-Ju
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Wang, Hao-Chen
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Lai, Yuan-Cheng
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Lin, Ying-Dar
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交大名義發表
The next generation of mobile networks, 5G, aims at supporting lower end-to-end latency, higher reliability and higher throughput, which can be improved by MEC and multi-RAT offloading, respectively. With MEC, a base station in 5G can be equipped with computing power, which can be called as an edge in MEC. With the assistance of the edges, traffic with computational tasks can be directly executed in local servers, without forwarding the tasks to the cloud or core network. Edge computing hence reduces the latency and the traffic load significantly. Conventional multi- RAT offloading decides based only on either communication resources or computing resources. In this work, we argue that, to better utilize the communication and computing resources, neighboring edges should share their resources and cooperatively offload the requests from their clients. To this end, we introduced a double offloading mechanism, called LCCOP, to offload incoming traffic to the best pair of radio and edge subject to the endto- end latency of the requesting connections. We conduct simulations to compare LCCOP with the conventional offloading schemes. The results show that LCCOP's double offloading can significantly improve the satisfaction ratio by up to 83 percent and 143 percent, respectively, as compared to pure computation offloading and pure communication offloading.
A scalable and accurate distributed traffic generator with Fourier transformed distribution over multiple commodity platforms
(2019-10-15)
Chang, Ching-Hao
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Lin, Ying-Dar
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Lai, Yu-Kuen
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Lai, Yuan-Cheng
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資訊工程學系
The rapid growth of high-speed computer networks poses a challenge in the design of testing and verification equipment. The hardware-based packet generators that are often used in the verification process are accurate but costly. Software-based packet generators, on the other hand, are relatively low-cost but have a limited performance with low accuracy. This paper proposes a Fourier-based profile decomposition and formulation methodology for a distributed packet generating system, featuring good horizontal scalability and high accuracy. Different from traditional software-based packet generators, this proposed system extracts the traffic components from a specific traffic distribution applying Fourier transformation to generate traffic components. These traffic components are distributed to one or more worker nodes for packet generation, and thus achieving higher aggregated traffic rate in any given distribution. The system design is based on the Data Plane Development Kits (DPDK) framework to maximize the traffic generation performance. The accuracy and performance of the proposed system scale according to the number of worker nodes used. Currently, with multiple CPU cores and five workers, the proposed system can generate aggregated traffic of more than 40 Gbps in a Poisson distribution.
Analytical Modelling of Software and Hardware Switches with Internal Buffer in Software-Defined Networks
(2019-06-15)
Singh, Deepak
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Ng, Bryan
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Lai, Yuan-Cheng
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Lin, Ying-Dar
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Seah, Winston K. G.
Open Flow supports internal buffering of data packets in Software-Defined Networking (SDN) switch whereby a fraction of data packet header is sent to the controller instead of an entire data packet. This internal buffering increases the robustness and the utilization of the link between SDN switches and the controller by absorbing temporary burst of packets which may overwhelm the controller. Existing queuing models for an SDN have focused on the switches that immediately sends packets to the controller for decisioning, with no existing models investigating the impact of the internal buffer in SDN software and hardware switches. In this paper, we propose a unified queueing model to characterise the performance of SDN software and hardware switches with the internal buffer. This unified queueing model is an analytical tool for network engineers to predict a delay and loss during SDN deployments in delay and loss sensitive environments. Our results show that a hardware switch achieves up to 80% lower average packet transfer delay and 99% lower packet loss rate at the cost of requiring up to 50% more queue capacity than a software switch. The proposed models are validated with a discrete event simulation, where the error between 0.6% and 2.8% was observed for both average packet transfer delay and average packet loss rate. Moreover, a hardware switch outperforms a software switch with increasing number of hosts per switch suggesting that a hardware switch has better scalability. We use the insights from the model to develop guidelines that help network engineers decide between a software and hardware switch in their SDN deployments.
Proactive multipath routing with a predictive mechanism in software-defined networks
(1970-01-01)
Lin, Ying-Dar
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Liu, Te-Lung
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Wang, Shun-Hsien
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Lai, Yuan-Cheng
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資訊工程學系
With the growth of network traffic volume, link congestion cannot be avoided efficiently with conventional routing protocols. By utilizing the single shortest-path routing algorithm from link state advertisement information, standard routing protocols lack of global awareness and are difficult to be modified in a traditional network environment. Recently, software-defined network (SDN) provided innovative architecture for researchers to program their own network protocols. With SDN, we can divert heavy traffic to multiple paths in order to resolve link congestion. Furthermore, certain network traffics come in periodic fashion such as peak hours at working days so that we can leverage forecasting for resource management to improve its performance. In this paper, we propose a proactive multipath routing with a predictive mechanism (PMRP) to achieve high-performance congestion resolution. PMRP has two main concepts: (a) a proactive mechanism where PMRP deploys M/M/1 queue and traffic statistics to simulate weighted delay for possible combinations of multipaths placement of all subnet pairs, and leverage genetic algorithm for accelerating selection of optimized solution, and (b) a predictive mechanism whereby PMRP uses exponential smoothing for demand traffic volumes and variance predictions. Experimental results show a 49% reduction in average delay as compared with single shortest routing, and a 16% reduction in average delay compared with utilization & topology-aware multipath routing (UTAMP). With the predictive mechanism, PMRP can decrease an additional 20% average delay. Furthermore, PMRP reduces 93% of flow table usage on average as compared with UTAMP.

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