Browsing by Author "Chen, Lung-Pin"

Now showing 1 - 8 of 8

A Fault-Tolerant ONVIF Standard Extension for Cloud-Based Surveillance Systems
(2017-01-01)
Chiao, Hsin-Ta
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Lin, Chia-Feng
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Chang, Yue-Shan
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Sheu, Ruey-Kai
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Chen, Lung-Pin
ONVIF (Open Network Video Interface Forum) is a popular standard for the video surveillance industries. Since ONVIF specifications do not provide fault-tolerant functions to deal with NVT (Network Video Transmitter) or NVS (Network Video Storage) failure, we propose a fault-tolerant solution for both ONVIF NVT and NVS in this paper. The proposed fault-tolerant extension is also possible to be deployed over cloud-based environments, such as IaaS (Infrastructure as a Service) platforms. The proposed solution is evaluated by the following three performance metrics: the access response time for ONVIF web services, the video recording interrupt duration due to NVS failure, and the video black out time of the surveillance video monitoring system. The experimental results show that all the metrics are within the acceptable range for a real world video surveillance system.
Efficient online algorithm for identifying useless states in distributed systems
(2011-04-01)
Chen, Lung-Pin
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Sun, Der-Johng
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Chu, William
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資訊工程學系
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Department of Computer Science
In a distributed system, detecting whether a given logical predicate is true on the global states is fundamental for testing and debugging the program. Detecting predicates by examining all global states is intractable due to the combinatorial nature of the problem. This work designs an efficient online algorithm that identifies the consistent and useless states each time a new state is reported. This paper formulates the optimality of detecting algorithms in terms of pseudo states, which are employed to represent unknown states to the monitor process. Based on this technique, memory space of the debugger can be minimized by removing the useless states without affecting the debugging results. While minimizing memory space, the proposed algorithm requires only O(p (2) M) time in total, where p is the number of processes, and M is the number of reported states.
Enhancing Parallel Game-Tree Searches by using Idle Resources of a High Performance Render Farm
(2015)
Chen, Lung-Pin
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Wu, I-Chen
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Liang, Geng-Ze
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資訊工程學系
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Department of Computer Science
Due to the complex scheduling criteria, resource fragmentation often occurs in modern high performance computing systems. This paper addresses both of the policy and technical issues of using unstable resources formed by fragmentation. We propose that the parallel Monte-Carlo tree search (MCTS) can be implemented as lightweight single-core tasks to adapt to the unstable fractured resources. We develop a broker to collect idle resources in a high performance render farm and make use of them to execute MCTS tasks. The broker tends to assign promising tree nodes to reliable and responsive processors to confirm best-first search. Our work demonstrates a successful integration in which the MCTS computation gains significant resources without interfering with the render farm tasks.
Incremental Digital Content Object Delivering in Distributed Systems
(2010-06-01)
Chen, Lung-Pin
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Wu, I-Chen
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Chu, William
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Hong, Jhen-You
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Ho, Meng-Yuan
Deploying and managing content objects efficiently is critical for building a scalable and transparent content delivery system. This paper investigates the advanced incremental deploying problem of which the objects are delivered in a successive manner. Recently, the researchers show that the minimum-cost content deployment can be obtained by reducing the problem to the well-known network flow problem. In this paper, the maximum flow algorithm for a single graph is extended to the incremental growing graph. Based on this extension, an efficient incremental content deployment algorithm is developed in this work.
NONOGRAM TOURNAMENT IN OLYMPIAD YOKOHAMA 2013
(2014-03-01)
Chen, Lung-Pin
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Sun, Der-Johng
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Tseng, Wen-Jie
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資訊工程學系
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Department of Computer Science
ON DESIGN OF BROWSER-ORIENTED DATA EXTRACTION SYSTEM AND THE PLUG-INS
(2010-04-01)
Su, Jui-Yuan
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Sun, Der-Johng
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Wu, I-Chen
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Chen, Lung-Pin
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資訊工程學系
Web data extraction systems currently not only extract data on web pages but also need to navigate to the target correctly. Most traditional web data extraction systems extract URLs directly from web pages, and then access next pages using the extracted URLs. This data extraction approach is herein called the URL-oriented data extraction approach in this paper. However, currently, more and more web pages use script functions, such as Java Script, to access next pages and may hide URLs inside these functions, making it difficult to extract URLs. In order to solve this problem, a new data extraction approach, named the browser-oriented data extraction (BODE) approach, is proposed to be built on top of browser objects access pages by simulating users' operations on browsers to invoke script functions. A data extraction system based on this approach is called the BODE system. Based on the BODE approach, this paper designed a BODE system with the following contributions: (a) Define a scripting language, named the BODED (Browser-Oriented Data Extraction Description) language, which instructs the BODE system to extract data. (b) Design a plug-ins that can be used to extend the functionalities of the BODE system. (c) Design a visualization tool to support the data extraction in the BODE system. (d) Illustrate the plug-in mechanism of the BODE system by automating the playing of the game Connect6 over an Internet game site.
Reinforcement Learning based Fragment-Aware Scheduling for High Utilization HPC Platforms
(2019-01-01)
Chen, Lung-Pin
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Wu, I-Chen
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Chang, Yen-Ling
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資訊工程學系
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Department of Computer Science
Due to high capacity and complex scheduling activities, a HPC platform often creates resource fragments with low usability. This paper develops a novel fragment-aware scheduling approach which improves system utilization by fitting elastic lightweight tasks to the fragments of resources dynamically. The new approach employs a threshold to determine the balancing factor between the length of tasks and the degree of granularity of the resource fragments. We employ the PPO reinforcement learning approach to train a neural network that can compute the threshold precisely. With the threshold that is adaptive to the changing system states, the PPO-based scheduler is able to utilize the idle resources and maximize the execution success rate of the tasks.
SOFTWARE DEVELOPMENT ARCHITECTURE FOR JOB-LEVEL ALGORITHMS
(2015-09-01)
Wei, Ting Han
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Liang, Chao-Chin
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Wu, I-Chen
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Chen, Lung-Pin
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資訊工程學系
Recently, Wu et al. introduced a general distributed computing approach, named Job-Level (JL) Computing. In JL computing, a search tree is maintained by a client process, while search tree nodes are evaluated, expanded, or generated by leveraging game-playing programs. These node operations are encapsulated as coarse-grain jobs, each requiring tens of seconds or more of running the programs. This article presents an abstraction of the JL computing approach and develops a general JL search framework so that common modules may be reused for various JL applications, making JL development easier. We describe in detail the implementation of the JL Proof-number Search (JL-PNS) and JL Upper Confidence Bound Search (JL-UCT) as case studies in the application of the JL search framework. In our case studies, only hundreds of lines of code are required for new JL applications, while the code for the JL framework consists of more than ten thousand lines of code. It demonstrates that this framework can be used to greatly reduce new JL application development and software maintenance efforts.