Browsing by Author "Sheu, Ruey-Kai"

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A fault-tolerant ONVIF protocol extension for seamless surveillance video stream recording
(2018-01-01)
Lin, Chia-Feng
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Chiao, Hsin-Ta
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Sheu, Ruey-Kai
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Chang, Yue-Shan
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Yuan, Shyan-Ming
ONVIF (Open Network Video Interface Forum) is an importance industrial standard for the video surveillance field. There are over 8000 ONVIF-compliant IP cameras (i.e., ONVIF NVT) and network video recorders (i.e., ONVIF NVS) from various vendors listed on the ONVIF official website. However, because the current ONVIF specifications do not support any fault-tolerant functions to handle NVS failure, in this paper we propose a fault tolerant solution for ONVIF NVS and try to minimize the modifications to the current ONVIF specifications in the proposed design. Besides, we also propose an extension to the ONVIF NVT for preventing the loss of captured NVT video data during the failover procedure triggered by NVS failure. Finally, we evaluate the following four performance metrics of the proposed fault-tolerant ONVIF surveillance system: the maximal video local cache time supported by an NVT, the response time for accessing an ONVIF web service, the interrupt duration of video recording due to NVS failure, and the live video black out time observed by a video management system. Since the experimental results are all within the acceptable range for a real fault-tolerant video surveillance system in the real world, the experiences provided by this paper is a good reference for improving the fault-tolerant capabilities of the ONVIF video surveillance systems. (C) 2017 Elsevier B.V. All rights reserved.
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.
A file-deduplicated private cloud storage service with CDMI standard
(2016-02-01)
Liu, Xiao-Long
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Sheu, Ruey-Kai
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Yuan, Shyan-Ming
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Wang, Yu-Ning
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資訊工程學系
The emergence of cloud environments makes users convenient to synchronize files across platform and devices. However, the data security and privacy are still critical issues in public cloud environments. In this paper, a private cloud storage service with the potential for security and performance concerns is proposed. A data deduplication scheme is designed in the proposed private cloud storage system to reduce cost and increase the storage efficiency. Moreover, the Cloud Data Management Interface (CDMI) standard is implemented in the proposed system to increase the interoperability. The proposed service provides an easy way to for user to establish the system and access data across devices conveniently. The experiment results also show the superiority of the proposed interoperable private cloud storage service in terms of data transmission and storage efficiency. By comparing with the existing system Gluster Swift, the proposed system is demonstrated much suitable for the service environment where most of the transmitted data are small files. (C) 2015 Elsevier B.V. All rights reserved.
A relaxable service selection algorithm for QoS-based web service composition
(2011-12-01)
Lin, Chia-Feng
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Sheu, Ruey-Kai
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Chang, Yue-Shan
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Yuan, Shyan-Ming
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資訊工程學系
Context: Web services are emerging technologies that enable application to application communication and reuse of autonomous services over Web. Composition of web services is a concept of integrating individual web services to conduct complex business transactions based on functionality and performance constraints Objective: To satisfy user requirements, technologies of Quality of service (QoS)-based web service composition (QWSC) are widely used to build complex applications by discovering the best-fit web services in term of QoS. Method: In this paper, a QoS-based service selection (RQSS) algorithm is proposed to help composite web application development by discovering feasible web services based on functionalities and QoS criteria of user requirements. The RQSS recommends prospective service candidates to users by relaxing QoS constraints if no suitable or available web service could exactly fulfill user requirements. Results: A generic framework is implemented to demonstrate the feasibility and performance of RQSS by adapting WS-BPEL standards, and can be reused for QoS-based web composition applications. Conclusion: The experimental results show that the RQSS algorithm indeed performs well and increases the system availability and reliability. (C) 2011 Elsevier B.V. All rights reserved.
Adaptive Block Partition of Geographic Map Based on Road Coverage in Urban Environment
(2015-01-01)
Wu, Guang-Ting
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Sheu, Ruey-Kai
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Fan, Chih-Tien
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Chang, Yue-Shan
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Tai, Chih-Hua
Smart cities try to deploy new sensors or controller to existing hardware infrastructure to improve the efficiency of city life. How to effectively utilize geographic information to deploy ubiquitous facilities (such as street light control unit) in the urban environment increasingly becomes an important issue. Calculating the minimum block number for facility deployment in a city is similar to the maximum clique problem which belongs to the NP-hard problems. In this paper, we propose an algorithm to find the near minimum block number of geographic map based on road coverage in the urban environment. In the algorithm, we first exploit the Google map as the geographic information to calculate the road coverage of a city and then partitioning the map into numerous partitions according to a given lights (road coverage). We also conduct some experiments to evaluate the performance and accuracy to demonstrate the feasibility of the system. The result shows that the proposed algorithm is effective and efficient.
