• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.292-298
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• 2019

In this paper, we show how to provide high quality image service using cloud server and image quality enhancement algorithm. In other words, based on the concept of ViVa (Video Value Addition) proposed in the paper, we propose an improved system compared to the existing streaming service by providing a high-quality video with the transmission bit rate and calculation amount necessary to serve low-quality images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4886-4907
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• 2019

The use of mobile devices is increasing in multimedia applications. The multimedia contents are delivered to mobile users over heterogeneous networks. Due to fluctuation in bandwidth and user mobility, the service providers are facing difficulties in providing Quality of Service (QoS) guaranteed delivery for multimedia applications. Multimedia applications depend on QoS parameters such as delay, bandwidth, and jitter to offer better user experience. The existing schemes use the single source and multisource delivery but are unable to balance between stream quality and network congestion for mobile users. We proposed a Quality Oriented Multimedia Content Delivery Scheme (QOMCDS) for the mobile cloud to deliver better quality multimedia contents for the mobile user. The multimedia contents are delivered to the mobile device based on the device's parameters and network environment. The objective video quality assessment models like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Video Quality Measurement (VQM) are used to measure the quality of the video. The client side Quality of Experience metric such as Startup delay, Rebuffering events, and Bitrate switch count was used for evaluation. The proposed scheme is evaluated using dash.js and is compared to existing schemes. The results show significant improvement over existing multimedia content delivery schemes.

• Journal of Ocean Engineering and Technology
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• v.32 no.3
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• pp.158-165
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• 2018

Generally, container ships contain cargo holds with cell guides that serve to increase the container loading and unloading efficiency, minimize the space loss, and fix containers during the voyage. This paper describes a new quality management system for the cell guides of container ships (the so-called Trim Cell Guide system). The main functions of this system are the trimming of the point cloud obtained using a 3D scanner and an inspection simulation for cell guide quality. In other words, the raw point cloud of cell guides after construction is measured using a 3D scanner. Here, the raw point cloud contains a lot of noise and unnecessary information. Using the GUI interface supported by the system, the raw point cloud can be trimmed. The trimmed point cloud is used in a simulation for cell guide quality inspection. The RANSAC (Random Sample Consensus) algorithm is used for the transverse section representation of a cell guide at a certain height and applied for the calculation of the intervals between the cell guides and container. When the container hits the cell guides during the inspection simulation, the container is rotated horizontally and checked again for a possible collision. It focuses on a system that can be simulated with the same inspection process as in a shipyard. For a practicality review, we compared the precision data gained from an inspection simulation with the measured data. As a result, it was confirmed that these values were within approximately ${\pm}2mm$.

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.2
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• pp.73-91
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• 2016

This study sheds light on the quality aspect of cloud computing services as next IT platform. Three tasks of the research are to extract the quality factors of cloud service from the user's viewpoint, empirically analyze the perceptual differences between the user group and the provider group by applying the IPA technique, and suggest some quality factors that need to be improved. Based on the previous researches and focus group evaluation, 13 quality factors have been established. Two field surveys have been performed respectively to collect the perceptual importance and satisfaction level of the users and the providers. It is shown that the quality satisfaction of the user group is lower than the quality perceived by the providers. And there exist significant differences between two groups in respect to quality importance level and IPA matrix. In conclusion, 6 quality factors that need to be improved are suggested such as service functionality, service availability, interoperability, scalability, confidentiality, and provider's responsiveness.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1192-1200
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• 2019

Recently, artificial intelligence techniques have been widely used in the computer science field, such as the Internet of Things, big data, cloud computing, and mobile computing. In particular, resource management is of utmost importance for maintaining the quality of services, service-level agreements, and the availability of the system. In this paper, we review and analyze various ways to meet the requirements of cloud resource management based on artificial intelligence. We divide cloud resource management techniques based on artificial intelligence into three categories: fog computing systems, edge-cloud systems, and intelligent cloud computing systems. The aim of the paper is to propose an intelligent resource management scheme that manages mobile resources by monitoring devices' statuses and predicting their future stability based on one of the artificial intelligence techniques. We explore how our proposed resource management scheme can be extended to various cloud-based systems.

• 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.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.59-66
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• 2024

This paper presents the concept and characteristics of cloud computing, and it addresses how cloud computing delivers quality of service (QoS) to the end-user. Next, it discusses how to schedule one's workload in the infrastructure using technologies that have recently emerged such as Machine Learning (ML). That is followed by an overview of how ML can be used for resource management. This paper then looks at the primary goal of this project, which is to outline the benefits of using ML to schedule upcoming demands to achieve QoS and conserve energy. In this survey, we reviewed the research related to ML methods for predicting workloads in cloud computing. It also provides information on the approaches to elasticity, while another section discusses the methods of prediction used in previous studies and those that used in this field. The paper concludes with a summary of the literature on predicting workloads and optimising QoS in the cloud computing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5357-5381
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• 2018

Cloud computing offers a wide range of on-demand resources over the internet. Utility-based resource allocation in cloud data centers significantly increases the number of cloud users. Heavy usage of cloud data center encounters many problems such as sacrificing system performance, increasing operational cost and high-energy consumption. Therefore, the result of the system damages the environment extremely due to heavy carbon (CO2) emission. However, dynamic allocation of energy-efficient resources in cloud data centers overcomes these problems. In this paper, we have proposed Energy and Service Level Agreement (SLA) Aware Resource Allocation Heuristic Algorithms. These algorithms are essential for reducing power consumption and SLA violation without diminishing the performance and Quality-of-Service (QoS) in cloud data centers. Our proposed model is organized as follows: a) SLA violation detection model is used to prevent Virtual Machines (VMs) from overloaded and underloaded host usage; b) for reducing power consumption of VMs, we have introduced Enhanced minPower and maxUtilization (EMPMU) VM migration policy; and c) efficient utilization of cloud resources and VM placement are achieved using SLA-aware Modified Best Fit Decreasing (MBFD) algorithm. We have validated our test results using CloudSim toolkit 3.0.3. Finally, experimental results have shown better resource utilization, reduced energy consumption and SLA violation in heterogeneous dynamic cloud environment.