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    Centralizing Data Warehouse Platform for Mining Management Using AWS Computing Instance
    (2024-01-01)
    Sathaporn, Posathip
    ;
    Chaowalittawin, Vasutorn
    ;
    Krungseanmuang, Woranidtha
    ;
    Benjangkaprasert, Chawalit
    ;
    Purahong, Boonchana
    Mining industry is one of significant industry in the world. In order to increase efficiency, safety, sustainability and environmental impacts in mining process. This article presents a centralized data platform for mining process by design based on microservices architecture which can be supported various of input data source such as manual form, excel file and internet of things (IoT) device and notification when the value is abnormal. Our platform implementation on Amazon Web Service (AWS) cloud. For development of productivity in process and controlling quality the mine environment. The results of the platform operations with various source of data acquisition with response time is less than 1.5 seconds per each request.
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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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    Satisfaction ofworking people in Thailand in their usage of cloud storage systems
    (2018-09-22)
    Chaiyasoonthorn, Wornchanok
    ;
    Najantong, Kulapa
    ;
    Chaveesuk, Singha
    The purpose of this research was to investigate Satisfaction of working people in Thailand in their usage of cloud storage systems. The theories that this study was based on are Technology Acceptance Model 3 or TAM3, Unified Theory of Acceptance and Use of Technology Model or UTAUT and Service satisfaction. The sample group consisted of 400 working people in the central region of Thailand. Five hypotheses were formulated and tested by multiple linear regression statistic. Briefly, it was found that all of the four independent variables featured in the hypotheses: Perceived Usefulness, Perceived Ease of Use, Social Influence and Experience influent were significant to user satisfaction in terms of equitable service and progressive service. Some of the above independent variables also influenced user satisfaction in terms of timely service, ample service, and continuous service. The independent factor that did not influence user satisfaction in terms of timely service was Social Influence while the factor that did not do so in terms of ample service and continuous service was Perceived Usefulness.
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    VDEP: VM Dependency Discovery in Multi-tier Cloud Applications
    (2015-08-19)
    Sangpetch, Akkarit
    ;
    Kim, Hyong S.
    The automatic discovery of dependencies in distributed Cloud applications is very useful for large scale deployments. Dependencies can be used to identify the anomalies due to errors, failures or the performance bottleneck in applications. Although existing dependency models can be useful, we believe more comprehensive dependency model would improve anomaly detection in large scale distributed applications. We propose a VM dependency discovery system and introduce dependency primitives that incorporate complex application behavior/interaction patterns. We also formulate response time characteristics for each dependency primitive. Using the component dependencies and traffic monitoring, we develop a stochastic model to estimate the response time probability distribution for components and overall application. We evaluate and validate our system with various production applications. Experiments show that we can accurately discover application dependencies and also predict not only the average response time but the 95th percentile response time within 8% of the actual response time.
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    A virtualization approach to auto-scaling problem
    (2011-01-01)
    Thepparat, Theera
    ;
    Harnprasarnkit, Amnart
    ;
    Thippayawong, Douanghatai
    ;
    Boonjing, Veera
    ;
    Chanvarasuth, Pisit
    The paper proposes to simulate feasibility of using virtualization technology to auto-scaling problem in cloud computing. It uses ARENA simulation software to build two different models. There are auto-scaling without server virtualization and auto-scaling with server virtualization. The results of our experiment show that employing virtualization technology increases both life time of servers and CPU utilization. © 2011 IEEE.