• ETRI Journal
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• v.35 no.2
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• pp.348-351
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• 2013

Cloud computing has recently become a significant technology trend in the IT field. Among the related technologies, desktop virtualization has been applied to various commercial applications since it provides many advantages, such as lower maintenance and operation costs and higher utilization. However, the existing solutions offer a very limited performance for 3D graphics applications. Therefore, we propose a novel method in which rendering commands are not executed at the host server but rather are delivered to the client through the network and are executed by the client's graphics device. This method prominently reduces server overhead and makes it possible to provide a stable service at low cost. The results of various experiments prove that the proposed method outperforms all existing solutions.

• Electronics and Telecommunications Trends
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• v.34 no.2
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• pp.51-59
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• 2019

Edge computing, which is computing on the edge of the network, is becoming a market value as a means of overcoming the fear of communication disconnection and delay reduction, which are the technical weaknesses of cloud computing. Edge computing is continuously expanding applications in various applications such as safety industry, smart factories, autonomous vehicles, mobile communications, and AR/VR. Looking at edge computing trends from Microsoft, IBM, HPE, and Dell EMC, current edge computing must be understood as an integral binding technology and not as a simple complement to the cloud. This paper examines market trends in edge computing and analyzes the impact of edge computing on major related industries.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.89-100
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• 2022

With the advancement in the Internet of things Technology (IoT) cloud computing, billions of physical devices have been interconnected for sharing and collecting data in different applications. Despite many advancements, some latency - specific application in the real world is not feasible due to existing constraints of IoT devices and distance between cloud and IoT devices. In order to address issues of latency sensitive applications, fog computing has been developed that involves the availability of computing and storage resources at the edge of the network near the IoT devices. However, fog computing suffers from many limitations such as heterogeneity, storage capabilities, processing capability, memory limitations etc. Therefore, it requires an adequate task scheduling method for utilizing computing resources optimally at the fog layer. This work presents a comprehensive review of different task scheduling methods in fog computing. It analyses different task scheduling methods developed for a fog computing environment in multiple dimensions and compares them to highlight the advantages and disadvantages of methods. Finally, it presents promising research directions for fellow researchers in the fog computing environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.873-879
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• 2012

Recently the cloud computing paradigm has been widely accepting in various applications. Data management system of cloud computing requires ability to manage tremendous data and supporting scalability. The former can be achieved by weaken consistency and limitation of transactions, and the latter needs expand or shrink of components. In this paper, we propose a transaction processing model and a scalable module management algorithm when transaction is executed in the cloud. Transaction processing model consists of a transaction management module and a data management module. Scalable module management algorithm has no redistribution of components and may alleviates loads of existed modules. With simulation results, we can see the improvement of response time and decrease abort ratio of transactions.

• International Journal of Contents
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• v.10 no.1
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• pp.62-67
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• 2014

Usually, cloud storage systems are developed based on DFS (Distributed File System) for scalability and reliability reasons. DFSs are designed to improve throughput than IO response time, and therefore, they are appropriate for batch processing jobs. Recently, cloud storage systems have been used for update intensive applications such as OLTP and so on. However, in DFSs, in-place update operations are not carefully considered. Therefore, when updates are frequent, I/O performance of DFSs are degraded significantly. DFSs with RAID techniques have been proposed to improve their performance and reliability. Their performance degradation caused by frequent update operations can be more significant. In this paper, we propose an in-place update method for DFS RAID exploiting a differential logging technique. The proposed method reduces the I/O costs, network traffic and XOR operation costs for RAID. We demonstrate the efficiency of our proposed in-place update method through various experiments.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1480-1493
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• 2018

Recently, computational intelligence has received a lot of attention from researchers due to its potential applications to artificial intelligence. In computer science, computational intelligence refers to a machine's ability to learn how to compete various tasks, such as making observations or carrying out experiments. We adopted a computational intelligence solution to monitoring residual resources in cloud computing environments. The proposed residual resource monitoring scheme periodically monitors the cloud-based host machines, so that the post migration performance of a virtual machine is as consistent with the pre-migration performance as possible. To this end, we use a novel similarity measure to find the best target host to migrate a virtual machine to. The design of the proposed residual resource monitoring scheme helps maintain the quality of service and service level agreement during the migration. We carried out a number of experimental evaluations to demonstrate the effectiveness of the proposed residual resource monitoring scheme. Our results show that the proposed scheme intelligently measures the similarities between virtual machines in cloud computing environments without causing performance degradation, whilst preserving the quality of service and service level agreement.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.471-479
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• 2024

Recent advancements in cloud computing have significantly increased its importance across various sectors. As sensors, devices, and customer demands have become more diverse, workloads have become increasingly variable and difficult to predict. Cloud providers, connected to multiple physical servers to support a range of applications, often over-provision resources to handle peak workloads. This approach results in inconsistent services, imbalanced energy usage, waste, and potential violations of service level agreements. In this paper, we propose a novel engine equipped with a scheduler based on the Hot-Threshold concept, aimed at optimizing resource usage and improving energy efficiency in cloud environments. We developed this engine to employ both proactive and reactive methods. The proactive method leverages workload estimate-based provisioning, while the reactive Hot-Cold Scheduler consists of a Predictor, Solver, and Processor, which together suggest an intelligent migration flow. We demonstrate that our approach effectively addresses existing challenges in terms of cost and energy consumption. By intelligently managing resources based on past user statistics, we provide significant improvements in both energy efficiency and service consistency.

• KIEE International Transactions on Electrophysics and Applications
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• v.11C no.4
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• pp.120-126
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• 2001

For the past five years, Inha University has been observing the electric fields produced by cloud-to-ground return strokes. This paper presents the summary of most recent results. Statistics on the zero-to-peak rise time, the zero-to-zero crossing time and the amplitude ratio of the second peak in the opposite polarity to the first peak were examined. The radiation electric fields produced by distant cloud-to-ground return strokes were substantially same pattern. The first return stroke field starts with a slowly increasing front and rises abruptly to peak. The rising portions of the electric fields produced by cloud-to-ground return strokes last 1 $mutextrm{s}$ to a few $mutextrm{s}$. The mean values of the zero-to-peak rise times of electric fields were 5.72 $mutextrm{s}$ and 4.12 $mutextrm{s}$ for the positive and the negative cloud-to-ground return strokes, respectively. The mean of the zero-to-zero crossing time for the positive return strokes was 29.48 $mutextrm{s}$ compared with 38.54 $mutextrm{s}$ for the negative return strokes. The depths of the dip after the peak of return stroke electric fields also have the dependence on the polarity of cloud-to-ground return stroke, and the mean values for the positive and negative cloud-to-ground return strokes were 33.55 and 28.19%, respectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.4
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• pp.141-152
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• 2013

The applications and concerned technologies of cloud computing services, one of major trends in the information communication technology, are widely progressing and advancing. OpenStack, one of open source cloud computing platforms, is comprised of several service components; using these, it can be possible to build public or private cloud computing service for a given target application. In this study, a remote sensing image analysis processing service on cloud computing environment has designed and implemented as an operational test application in the private cloud computing environment based on OpenStack. The implemented service is divided into instance server, web service, and mobile app. A instance server provides remote sensing image processing and database functions, and the web service works for storage and management of remote sensing image from user sides. The mobile app provides functions for remote sensing images visualization and some requests.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.30 no.11
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• pp.32-37
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• 2012