Browsing by Author "Chang, Yue-Shan"

Now showing 1 - 14 of 14

A fault-tolerant ONVIF protocol extension for seamless surveillance video stream recording
(2018-01-01)
Lin, Chia-Feng
;
Chiao, Hsin-Ta
;
Sheu, Ruey-Kai
;
Chang, Yue-Shan
;
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
;
Lin, Chia-Feng
;
Chang, Yue-Shan
;
Sheu, Ruey-Kai
;
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 Mental Disorder Early Warning Approach by Observing Depression Symptom in Social Diary
(2014)
Fang, Ying-En
;
Tai, Chih-Hua
;
Chang, Yue-Shan
;
Fan, Chih-Tien
;
資訊工程學系
With the advances of information technology, there are increasing researches aiming at assisting depression diagnosis and treatment. In most of them the user is necessarily actively joining the diagnosis and treatment program while he has perceived mental disorder himself. In order to early prevent the mental disorder, in this paper we propose an early warning mechanism that observes and mines user diary published on social network platform, and generates a score of getting mental disordered or depressed. If the score is large than a threshold, the system can notify the user and his friends on the social network to take care about the friend. We have conducted experiments to evaluate the proposed approach, and the results show that the proposed approach is effective.
A parallel Bees Algorithm implementation on GPU
(2014-03-01)
Luo, Guo-Heng
;
Huang, Sheng-Kai
;
Chang, Yue-Shan
;
Yuan, Shyan-Ming
;
資訊科學與工程研究所
Bees Algorithm is a population-based method that is a computational bound algorithm whose inspired by the natural behavior of honey bees to finds a near-optimal solution for the search problem. Recently, many parallel swarm based algorithms have been developed for running on GPU (Graphic Processing Unit). Since nowadays developing a parallel Bee Algorithm running on the GPU becomes very important. In this paper, we extend the Bees Algorithm (CUBA (i.e. CUDA based Bees Algorithm)) in order to be run on the CUDA (Compute Unified Device Architecture). CUBA (CUDA based Bees Algorithm). We evaluate the performance of CUBA by conducting some experiments based on numerous famous optimization problems. Results show that CUBA significantly outperforms standard Bees Algorithm in numerous different optimization problems. (C) 2013 Elsevier B.V. All rights reserved.
A relaxable service selection algorithm for QoS-based web service composition
(2011-12-01)
Lin, Chia-Feng
;
Sheu, Ruey-Kai
;
Chang, Yue-Shan
;
Yuan, Shyan-Ming
;
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
;
Sheu, Ruey-Kai
;
Fan, Chih-Tien
;
Chang, Yue-Shan
;
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
;
Fan, Chih-Tien
;
Sheu, Ruey-Kai
;
Jhu, Syuan-Ru
;
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.
CUDT: A CUDA Based Decision Tree Algorithm
(2014-01-01)
Lo, Win-Tsung
;
Chang, Yue-Shan
;
Sheu, Ruey-Kai
;
Chiu, Chun-Chieh
;
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.
Execution Time Prediction Using Rough Set Theory in Hybrid Cloud
(2012)
Fan, Chih-Tien
;
Chang, Yue-Shan
;
Wang, Wei-Jen
;
Yuan, Shyan-Ming
;
資訊工程學系
Execution time prediction is an important issue in cloud computing. Predicting the execution time fast and accurately not only can help users to schedule jobs smarter, but also maximize the throughput and minimize the resource consumption of cloud platform. While hybrid cloud provides methods to federate multiple cloud platforms, different cloud platforms have different resource attributes, which will increase the difficulties to predict a job's execution time. In this paper, we exploit Rough Set Theory (RST), which is a well-known prediction technique that uses the historical data, to predict the execution time of jobs. The evaluation presents that RST can utilize the accuracy of the execution time, while the decision can be made in a short period of time.
Implementing a Workflow Agent on Federated Cloud
(2015)
Jhu, Syuan-Ru
;
Fan, Chih-Tien
;
Yuan, Shyan-Ming
;
Chang, Yue-Shan
;
資訊工程學系
Workflow scheduling is the way to schedule the complex scientific workflow. In a cloud platform, the resources can be utilized more effectively and efficiently by scheduling the workflow properly. In this paper, an agent-based workflow scheduling mechanism on cloud environment is proposed. We implement a set of agents that can federate multiple clouds and automatically monitor the entire system, and manage the job if it\'s a workflow job. We also implement a dynamic job dispatching algorithm to reduce the VM usage amount and improve the resource utilization.
Mobile cloud-based depression diagnosis using an ontology and a Bayesian network
(2015-02-01)
Chang, Yue-Shan
;
Fan, Chih-Tien
;
Lo, Win-Tsung
;
Hung, Wan-Chun
;
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.
Scaling database performance on GPUs
(2012-09-01)
Chang, Yue-Shan
;
Sheu, Ruey-Kai
;
Yuan, Shyan-Ming
;
Hsu, Jyn-Jie
;
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."
UCS - A Unified Cloud Storage Integration Service
(2017-01-01)
Chang, Che-Pin
;
Chiao, Hsin-Ta
;
Chang, Yue-Shan
;
Tsai, Ching-Tsorng
;
Yuen, Kuo-Kai
Although cloud storage integration services have been developed in recent years that providing a single interface to manage multiple cloud storage accounts across different providers, these cloud storage integration services still remain several issues that do not address. First, because these services do not support arrangement mechanisms for users, they need to keep track of which account they want to upload files to and arrange their storage spaces manually. Second, since each account is divided into different sections, it is impossible to provide a virtual folder that contains files from different cloud storage providers. In addition, when all accounts are nearly full, integration services could not chunk a file to store it across multiple accounts. Third, uploading and moving files across accounts are always done through the integration service's server, which might become a bottleneck when there are a lot of users uploading files at the same time. In this paper, we propose a new cloud storage integration service - Unified Cloud Storage (UCS) for solving the above-mentioned issues, and evaluate its performance by experiments.
VM instance selection for deadline constraint job on agent-based interconnected cloud
(2018-10-01)
Fan, Chih-Tien
;
Chang, Yue-Shan
;
Yuan, Shyan-Ming
;
資訊工程學系
;
Department of Computer Science
In recent years, users have been buying computing resources, such as VM instances, from the cloud resource providers. However, every cloud has its pricing models, solutions, and interfaces. Without a united interface, it is hard to manage across different clouds. To make things worse, it is even hard to select proper VM instances among multiple cloud resource providers when the resources are not sufficient. This paper proposes an interconnected cloud of selecting VM instances based on the job's deadline constraint. To make the system function, five agents are created for different purposes: a System Monitoring Agent, a Job Dispatching Agent, an Instance Group Managing Agent, an Instance Group Administrating Agent, and a Job Executing Agent. This paper also presents two decision algorithms, the Job Dispatching Algorithm and the Instance Group Invocation Algorithm, based on the Rough Set Theory, the jobs' deadline constraint, and the computing resource of the VM instances. The proposed system was deployed and evaluated on the real machines. The result shows not only the VM instances on different machines are interconnected, but also the algorithms successfully help manage the interconnected cloud's computing resources according to the submitted jobs with deadline constraints. (C) 2018 Elsevier B.V. All rights reserved.