Browsing by Author "Lin, Che-Min"

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A first near real-time seismology-based landquake monitoring system
(2017-03-02)
Chao, Wei-An
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Wu, Yih-Min
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Zhao, Li
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Chen, Hongey
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Chen, Yue-Gau
Hazards from gravity-driven instabilities on hillslope (termed 'landquake' in this study) are an important problem facing us today. Rapid detection of landquake events is crucial for hazard mitigation and emergency response. Based on the real-time broadband data in Taiwan, we have developed a near real-time landquake monitoring system, which is a fully automatic process based on waveform inversion that yields source information (e. g., location and mechanism) and identifies the landquake source by examining waveform fitness for different types of source mechanisms. This system has been successfully tested offline using seismic records during the passage of the 2009 Typhoon Morakot in Taiwan and has been in online operation during the typhoon season in 2015. In practice, certain levels of station coverage (station gap < 180 degrees), signal-to-noise ratio (SNR >= 5.0), and a threshold of event size (volume > 10(6) m(3) and area > 0.20 km(2)) are required to ensure good performance (fitness > 0.6 for successful source identification) of the system, which can be readily implemented in other places in the world with real-time seismic networks and high landquake activities.
Machine Learning-Based Configuration Parameter Tuning on Hadoop System
(2015)
Chen, Chi-Ou
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Zhuo, Ye-Qi
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Yeh, Chao-Chun
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Lin, Che-Min
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Liao, Shih-wei
Apache Hadoop system is a software framework with the capability to process large-scale datasets across a cluster of distributed machines using MapReduce programming model. However, there are two main challenges for system administrators to manage the Hadoop system; (1) system administrators are difficult to tune the parameters appropriately since the behaviors and characteristics of large-scale distributed systems are too complicated; (2) there are dozens of configuration parameters affecting the system performance which makes the configuration parameters tuning task becomes troublesome. In this paper, we focus on optimizing the Hadoop MapReduce job performance by tuning configuration parameters, and then we propose an analytical method to help system administrators choose approximately optimal configuration parameters depending on the characteristics of each application. Our approach has two key phases: prediction and optimization phase. The prediction phase is to estimate the performance of a MapReduce job, whereas the optimization phase is to search the approximately optimal configuration parameters strategically by invoking the predictor repeatedly. In our evaluation results, our work can help system administrators to improve the performance about 2X to 8X better than traditional methods.
Trickle: Resilient real-time video multicasting for dynamic peers with limited or asymmetric network connectivity
(2006)
Guo, Yu-Hsuang
;
Zao, John K.
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Peng, Wen-Hsiao
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Huang, Lin-Shung
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Kuo, Fang-Po
Some of the most challenging scenarios for peer-to-peer multimedia applications arise when the applications require real-time interactions among their users. In those cases, the expectation of sub-second responses prohibits the use of popular P2P IPTV software because those programs invariably use large video buffers to amortize the propagation delays of individual frames and thus cause notable and dispersed viewing latencies among their users. The performance of these programs degrade even further if the users are connected to home networks that offer narrow uplink channels or through wireless links that experience frequent throughput fluctuations. In order to overcome these shortcomings, we develop Trickle, a peer-to-peer real-time media streaming system that can transport H.264 video streams with low link stresses (less than 250Kb/s) and stable sub-second frame delays through the use of erasure correction codes along with the clever construction of multiple mullicast trees and the recruitment of many peer helpers. This paper presents the first fruits of our work including the principles and mechanisms of Trickle, its simulated performance based on H.264 video traces and its merit comparisons against SplitStream, the first application layer multicasting protocol for video streaming, and CoolStreansing, a news-making P2P IPTV program that works like BitTorrent.