An agent-based workflow scheduling mechanism with deadline constraint on hybrid cloud environment
(2018-01-10)
Chang, Yue-Shan
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Fan, Chih-Tien
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Sheu, Ruey-Kai
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Jhu, Syuan-Ru
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Yuan, Shyan-Ming
With the advances of cloud computing, business and scientific-oriented jobs with certain workflows are increasingly migrated to and run on a variety of cloud environments. These jobs are often with the property of deadline constraint and have to be completed within limited time. Therefore, to schedule a job with workflow (short for workflow) with deadline constraint is increasingly becoming a crucial research issue. In this paper, we, based on previous work, propose an agent-based workflow scheduling mechanism to schedule workflows that are with deadline constraint into federated cloud environment. Design and MethodsWe add a workflow agent into the original framework to schedule the deadline-constraint workflow. The workflow agent can smoothly schedule workflows to the cloud system according to their required resource and automatically monitor their execution. In order to accurately predict the execution time of each task to meet deadline constraint on certain VM with given resource, we inherit the use of rough set theory to estimate execution time of task in our previous work. Result and DiscussionA heuristic algorithm that is embedded into the workflow agent is also proposed because the problem had been shown to be NP-complete. The mechanism also adopts dynamic job dispatching method to reduce the usage of VM and to improve the resource utilization. We also conducted experiments to evaluate the efficiency and effectiveness. ConclusionThe experimental results show that the prediction time is very close to the real execution time and can efficiently schedule multiple scientific workflows to meet the deadline constraints simultaneously.
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
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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.
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.
IDS-DLA: Sheet Metal Part Identification System for Process Automation Using Deep Learning Algorithms
(2020-01-01)
Sheu, Ruey-Kai
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Lin, Yuan-Cheng
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Huang, Chin-Yin
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Chen, Lun-Chi
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Pardeshi, Mayuresh Sunil
Sheet metal-based manufacturing industries operate on several varieties of sheet metal parts. Previously QR codes stickers were put on sheet metal parts for identification by manual workers as per their respective shape and size features, thus ensuring synchronized raw material flow for the manufacturing process. However, identifying a particular type of sheet metal part based on its different features is still a challenge for a trained manual operator. Currently, in the market, there exist some automation solutions for solving such kind of problem but are incapable of achieving better performance and possess accuracy issues. So our goal is to provide process automation by overcoming manual work-based dependency and limitations. Therefore, a system is required that can take input a high definition camera captured sheet metal part image and provide an accurately identified type as output by utilizing a deep learning classification model in computer vision. The automation of sheet metal part identification by using ERP, CAD files and scheduling among them would make a smooth workflow by IDS-DLA. This paper aims to solve the identification problem by using the design and implementation of a sheet metal part identification, given as sheet metal part IDentification System for process automation using Deep Learning Algorithms (IDS-DLA). Considering the sheet metal parts there exists a large volume of types but fewer quantities. IDS-DLA performs high accuracy sheet metal part identification from the CAD model database by using the Geometric and CNN triplet filter. The IDS-DLA also evaluates the Hu moment ranking to choose the top 5 rank predictions as final ranking results. The applications can be given as manufacturing process automation for industry, 2D CAD search, 2D measuring solution, closet formation, etc. Ultimately, from the experiments, it can be observed that better accuracy is obtained as compared with the previous benchmarks. This multi-filtering approach using a deep feature extraction algorithm concludes to be the better approach and achieves higher performance.
Scaling database performance on GPUs
(2012-09-01)
Chang, Yue-Shan
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Sheu, Ruey-Kai
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Yuan, Shyan-Ming
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Hsu, Jyn-Jie
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交大名義發表
"The market leaders of Cloud Computing try to leverage the parallel-processing capability of GPUs to provide more economic services than traditions. As the cornerstone of enterprise applications, database systems are of the highest priority to be improved for the performance and design complexity reduction. It is the purpose of this paper to design an in-memory database, called CUDADB, to scale up the performance of the database system on GPU with CUDA. The details of implementation and algorithms are presented, and the experiences of GPU-enabled CUDA database operations are also shared in this paper. For performance evaluation purposes, SQLite is used as the comparison target. From the experimental results, CUDADB performs better than SQLite for most test cases. And, surprisingly, the CUDADB performance is independent from the number of data records in a query result set. The CUDADB performance is a static proportion of the total number of data records in the target table. Finally, this paper comes out a concept of turning point that represents the difference ratio between CUDADB and SQLite."
STAM-CCF: Suspicious Tracking Across Multiple Camera Based on Correlation Filters
(2019-07-01)
Sheu, Ruey-Kai
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Pardeshi, Mayuresh
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Chen, Lun-Chi
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Yuan, Shyan-Ming
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交大名義發表
There is strong demand for real-time suspicious tracking across multiple cameras in intelligent video surveillance for public areas, such as universities, airports and factories. Most criminal events show that the nature of suspicious behavior are carried out by un-known people who try to hide themselves as much as possible. Previous learning-based studies collected a large volume data set to train a learning model to detect humans across multiple cameras but failed to recognize newcomers. There are also several feature-based studies aimed to identify humans within-camera tracking. It would be very difficult for those methods to get necessary feature information in multi-camera scenarios and scenes. It is the purpose of this study to design and implement a suspicious tracking mechanism across multiple cameras based on correlation filters, called suspicious tracking across multiple cameras based on correlation filters (STAM-CCF). By leveraging the geographical information of cameras and YOLO object detection framework, STAM-CCF adjusts human identification and prevents errors caused by information loss in case of object occlusion and overlapping for within-camera tracking cases. STAM-CCF also introduces a camera correlation model and a two-stage gait recognition strategy to deal with problems of re-identification across multiple cameras. Experimental results show that the proposed method performs well with highly acceptable accuracy. The evidences also show that the proposed STAM-CCF method can continuously recognize suspicious behavior within-camera tracking and re-identify it successfully across multiple cameras.