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Now showing 1 - 9 of 9

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.
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.
End-to-End Slicing as a Service with Computing and Communication Resource Allocation for Multi-Tenant 5G Systems
(2019-10-01)
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
5G services, such as Ultra-reliable and Low Latency Communications, Massive Machine Type Communications and Enhanced Mobile Broadband, support low (or ultra-low) latency, a huge number of connections, and high bandwidth. Typically, 5G platforms are shared by multiple tenants, where each tenant has different service deployment requirements and thus requires different amounts of resources from the lower tier (i.e., radio access network and mobile edge) and upper tier (i.e., transport network and central office) of the 5G architecture. Consequently, slicing is a key technology in 5G networks for providing scalability and flexibility in allocating the 5G resources with appropriate isolation. This study proposes an Endto- End Slicing as a Service framework for slicing both computing and communication resources across the full 2-tier multi-access edge computing architecture. The framework is implemented using open source tools. It is shown that the framework successfully isolates the 5G resources, which are computing and communication, between slices (slicing isolation effect ratio equals 1) and ensures that resources of the deployed slices are merely sufficient to meet the latency requirements of the tenants. Moreover, the experiments show that URLLC and mMTC services require more than 70 percent of the needed computing and communication resources be provided by the edge and RAN in order to satisfy their tighter latency requirements. Overall, the results suggest a central to edge resource allocation ratio of 9:1, 3:7, and 1:9 for the eMBB, URLLC, mMTC applications, respectively. Therefore,
Multi-operator Fairness in Transparent RAN Sharing
(2018-01-01)
Lin, Ying-Dar
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Chien, Hsu-Tung
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Chang, Hsien-Wen
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Lai, Chia-Lin
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資訊工程學系
Radio access network (RAN) sharing attracts much attention from telecom operators. However, current mechanisms applied to RAN sharing do not consider fairness among operators such that the RAN is not fairly shared, and either under-or over-utilized. Fairness cannot be guaranteed among operators because resources are distributed on a first-come-first-serve basis. "Soft-partition with Blocking and Dropping" (SBD)" is proposed to offer inter-operator fairness based on a "soft-partition" concept. Subscribers to an operator may overuse the predefined service-level-agreement (SLA) when the shared RAN is under-utilized, but become blocked or even dropped when over-utilized. According to our simulation results, SBD maintains inter-operator fairness at 0.997, even better than hard-partition at 0.98 and much better than no-partition at 0.6. Meanwhile, it retains high utilization of shared RAN at 98%. SBD reduces blocking rate from 35% of hard partition to almost 0%, while controlling dropping rate at 5%.
An Integrated Edge and Fog System for Future Communication Networks
(2018-01-01)
Kuo, Ping-Heng
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Mourad, Alain
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Lu, Chenguang
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Berg, Miguel
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Duquennoy, Simon
Put together, the edge and fog form a large diverse pool of computing and networking resources from different owners that can be leveraged towards low latency applications as well as for alleviating high traffic volume in future networks including 5G and beyond. This paper sets out a framework for the integration of edge and fog computing and networking leveraging on ongoing specifications by ETSI MEC ISG and the OpenFog Consortium. It also presents the technological gaps that need to be addressed before such an integrated solution can be developed. These noticeably include challenges relating to the volatility of resources, heterogeneity of underlying technologies, virtualization of devices, and security issues. The framework presented is a Launchpad for a complete solution under development by the 5G-CORAL consortium.
Multi-Operator Fairness in Transparent RAN Sharing by Soft-Partition With Blocking and Dropping Mechanism
(2018-12-01)
Chien, Hsu-Tung
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Lin, Ying-Dar
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Chang, Hsien-Wen
;
Lai, Chia-Lin
;
資訊工程學系
Radio access network (RAN) sharing has attracted significant attention from telecom operators as a means of accommodating data surges. However, current mechanisms for RAN sharing ignore the fairness issue among operators, and hence the RAN may be under- or over-utilized. Furthermore, the fairness among different operators cannot be guaranteed, since the RAN resources are distributed on a first come, first served basis. Accordingly, the present study proposes a "soft-partition with blocking and dropping" (SBD) mechanism that offers inter-operator fairness using a "soft-partition" approach. In particular, the operator subscribers are permitted to overuse the resources specified in the predefined service-level-agreement when the shared RAN is under-utilized, but are blocked (or even dropped) when the RAN is over-utilized. The simulation results show that SBD achieves an inter-operator fairness of 0.997, which is higher than that of both a hard-partition approach (0.98) and a no-partition approach (0.6) while maintaining a shared RAN utilization rate of 98%. Furthermore, SBD reduces the blocking rate from 35% (hard partition approach) to almost 0%, whereas controlling the dropping rate at 5%. Notably, the dropping rate can be reduced to almost 0% using a newly proposed bandwidth scale down procedure.
Three-Tier Capacity and Traffic Allocation for Core, Edges, and Devices for Mobile Edge Computing
(2018-09-01)
Lin, Ying-Dar
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Lai, Yuan-Cheng
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Huang, Jian-Xun
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Chien, Hsu-Tung
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資訊工程學系
In order to satisfy the 5G requirements of ultra-low latency, mobile edge computing (MEC)-based architecture, composed of three-tier nodes, core, edges, and devices, is proposed. In MEC-based architecture, previous studies focused on the control-plane issue, i.e., how to allocate traffic to be processed at different nodes to meet this ultra-low latency requirement. Also important is how to allocate the capacity to different nodes in the management plane so as to establish a minimal-capacity network. The objectives of this paper is to solve two problems: 1) to allocate the capacity of all nodes in MEC-based architecture so as to provide a minimal-capacity network and 2) to allocate the traffic to satisfy the latency percentage constraint, i.e., at least a percentage of traffic satisfying the latency constraint. In order to achieve these objectives, a two-phase iterative optimization (TPIO) method is proposed to try to optimize capacity and traffic allocation in MEC-based architecture. TPIO iteratively uses two phases to adjust capacity and traffic allocation respectively because they are tightly coupled. In the first phase, using queuing theory calculates the optimal traffic allocation under fixed allocated capacity, while in the second phase, allocated capacity is further reduced under fixed traffic allocation to satisfy the latency percentage constraint. Simulation results show that MEC-based architecture can save about 20.7% of capacity of two-tier architecture. Further, an extra 12.2% capacity must be forfeited when the percentage of satisfying latency is 90%, compared to 50%.
TRANSPARENT RAN SHARING OF 5G SMALL CELLS AND MACROCELLS
(2017-12-01)
Lin, Ying-Dar
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Chien, Hsu-Tung
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Chang, Hsien-Wen
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Lai, Chia-Lin
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Lin, Kun-Yi
To cope with the growth of mobile data traffic in 5G systems, telecom operators have to set up more small cells and macrocells in the same area. However, because of space limitations, it is difficult for each operator to do so, and RAN sharing would thus become mandatory for 5G systems. In this work, RANP is proposed, which will achieve transparent RAN sharing by setting up RANP between a base station to be shared and multiple core networks. Over and above the general one-level proxy architecture, an extended two-level architecture is proposed that will share both small cells and macrocells. According to our emulation results, additional packet processing time of a RANP is about 2 ms, 3.8 percent of the total end-to-end latency. Its impact on throughput is about 3 percent and can be regarded as irrelevant.

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