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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Yuan, Shyan-MingSubject: search.filters.subject.Cloud Computing

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Now showing 1 - 4 of 4
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    Peer-to-Peer Based Parallel Genetic Algorithm
    (2017-01-01)
    Helal, Mohammed H. S.
    ;
    Fan, Chih-Tien
    ;
    Liu, De-You
    ;
    Yuan, Shyan-Ming
    ;
    資訊科學與工程研究所
    In an attempt to reduce communication overhead while maintaining high quality Genetic Algorithm results, this paper presents a Peer-to-Peer based Genetic Algorithm that suites both Parallel and Distributed environments. In order to improve our approach's applicability on both distributed and parallel environments, we experimented a set of different individual exchange intervals when running some well-known hard optimization problems. The proposed approach has been applied in different exchange rates and benchmarked with a regular Master-Slave based PGA on result quality and executions time. Experimental results show that our approach managed to find high quality results in shorter execution time compared to Master-Slave based PGA.
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    Using Google's Compute Engine Service Pricing as a Reference for Comparison Between Master-Slave and Island Model-Based Fully Distributed Genetic Algorithm
    (2017-01-01)
    Helal, Mohammed H. S.
    ;
    Liu, De-You
    ;
    Yuan, Shyan-Ming
    ;
    資訊科學與工程研究所
    ;
    Institute of Computer Science and Engineering
    Common Master-Slave based Parallel Genetic Algorithms can efficiently utilize multiple computational nodes working on a single process, which leads to finding solutions in less execution time. However, Master-Slave model causes high communication traffic between the nodes. On the other hand, Island-model based parallel implementation can reduce the amount of traffic between the nodes while reducing the efficiency in finding solutions. However it is discussed deeply in the literature, the tradeoff between execution time, traffic size and quality of result have not been estimated based on actual money cost. This paper presents a comparison between Master-Slave and Island-model based fully distributed implementations for Genetic Algorithm. The comparison is based on the actual money cost when running on Google Cloud Compute Engine Service. We implemented Genetic Algorithm in Master-Slave model and in Island model running in different migration rates in order to find a reasonable migration rate that can help find high quality results will the least possible cost.
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    A Hadoop based Web log Analysis System
    (2014-01-01)
    Wang, Chen-Hau
    ;
    Tsai, Ching-Tsorng
    ;
    Fan, Chia-Chen
    ;
    Yuan, Shyan-Ming
    ;
    資訊工程學系
    In recent years, cloud computing has been an important issue in the Field of research. Cloud computing employs distributed storage and distributed computing technology to achieve a large number of stored data, as well as fast data analysis and processing. As the rapid development of Internet technology, digital data showing explosive growth, the face of massive data processing, the traditional text software and relational database technology has been facing a bottleneck, presented the results are not very satisfactory. For this problem, the concept of cloud computing is a more appropriate choice. In this paper, based on the architecture of Hadoop with HDFS (Hadoop Distributed File System) and Hadoop NlapRecluce software framework and Pig Latin language, we design and implement an enterprise Web log analysis system. Experimental results, by analyzing daily Web log records, we get Application Server traffic trends, performance of program statistical reports, and performance reports of different intervals and different actions of program by user request. The main purpose of this system is to assist system administrators to quickly capture and analyze data hidden in the massive potential value, thus providing an important basis for business decisions.
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    File Deduplication with Cloud Storage File System
    (2013-01-01)
    Ku, Chan-I
    ;
    Luo, Guo-Heng
    ;
    Chang, Che-Pin
    ;
    Yuan, Shyan-Ming
    ;
    資訊工程學系
    The Hadoop Distributed File System (HDFS) is used to solve the storage problem of huge data, but does not provide a handling mechanism of duplicate files. In this study, the middle layer file system in the HBASE virtual architecture is used to do File Deduplicate in HDFS, with two architectures proposed according to different requires of the applied requirement reliability, therein one is RFD-HDFS (Reliable File Deduplicated HDFS) which is not permitted to have any errors and the other is FD-HDFS (File Deduplicated HDFS) which can tolerate very few errors. In addition to the advantage of the space complexity, the marginal benefits from it are explored. Assuming a popular video is uploaded to HDFS by one million users, through the Hadoop replication, they are divided into three million files to store, that is a practice wasting disk space very much and only by the cloud to remove repeats for effectively loading. By that, only three file spaces are taken up, namely the 100% utility of removing duplicate files reaches. The experimental architecture is a cloud based documentation system, like the version of EndNote Cloud, to simulate the cluster effect of massive database when the researcher synchronized the data with cloud storage.