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Communication and Computing Cost Optimization of Meshed Hierarchical NFV Datacenters
(2020-01-01)
Kar, Binayak
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Wu, Eric Hsiao-Kuang
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Lin, Ying-Dar
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資訊工程學系
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Department of Computer Science
Telecommunication carriers of 5G-MEC are re-architecting their central offices and mobile base stations as datacenters with network function virtualization (NFV) technology. These datacenters (DCs) are known as edge datacenters that help network operators speed deployment and reduce costs. Previously, the use of NFV was limited to within a datacenter (DC) known as intra-DC. Recently, many studies have been conducted into the use of NFV across DCs, i.e., inter-DC. However, these NFV inter-DC architectures have limited communication between DCs with either horizontal or vertical connectivity. In this paper, we propose a generic architecture of such edge NFV datacenters with both horizontal and vertical connectivity, and demonstrate the consequences of both vertical and horizontal connectivity between DCs in terms of communication and computing costs. We formulate a cost optimization problem with latency and capacity as constraints by estimating the traffic dispatch rate between DCs. We propose a vertical-horizontal communication (VHC) heuristic solution to the NP-hard problem. Compared to horizontal connectivity, our results show that vertical connectivity helps to reduce computing costs by 10-30 & x0025;. However, both vertical and horizontal communications together can help to reduce such costs by 20-30 & x0025; compared to only vertical communication.
Editorial: Second Quarter 2020 IEEE Communications Surveys and Tutorials
(2020-01-01)
Lin, Ying-Dar
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交大名義發表
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National Chiao Tung University
I welcome you to the second issue of the IEEE Communications Surveys and Tutorials in 2020. This issue includes 20 papers covering different aspects of communication networks. In particular, these articles survey and tutor various issues in "Wireless and Optical Communications," "Vehicular Communications," "IoT and M2M," "Network Security," and "Internet Technologies." A brief account for each of these papers is given below.
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%.
Editorial: First Quarter 2020 IEEE Communications Surveys and Tutorials
(2020-01-01)
Lin, Ying-Dar
;
交大名義發表
;
National Chiao Tung University
I welcome you to the first issue of the IEEE COMMUNICATIONS SURVEYS AND TUTORIALS in 2020. This issue includes 25 papers covering different aspects of communication networks. In particular, these articles survey and tutor various issues in "Wireless and Cellular Communications," "5G Communications," "Vehicular and Sensor Communications," "IoT and M2M," "SDN and NFV," "Internet Technologies," and "Network Security." A brief account for each of these papers is given below.
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.
End-to-End Slicing With Optimized Communication and Computing Resource Allocation in Multi-Tenant 5G Systems
(2020-02-01)
Chien, Hsu-Tung
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Lin, Ying-Dar
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Lai, Chia-Lin
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Wang, Chien-Ting
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交大名義發表
Slicing is a key technology in 5G networks to provide scalability and flexibility in allocating computing and communication resources among multiple tenants. Typically, 5G networks have a 2-tier architecture consisting of a central office and transport network in the upper tier and a multi-access edge and radio access network in the lower tier. The tenants which share the 2-tier architecture typically have different service-dependent resource requirements. This study proposes an algorithm, designated as Upper-tier First with Latency-bounded Over-provisioning Prevention (UFLOP), to adjust the capacity and traffic allocation in such a way as to minimize the "over-provisioning ratio" while still satisfying the latency constraints and Service Level Agreements (SLAs) of the tenants. The performance of UFLOP is evaluated experimentally with a real testbed on an end-to-end slicing framework using three typical 5G services, namely Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency (URLLC), and massive Machine Type Connection (mMTC). It is shown that UFLOP successfully determines the critical traffic allocation ratio between the central office and the edge which achieves an over-provisioning ratio close to zero while still meeting the latency requirements. The results suggest optimal resource allocation ratios of 10:0, 1.5:8.5 and 7.8:2.2 for the eMBB, URLLC and mMTC applications, respectively. Furthermore, it is shown that the computing resource behaves as a bottleneck for the eMBB and mMTC services, while the communication resource serves as a bottleneck for the URLLC service.
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%.
Maximizing accuracy in multi-scanner malware detection systems
(2020-03-14)
Sakib, Muhammad N.
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Huang, Chin-Tser
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Lin, Ying-Dar
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資訊工程學系
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Department of Computer Science
A variety of anti-malware scanners have been developed for malware detection. Previous research has indicated that combining multiple different scanners can achieve better result compared to any single scanner. However, given the diversity in detection rates and accuracy of different anti-malware scanners, how to determine the best possible outcome of multi-scanner systems in terms of accuracy and how to achieve this best outcome remain formidable tasks. In this paper, we propose three models to capture the combined output of different combinations of anti-malware scanners based on the limited amount of historical information available. These models enable us to predict the accuracy level of each combination, which helps us to determine the optimal configuration of the multi-scanner detection system to achieve maximum accuracy. We also introduce two methods to identify a near-optimal subset of scanners that can help reduce scanning cost while under time constraint. From simulations over randomly generated hypothetical datasets and experiments conducted with real world malware and goodware datasets and anti-virus scanners, we found that our models perform well in predicting the optimal configuration and can achieve an accuracy as high as within 1% of true maximum. (C) 2019 Elsevier B.V. All rights reserved.
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.

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