Browsing by Author "Tung, Yuan-Hsin"

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A Collaborative Approach for Minimal-Cost Monitor Deployment in Cloud Environment
(2015-08-01)
Tung, Yuan-Hsin
;
Tseng, Shian-Shyong
;
Tsai, Wei-Tek
;
資訊工程學系
;
Department of Computer Science
Monitoring is widely applied in problem diagnosis, fault localization, and system maintenance. And since the cloud infrastructure is complex, the applications on the cloud are therefore complex, which makes monitoring in cloud more difficult. Rich monitors that contain composite and heterogeneous probes are often used in service-oriented system monitoring. These rich monitors often involve multiple entities, and the interpretation may require expert opinions from multiple domains. This paper proposes a knowledge-based collaborative monitoring approach to find out minimal cost monitor deployment in a cloud environment. The approach contains two main phases. In the knowledge acquisition phase, three acquisition tables, monitor-probe relationship matrix, cost of monitoring, and probe-problem dependence matrix, are generated according to diagnosis ontology and monitor ontology acquired from domain experts. And then based upon the three acquisition tables and three consensus building strategies, we formulate the problem of optimizing the cost of monitoring as an Integer Linear Programming (ILP) problem, which is NP-Complete. In the monitor deployment phase, the proposed algorithm applies two heuristic rules to address the problem. Three experiments are conducted to evaluate the performance of the proposed approach. The results from the experiments show that our approach is effective and produce quality approximate solutions in monitor deployment.
A Collaborative Approach for Minimal-Cost Monitor Deployment in Cloud Environment
(2015-08)
Tung, Yuan-Hsin
;
Tseng, Shian-Shyong
;
Tsai, Wei-Tek
;
資訊工程學系
;
Department of Computer Science
Monitoring is widely applied in problem diagnosis, fault localization, and system maintenance. And since the cloud infrastructure is complex, the applications on the cloud are therefore complex, which makes monitoring in cloud more difficult. Rich monitors that contain composite and heterogeneous probes are often used in service-oriented system monitoring. These rich monitors often involve multiple entities, and the interpretation may require expert opinions from multiple domains. This paper proposes a knowledge-based collaborative monitoring approach to find out minimal cost monitor deployment in a cloud environment. The approach contains two main phases. In the knowledge acquisition phase, three acquisition tables, monitor-probe relationship matrix, cost of monitoring, and probe-problem dependence matrix, are generated according to diagnosis ontology and monitor ontology acquired from domain experts. And then based upon the three acquisition tables and three consensus building strategies, we formulate the problem of optimizing the cost of monitoring as an Integer Linear Programming (ILP) problem, which is NP-Complete. In the monitor deployment phase, the proposed algorithm applies two heuristic rules to address the problem. Three experiments are conducted to evaluate the performance of the proposed approach. The results from the experiments show that our approach is effective and produce quality approximate solutions in monitor deployment.
A Cost-effective Approach to evaluating Security Vulnerability Scanner
(2013)
Tung, Yuan-Hsin
;
Tseng, Shian-Shyong
;
Shih, Jen-Feng
;
Shan, Hwai-Ling
;
資訊工程學系
Web applications are exposed to various threats and attacks, and therefore numerous tools are developed for detecting web application vulnerabilities. Many studies have focused on evaluating vulnerability scanners. An efficient evaluation approach for detection tools is essential and can be extremely helpful to the users. In this paper, we propose a cost-effective approach to evaluating vulnerability scanners by considering redundant vulnerability alert problem. We define the redundant alert problem in scanner evaluation with our motivational example and propose the advanced confusion matrix by extending two defined attributes, true duplication (TD) and false duplication (FD). Then we apply our proposed cost-effective evaluation approach and build up the web Vulnerability Scanner Testbed.
A novel approach to collaborative testing in a crowdsourcing environment
(2013-08-01)
Tung, Yuan-Hsin
;
Tseng, Shian-Shyong
;
資訊工程學系
;
Department of Computer Science
Software testing processes are generally labor-intensive and often involve substantial collaboration among testers, developers, and even users. However, considerable human resource capacity exists on the Internet in social networks, expert communities, or internet forums-referred to as crowds. Effectively using crowd resources to support collaborative testing is an interesting and challenging topic. This paper defines the collaborative testing problem in a crowd environment as an NP-Complete job assignment problem and formulates it as an integer linear programming (ILP) problem. Although package tools can be used to obtain the optimal solution to an ILP problem, computational complexity makes these tools unsuitable for solving large-scale problems. This study uses a greedy approach with four heuristic strategies to solve the problem. This is called the crowdsourcing-based collaborative testing approach. This approach includes two phases, training phase and testing phase. The training phase transforms the original problem into an ILP problem. The testing phase solves the ILP using heuristic strategies. A prototype system, called the Collaborative Testing System (COTS), is also implemented. The experiment results show that the proposed heuristic algorithms produce good quality approximate solutions in an acceptable timeframe. (c) 2013 Elsevier Inc. All rights reserved.
A novel approach to collaborative testing in a crowdsourcing environment (vol 86, pg 2143, 2013)
(2014-01-01)
Tung, Yuan-Hsin
;
Tseng, Shian-Shyong
;
Tsai, Wei-Tek
;
資訊工程學系
;
Department of Computer Science
A rule-based CBR approach for expert finding and problem diagnosis
(2010-03-15)
Tung, Yuan-Hsin
;
Tseng, Shian-Shyong
;
Weng, Jui-Feng
;
Lee, Tsung-Ping
;
Liao, Anthony Y. H.
It is important to find the person with right expertise and the appropriate solutions in the specific field to solve a critical situation in a large complex system such as an enterprise level application. In this paper, we apply the experts' knowledge to construct a solution retrieval system for expert finding and problem diagnosis. Firstly, we aim to utilize the experts' problem diagnosis knowledge which can identify the error type of problem to suggest the corresponding expert and retrieve the solution for specific error type. Therefore, how to find an efficient way to use domain knowledge and the corresponding experts has become an important issue. To transform experts' knowledge into the knowledge base of a solution retrieval system, the idea of developing a solution retrieval system based on hybrid approach using RBR (rule-based reasoning) and CBR (case-based reasoning), RCBR (rule-based CBR), is proposed in this research. Furthermore, we incorporate domain expertise into our methodology with role-based access control model to suggest appropriate expert for problem solving, and build a prototype system with expert finding and problem diagnosis for the complex system. The experimental results show that RCBR (rule-based CBR) can improve accuracy of retrieval cases and reduce retrieval time prominently. (C) 2009 Elsevier Ltd. All rights reserved.
Using a Progressive Model for Performance Diagnosis of Distributed System
(2010)
Tung, Yuan-Hsin
;
Tseng, Shian-Shyong
;
Shih, Pin-Zei
;
資訊工程學系
;
Department of Computer Science
It is important to keep an information system work properly with efficient performance in enterprise environment. The heterogeneous compositions and complex architectures of distributed systems make performance diagnosis difficult. When the system performance goes down, how to find out the root cause of inefficiency is a challenging task. Basically, the performance diagnosis is the iterative processes which should be repeatedly performed until the problem is solved. In this paper, we propose the three-phase progressive model for constructing the performance diagnosis system. The main purpose of our model is to acquire experts\' knowledge in performance diagnosis, and transform it into rule bases to perform performance diagnosis and locate the possible issue. By using this model, we further construct a prototype system to assist IT department employees to solve the problem progressively in trouble shooting. Two online systems in enterprise environment are adopted to evaluate the performance of the progressive model. The experimental results show that our prototype system can find out the root cause efficiently when system goes wrong.