Browsing by Author "Wu, Eric Hsiao-Kuang"

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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.
Energy Cost Optimization in Dynamic Placement of Virtualized Network Function Chains
(2018-03-01)
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
Network function virtualization (NFV), with its virtualization technologies, brings cloud computing to networking. Virtualized network functions (VNFs) are chained together to provide the required functionality at runtime on demand. It has a direct impact on power consumption depending on where and how these VNFs are placed and chained to accomplish certain demands as the power consumption of a physical machine (PM) depends on its traffic load. One of the advantages of VNF placement over traditional virtual machine placement is that virtualization is not limited solely to servers. The PMs, including the servers and varying loads to these machines and their utilization, are critical issues related to the network's energy consumption. In this paper, we designed a dynamic energy-saving model with NFV technology using an M/M/c queuing network with the minimum capacity policy where a certain amount of load is required to start the machine, which increases the utilization of the machine and avoids frequent changes of the machines' states. We formulate an energy-cost optimization problem with capacity and delay as constraints. We propose a dynamic placement of VNF chains (DPVC) heuristic solution to the NP-hard problem. The results show that the DPVC solution performs better and saves more energy. It uses 45%-55% less active nodes to satisfy the requested demands and increases the utilization of the active nodes by 40%-50% compared to other algorithms.
Simultaneous Heterogeneous Sensor Localization, Joint Tracking, and Upper Extremity Modeling for Stroke Rehabilitation
(2020-09-01)
Chen, Jiun-Fu
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Wang, Chieh-Chih
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Wu, Eric Hsiao-Kuang
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Chou, Cheng-Fu
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電機工程學系
In stroke rehabilitation systems and applications, reliability, accuracy, and occlusion should be taken into consideration. Unfortunately, most existing approaches focus primarily on the first two issues. However, during the stroke rehabilitation process, occlusion leads to incorrect judgements even for medical staff. In order to tackle these three important issues simultaneously, we propose a heterogeneous sensor fusion framework composed of an RGB-D camera and a wearable device to consider occlusion and provide robust joint locations for rehabilitation. To fuse multiple sensor measurements when compensating for occlusion, we apply heterogeneous sensor simultaneous localization, tracking, and modeling to estimate the locations of joints and sensors and construct an upper extremity model for occlusion situation. Virtual measurements based on this model are used to estimate the joint's location during occlusion, and a virtual relative orientation technique is applied to relax system limitations regarding orientation. Experimental results using the proposed approach with synthetic data and data collected from ten subjects show a 4.6 cm error on average and about 15 cm error on average during occlusion. This constitutes a more robust approach for stroke patients which takes into account these three important issues.