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Author: search.filters.author.Sithiyopasakul, SaranHas files: No

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    Implementation of Cloud Computing and Internet of Things (IoT) by Performance Evaluation
    (2024-01-01)
    Sithiyopasakul, Jiran
    ;
    Archevapanich, Tuanjai
    ;
    Sithiyopasakul, Saran
    ;
    Lasakul, Attasit
    ;
    Purahong, Boonchana
    The integration of cloud computing and the Internet of Things (IoT) holds transformative potential across diverse industries. Performance assessment is essential to gauge the quality and efficiency of cloud computing and IoT systems. This paper presents a comprehensive performance evaluation of cloud computing and IoT systems, focusing on three major platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Experimental results encompass various scenarios, including normal operation, heavy load conditions, IoT applications, and scalability testing. The outcomes reveal distinct performance metrics such as response time, throughput, latency, and reliability for each cloud platform.
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    Performance Evaluation of Infrastructure as a Service across Cloud Service Providers
    (2023-01-01)
    Sithiyopasakul, Saran
    ;
    Archevapanich, Tuanjai
    ;
    Purahong, Boonchana
    ;
    Sithiyopasakul, Paisan
    ;
    Lasakul, Attasit
    The purpose of this research aims to monitor, analyze, and compare the performance of infrastructure as a service (IaaS) between the selective cloud providers. To assure which cloud provider has more stability, reliability, and scalability. This paper focuses on performance testing based on a deployed web server in the cloud environment. The main feature of cloud computing is scalability thus most common IaaS cloud service providers (CSPs) have Auto Scaling features for instances or virtual machines. Not only does this paper gives the experimental results of the scaling scalability testing, but it also provides the results of recovery testing to inspect how long a web server is able to recover from failures and load testing which simulated traffic requests. Testing was conducted in the major public clouds of Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). Azure performs the most efficiently of almost all testing but hardest to configure.
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    Hybrid learning during the COVID-19 pandemic of engineering students at KMITL, Thailand
    (2021-01-01)
    Purahong, Boonchana
    ;
    Sithiyopasakul, Saran
    ;
    Sithiyopasakul, Paisan
    ;
    Anuwongpinit, Thanavit
    ;
    Archevapanich, Tuanjai
    This article presents the hybrid learning model during the COVID-19 for students of Agro-Industrial Systems Engineering who enrolled in the course embedded system, course code 01386304, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang for 29 students by selecting a specific sample group. The researcher presented a hybrid learning model during the COVID-19, where students were orientated for the same understanding and followed by teaching as active learning formed project-based learning. Finally, we assessed five aspects of hybrid learning satisfaction during the COVID-19. The evaluation results revealed that the students' satisfaction was at a good level and their arithmetic mean was 4.206 and the standard deviation is equal to 0.74 (X = 4.206, S.D. = 0.74)
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    Case study: To identify the students that participating in the cheer activity of the faculty of engineering, KMITL by using near field wireless communication
    (2020-11-04)
    Sithiyopasakul, Paisan
    ;
    Purahong, Boonchana
    ;
    Sithiyopasakul, Saran
    ;
    Archevapanich, Tuanjai
    This article presents wireless communication technology with NFC. In this case study, it is about using the NFC to identify the freshmen one by one that participating in the Cheer activity of the Faculty of Engineering, KMITL of the first-year students of 800 students, duration ten days. From the experiment results, when using the application, it can be used to check the names of students who have participated in the activity with 100 percent accuracy.