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Author: search.filters.author.Chang, Yue-ShanSubject: search.filters.subject.Rough Set Theory

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    VM instance selection for deadline constraint job on agent-based interconnected cloud
    (2018-10-01)
    Fan, Chih-Tien
    ;
    Chang, Yue-Shan
    ;
    Yuan, Shyan-Ming
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    資訊工程學系
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    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.
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    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.