• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.83-88
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• 2021

Cloud services have become the core infrastructure of the digital economy as a basis for collecting, storing, and processing large amounts of data to trigger artificial intelligence-based services and industrial innovation. Recently, cloud services have been spotlighted as a means of responding to corporate crises and changes in the work environment in a national disaster caused by COVID-19. While the cloud is attracting attention, the speed of adoption and diffusion of cloud services is not being actively carried out due to the lack of trust among users and uncertainty about security, performance, and cost. This study compares and analyzes the "Cloud Service Quality and Performance Management System" and the "Cloud Service Certification System" and suggests complementary points and improvement measures for the cloud service quality and performance management system.

• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.121-125
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• 2021

Cloud service has the characteristic that it must be always available and that it must be able to respond immediately to user requests. This study suggests a method for constructing a proactive and autonomous quality and performance management system to meet these characteristics of cloud services. To this end, we identify quantitative measurement factors for cloud service quality and performance management, define a structure for applying a time series framework to cloud service application quality and performance management for proactive management, and then use big data and artificial intelligence for autonomous management. The flow of data processing and the configuration and flow of big data and artificial intelligence platforms were defined to combine intelligent technologies. In addition, the effectiveness was confirmed by applying it to the cloud service quality and performance management system through a case study. Using the methodology presented in this study, it is possible to improve the service management system that has been managed artificially and retrospectively through various convergence. However, since it requires the collection, processing, and processing of various types of data, it also has limitations in that data standardization must be prioritized in each technology and industry.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.173-186
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• 2018

While cloud services are expanding, many users are having difficulty in adopting cloud services. This is because there is no information as to which cloud services can be trusted by users. loud service level agreement (Cloud SLA) is an agreement between cloud service providers and cloud service consumers using qualitative and quantitative indicators including quality and performance, etc. of cloud services. In this study, we propose a framework for cloud SLA that can be applied to the domestic cloud industry to improve service levels for cloud service providers and to protect users and also derive the detailed components of cloud SLA applicable to the domestic cloud industry using the proposed framework. Through this result, it is expected that the government will utilize the policy to enhance the reliability between cloud service providers and users under "the Act on the Development of Cloud Computing and Protection of Users", and eventually to activate cloud services by improving the quality and performance level of domestic cloud services and building a user trust.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2764-2783
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• 2012

Network virtualization supports future Internet environments and cloud computing. Virtualization can mitigate many hardware restrictions and provide variable network topologies to support variable cloud services. Owing to several advantages such as low cost, high flexibility, and better manageability, virtualization has been widely adopted for use in network virtualization platforms. Among the many issues related to cloud computing, to achieve a suitable cloud service quality we specifically focus on network and performance isolation schemes, which ensure the integrity and QoS of each virtual cloud network. In this study, we suggest a virtual network platform that uses Xen-based virtualization, and implement multiple virtualized networks to provide variable cloud services on a physical network. In addition, we describe the isolation of virtual networks by assigning a different virtualized network ID (VLAN ID) to each network to ensure the integrity of the service contents. We also provide a method for efficiently isolating the performance of each virtual network in terms of network bandwidth. Our performance isolation method supports multiple virtual networks with different levels of service quality.

• KIISE Transactions on Computing Practices
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• v.23 no.2
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• pp.85-96
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• 2017

IoT-Cloud service, integration of Internet of Things (IoT) and Cloud, is becoming a critical model for realizing creative and futuristic application services. Since IoT machines have little computing capacity, it is effective to attaching public Cloud resources for realizing IoT-Cloud service. Furthermore, utilizing containers and adopting a microservice architecture for developing IoT-Cloud service are useful for effective realization. The quality of microservice based IoT-Cloud service is affected by service function chaining which inter-connects each functions. For example, an issue with some of the functions or a bottleneck of inter-connection can degrade the service quality. To ensure functionality of the entire service, various test procedures considering various service environments are required to improve the service continuously. Hence in this paper, we introduce experimental realization of continuous integration based on DevOps and employ application performance monitoring for Node.js based IoT-Cloud service. Then we discuss its effectiveness.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.276-284
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• 2022

