Browsing by Author "Lo, Win-Tsung"

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An Architecture for Cloud Service Testing and Real Time Management
(2015)
Lo, Win-Tsung
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Liu, Xiao-Long
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Sheu, Ruey-Kai
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Yuan, Shyan-Ming
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Chang, Chun-Yu
Cloud service testing ensures that services run properly and meet the Service Level Agreement (SLA) requirements. However, performance problems of a cloud service, such as the availability and reliability, are difficult to diagnose because these issues might be caused from different system components. To solve these problems, this study proposes an architecture for testing environment configuration and quality estimation for both of fault diagnosis and bottleneck detection. The proposed system has two components: offline testing and online management. In offline testing, target service are tested by using the proposed testing module and corresponding metrics are collected for further analysis. The analyzed results which are separated between fault diagnosis and bottleneck detection will be stored in knowledge databases. Finally, online management module will automatically suggest how to do when facing this kind of problem according to knowledge based diagnosis.
Automatic cloud service testing and bottleneck detection system with scaling recommendation
(2020-01-10)
Liu, Xiaolong
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Sheu, Ruey-Kai
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Lo, Win-Tsung
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Yuan, Shyan-Ming
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資訊工程學系
Performance problems in a cloud service are difficult to diagnose because they may be caused by various system components. This study proposes an automatic cloud service testing and bottleneck detection system that is applicable to different types of services. With the proposed test module, the user can customize test scenarios to automatically test and collect the corresponding metrics of the target service. Afterward, the proposed bottleneck detection algorithm analyzes the collected metrics and determines whether a bottleneck is presented in the target system. The bottleneck detection module also provides a scaling recommendation for the service provider to facilitate the service system reconfiguration. The experimental results reveal that the proposed system could detect a potential bottleneck in a service system accurately. In accordance with the scaling recommendation, the performance of the target cloud service can be improved efficiently after reconfiguration. Therefore, usage of the proposed system can ensure a high quality of service, and the service level objective could be fulfilled.
Automatic Test Environment Deployment and Service Testing Model in the Cloud
(2016-05)
Lo, Win-Tsung
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Sheu, Ruey-Kai
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Yuan, Shyan-Ming
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Luo, Guo-Heng
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資訊工程學系
Cloud testing has become a crucial concern due to the explosion of cloud services and requirements of reliable and high-quality service. The technologies of virtualization and infrastructures of cloud services have enabled service providers to deploy their services conveniently. However, current test models decrease the flexibility of test scenarios and test client customization. For solving these problems, this study proposes a testing service in the cloud to help the testing environment generation, deployment and execution of test scenarios. The tester can customize test scenarios involving clients, network topologies, and test scripts. In the paper, a demonstration test toolkit was implemented, and also used for testing distributed file system with various numbers of hosts, threads, and virtual network topologies.
Automatic Test Environment Deployment and Service Testing Model in the Cloud
(2016-05-01)
Lo, Win-Tsung
;
Sheu, Ruey-Kai
;
Yuan, Shyan-Ming
;
Luo, Guo-Heng
;
資訊工程學系
Cloud testing has become a crucial concern due to the explosion of cloud services and requirements of reliable and high-quality service. The technologies of virtualization and infrastructures of cloud services have enabled service providers to deploy their services conveniently. However, current test models decrease the flexibility of test scenarios and test client customization. For solving these problems, this study proposes a testing service in the cloud to help the testing environment generation, deployment and execution of test scenarios. The tester can customize test scenarios involving clients, network topologies, and test scripts. In the paper, a demonstration test toolkit was implemented, and also used for testing distributed file system with various numbers of hosts, threads, and virtual network topologies.
CUDT: A CUDA Based Decision Tree Algorithm
(2014-01-01)
Lo, Win-Tsung
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Chang, Yue-Shan
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Sheu, Ruey-Kai
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Chiu, Chun-Chieh
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Yuan, Shyan-Ming
Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture), which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5 similar to 55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set.
Design and Implementation of File Deduplication Framework on HDFS
(2014)
Sheu, Ruey-Kai
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Yuan, Shyan-Ming
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Lo, Win-Tsung
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Ku, Chan-I
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資訊科學與工程研究所
File systems are designed to control how files are stored and retrieved. Without knowing the context and semantics of file contents, file systems often contain duplicate copies and result in redundant consumptions of storage space and network bandwidth. It has been a complex and challenging issue for enterprises to seek deduplication technologies to reduce cost and increase the storage efficiency. To solve such problem, researchers proposed in-line or offline solutions for primary storages or backup systems at the subfile or whole-file level. Some of the technologies are used for file servers and database systems. Fewer studies focus on the cloud file system deduplication technologies at the application level, especially for the Hadoop distributed file system. It is the goal of this paper to design a file deduplication framework on Hadoop distributed file system for cloud application developers. The architecture, interface, and implementation experiences are also shared in this paper.
Mobile cloud-based depression diagnosis using an ontology and a Bayesian network
(2015-02-01)
Chang, Yue-Shan
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Fan, Chih-Tien
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Lo, Win-Tsung
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Hung, Wan-Chun
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Yuan, Shyan-Ming
Recently, depression has becomes a widespread disease throughout the world. However, most people are not aware of the possibility of becoming depressed during their daily lives. Therefore, obtaining an accurate diagnosis of depression is an important issue in healthcare. In this study, we built an inference model based on an ontology and a Bayesian network to infer the possibility of becoming depressed, and we implemented a prototype using a mobile agent platform as a proof-of-concept in the mobile cloud. We developed an ontology model based on the terminology used to describe depression and we utilized a Bayesian network to infer the probability of becoming depressed. We also implemented the system using multi-agents to run on the Android platform, thereby demonstrating the feasibility of this method, and we addressed various implementation issues. The results showed that our method may be useful for inferring a diagnosis of depression. (C) 2014 Elsevier B.V. All rights reserved.