Browsing by Author "Wu, Chia-Ming"

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Can CSR reduce stock price crash risk? Evidence from China's energy industry
(2019-05-01)
Wu, Chia-Ming
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Hu, Jin-Li
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經營管理研究所
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Institute of Business and Management
This study explores the effect of CSR on reducing a firm's stock price crash risk in China's energy industry by using a regression model and further investigates the effect of items within CSR (e.g., corporate governance and environment protection) on reducing said risk to find which item is important for CSR for the energy industry. Covering a total of 100 energy firms including electricity generation and mining from 2014 to 2016, this study observes that CSR significantly affects stock price crash risk in the energy industry, as employee protection, environmental protection, product quality control, consumers' right protection, and supply chain partners all significantly reduce an energy firm's stock price crash risk. We also employ the industry as our moderator to test whether different sub-industries have varying correlations between CSR and stock price crash risk, finding that the electricity generation firms with higher CSR scores especially in environmental protection, promoting technology innovation, and corporate image have significantly lower stock price crash risk, while the mining firms do not. These results suggest that compared to other CSR items, investors should pay more attention on pollution improvement and trading provisions that decrease this risk more effectively, especially in the electricity generation industry.
High-speed data-plane packet aggregation and disaggregation by P4 switches
(2019-09-15)
Wang, Shie-Yuan
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Wu, Chia-Ming
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Lin, Yi-Bing
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Huang, Ching-Chun
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資訊工程學系
In this paper, we propose novel approaches that utilize the header manipulations of the P4 (Programming Protocol-Independent Packet Processor) switches to aggregate small IoT packets into a large one, transmit it over a network, and then disaggregate it back to the original small packets, all in the data plane of the hardware P4 switch to provide high throughputs. Packet aggregation and disaggregation provide many important benefits and have been proposed and performed in the past. However, most existing approaches perform packet aggregation and disaggregation in the control plane either by the switch CPU or by the server CPU, resulting in low throughputs. Our work is the first work that designs and implements packet aggregation and disaggregation purely in the pipelines of the switching ASIC. In this paper, we present the design and implementation of our approaches, their measured throughputs, and the insights that we have obtained from this pioneering work.
The SDN Approach for the Aggregation/Disaggregation of Sensor Data
(2018-07-01)
Lin, Yi-Bing
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Wang, Shie-Yuan
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Huang, Ching-Chun
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Wu, Chia-Ming
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資訊工程學系
In many Internet of Things (IoT) applications, large numbers of small sensor data are delivered in the network, which may cause heavy traffics. To reduce the number of messages delivered from the sensor devices to the IoT server, a promising approach is to aggregate several small IoT messages into a large packet before they are delivered through the network. When the packets arrive at the destination, they are disaggregated into the original IoT messages. In the existing solutions, packet aggregation/disaggregation is performed by software at the server, which results in long delays and low throughputs. To resolve the above issue, this paper utilizes the programmable Software Defined Networking (SDN) switch to program quick packet aggregation and disaggregation. Specifically, we consider the Programming Protocol-Independent Packet Processor (P4) technology. We design and develop novel P4 programs for aggregation and disaggregation in commercial P4 switches. Our study indicates that packet aggregation can be achieved in a P4 switch with its line rate (without extra packet processing cost). On the other hand, to disaggregate a packet that combines N IoT messages, the processing time is about the same as processing N individual IoT messages. Our implementation conducts IoT message aggregation at the highest bit rate (100 Gbps) that has not been found in the literature. We further propose to provide a small buffer in the P4 switch to significantly reduce the processing power for disaggregating a packet.