In this paper, we implemented cloud rendering using WebRTC for high-quality AR and VR services. Cloud rendering is an applied technology of cloud computing. It efficiently handles the rendering of large volumes of 3D content. The conventional VR and AR service is a method of downloading 3D content. The download time is delayed as the 3D content capacity increases. Cloud rendering is a streaming method according to the user's point of view. Therefore, stable service is possible regardless of the 3D content capacity. In this paper, we implemented cloud rendering using WebRTC and analyzed its performance. We compared latency of 100MB, 300MB, and 500MB 3D AR content in 100Mbps and 300Mbps internet environments. As a result of the analysis, cloud rendering showed stable latency regardless of data volume. On the other hand, the conventional method showed an increase in latency as the data volume increased. The results of this paper quantitatively evaluate the stability of cloud rendering. This is expected to contribute to high-quality VR and AR services

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.12
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• pp.2294-2314
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• 2011

The key issue in providing fast and reliable access on cloud services is the effective management of resources in a cloud system. However, the high variation in cloud service access rates affects the system performance considerably when there are no default routines to handle this type of occurrence. Adaptive techniques are used in resource management to support robust systems and maintain well-balanced loads within the servers. This paper presents an adaptive resource management for cloud systems which supports the integration of intelligent methods to promote quality of service (QoS) in provisioning of cloud services. A technique of dynamically assigning cloud services to a group of cloud servers is proposed for the adaptive resource management. Initially, cloud services are collected based on the excess cloud services load and then these are deployed to the assigned cloud servers. The assignment function uses the proposed proportional ordering which efficiently assigns cloud services based on its resource consumption. The difference in resource consumption rate in all nodes is analyzed periodically which decides the execution of service assignment. Performance evaluation showed that the proposed dynamic service assignment (DSA) performed best in throughput performance compared to other resource allocation algorithms.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.133-137
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• 2022

In the smart industry where data plays an important role, cloud computing is being used in a complex and advanced way as a convergence technology because it has and fits well with its strengths. Accordingly, in order to utilize artificial intelligence rather than human beings for quality management of cloud computing services, a consistent standardization method of data collected from various nodes in various areas is required. Therefore, this study analyzed technologies and cases for incorporating artificial intelligence into specific services through previous studies, suggested a plan to use artificial intelligence to comprehensively standardize data in quality management of cloud computing services, and then verified it through case studies. It can also be applied to the artificial intelligence learning model that analyzes the risks arising from the data formalization method presented in this study and predicts the quality risks that are likely to occur. However, there is also a limitation that separate policy development for service quality management needs to be supplemented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2463-2482
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• 2016

Cloud services are often provisioned to their customers using user-friendly web browsers with flexible and rich plug-in environments. Delay is one of the fundamental performance metrics of these web-based services. Commonly-used network measurement tools usually only measure network delay and it may be difficult to infer the web-delay performance using only network layer measurement approaches. In this paper, we propose to evaluate the application layer delay in a browser-based network measurement platform using engineered scripts. We conducted a delay measurement study using instrumented scripts in the proposed browser-based measurement platform. Our investigation included a comparison study of three browser-scripting delay measurement methods, including Java applet, JSP and Flash ActionScript. We developed a browser-based delay measurement testbed over the Internet so that different delay measurement tools could be evaluated in the same real network environment including typical Internet paths and the Baidu cloud. We also decomposed the components of the end-to-end delay process of the above measurements to reveal the difference and relationship between the network-layer delay and the application-layer delay. Our measurement results characterize the stochastic properties of the application-layer delay over real Internet paths, and how these properties vary from the underlying network layer delay. This browser-scripting measurement approach can be easily deployed on different cloud service platforms to inspect their application-layer delay performance between end clients and the cloud platforms. Our measurement results may provide insights into designing new cloud services with enhanced quality-of-experience perceived by cloud users.

• The Journal of Information Systems
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• v.32 no.2
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• pp.153-176
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• 2023

Purpose As non-face-to-face work environments become common due to COVID-19, interest in online collaboration tools that can communicate smoothly without time and space limitations is continuously increasing. Most of the prior studies are about the introduction, use intention, and satisfaction of cloud computing-based collaboration tools, and studies on the effects of collaboration tools on work-life balance and quality of life are somewhat lacking. Therefore, in this study, the characteristics of cloud computing-based collaboration tools were derived, and the effect on job satisfaction during work and job stress outside of working hours was confirmed. Design/methodology/approach This study applied the S-O-R framework and conducted an online survey of office workers who used cloud computing-based collaboration tools for more than three months. Hypotheses were tested using structural equations. Findings As a result of the analysis, among the characteristics of collaboration tools, stability, usefulness, and interoperability had higher job satisfaction as more stimuli were applied. In addition, the higher the job satisfaction during work, the higher the job performance, work-life balance, and quality of life.