• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.85-91
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• 2014

Cloud storage system uses a distributed file system for storing and managing data. Traditional distributed file system makes a triplication of data in order to restore data loss in disk failure. However, enforcing data replication method increases storage utilization and causes extra I/O operations during replication process. In this paper, we propose a data replication method using erasure codes in cloud storage system to improve storage space efficiency and I/O performance. In particular, according to data access frequency, the proposed method can reduce the number of data replications but using erasure codes can keep the same data recovery performance. Experimental results show that proposed method improves performance in storage efficiency 40%, read throughput 11%, write throughput 10% better than HDFS does.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.204-226
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• 2018

Big data processing applications have been migrated into cloud gradually, due to the advantages of cloud computing. Hadoop Distributed File System (HDFS) is one of the fundamental support systems for big data processing on MapReduce-like frameworks, such as Hadoop and Spark. Since HDFS is not aware of the co-location of virtual machines in the cloud, the default scheme of block allocation in HDFS does not fit well in the cloud environments behaving in two aspects: data reliability loss and performance degradation. In this paper, we present a novel location-aware data block allocation strategy (LDBAS). LDBAS jointly optimizes data reliability and performance for upper-layer applications by allocating data blocks according to the locations and different processing capacities of virtual nodes in the cloud. We apply LDBAS to two stages of data allocation of HDFS in the cloud (the initial data allocation and data recovery), and design the corresponding algorithms. Finally, we implement LDBAS into an actual Hadoop cluster and evaluate the performance with the benchmark suite BigDataBench. The experimental results show that LDBAS can guarantee the designed data reliability while reducing the job execution time of the I/O-intensive applications in Hadoop by 8.9% on average and up to 11.2% compared with the original Hadoop in the cloud.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.89-99
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• 2017

Many works have attempted to solve the scalability, the availability, and the low-latency problems of peer-to-peer (P2P) live video streaming; yet, the problems still remain. While tree-based systems are vulnerable to churn, the mesh-based systems suffer from high delay and overhead. The DHT-aided chunk-driven overlay (DCO) [1] tried to tackle these problems by using the distributed hash table (DHT), which structures into a mesh-based overlay to efficiently share the video segment. However, DCO fully depends on the capacity of the users' device which is small and unstable, i.e., the users' device may leave and join the network anytime, and the video server's bandwidth can be insufficient when the number of users joining the network suddenly increases. Therefore, cloud assist is introduced to overcome those problems. Cloud assist can be used to enhance the availability, the low-latency, and the scalability of the system. In this paper, the DHT is used to maintain the location of the streaming segments in a distributed manner, and the cloud server is used to assist other peers when the bandwidth which required for sharing the video segment is insufficient. The simulation results show that by using the threshold and cloud assist, the availability and the low-latency of the video segments, and the scalability of the network are greatly improved.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.2
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• pp.199-208
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• 2017

IoT, Cloud, Bigdata, Mobile, AI, and 3D print, which are called as the main axis of the 4th Industrial Revolution, can be predicted to be changed when the technology is applied to the military. Especially, when I think about the purpose of battle, I think that IoT, Cloud, Bigdata, Mobile, and AI will play many role. Therefore, in this paper, Smart Military is defined as the future military that incorporates these five technologies, and the architecture is established and the appropriate information security model is studied. For this purpose, we studied the existing literature related to IoT, Cloud, Bigdata, Mobile, and AI and found common elements and presented the architecture accordingly. The proposed architecture is divided into strategic information security and tactical information security in the Smart Military environment. In the case of vulnerability, the information security is divided into strategic information security and tactical information security. If a protection system is established, it is expected that the optimum information protection can be constructed within an effective budget range.

• ETRI Journal
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• v.41 no.5
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• pp.684-695
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• 2019

In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high-dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non-Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C-SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.175-178
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• 2013

Mobile Virtual Desktop Infrastructures (mVDI) are gaining popularity in cloud computing by allowing mobile devices to execute their mobile applications in a cloud server instead of relying on physical mobile devices. Consolidating many users into mVDI environment can significantly lower IT management expenses and enables new features such as "available-anywhere" desktops. However, there are many barriers to broad adoption including the slow performance of virtualized I/O, CPU scheduling interference problems. In this paper, we will discuss about mVDI with the current issues, the corresponding solutions and challenges.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.21-29
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• 2020

The Topic Modeling research, the methodology for deduction keyword within literature, has become active with the explosion of data from digital society transition. The research objective is to investigate research trends in D.N.A.(Data, Network, Artificial Intelligence) field using DTM(Dynamic Topic Model). DTM model was applied to the 1,519 of research projects with SW·A.I technology classifications among ICT(Information and Communication Technology) field projects between 6 years(2015~2020). As a result, technology keyword for D.N.A. field; Big data, Cloud, Artificial Intelligence, extended keyword; Unstructured, Edge Computing, Learning, Recognition was appeared every year, and accordingly that the above technology is being researched inclusively from other projects can be inferred. Finally, it is expected that the result from this paper become useful for future policy·R&D planning and corporation's technology·marketing strategy.

• Current Research on Agriculture and Life Sciences
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• v.5
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• pp.134-142
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• 1987

The purpose of this study is to establish more rational and practical planning theory for amusementpark. It analyze and considerate the fluctuation of the people who come and use a Tong-Ch'on amusementpark. The results drawn from this research work are as follows ; There are considerable correlation between use fluctuation and some factors. The factors are season (spring, summer, autumn) as a time, temperature, cloud amount, duration of sunshine, weather (rainy day) as a climate and date (weekday, holiday) as a social system. The important variables are temperature, cloud amount, duration of sunshine and date (week day, holiday) to estimate the user of amusementpark. I can reduce the following two types of regression models. 1.${\log}_eY1=6.9114+0.1135TEM+0.00002_eSUM-0.4068WI+0.4316W3$ ($R^2=0.94$) 2. ${\log}_eY2=7.2069+0.1177TEM-0.0990CLO+0.488W3$ ($R^2=0.95$) Y ; Number of User TEM ; Temperature CLO ; Amount of cloud SUN ; Duration of Sunshine WI ; Weekday W3 ; Holiday Those model is inorder to estimate the user for management of Tong-Ch'on amusementpark and use on the computation of facility size for reconstruction. Besides the amusementpark, city park and outdoor recreation area could estimate of user throuth this method. But, I am not sure about the regression models because I didn't apply the regression models to the other amusementpark, city park or outdoor recreation area. Therefore, I think that this problem needs to study in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5179-5202
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• 2018

Transmission Control Protocol (TCP) is the most widely used protocol in the cloud data centers today. However, cloud data centers using TCP experience many issues as TCP was designed based on the assumption that it would primarily be used in Wide Area Networks (WANs). One of the major issues with TCP in the cloud data centers is the Incast issue. This issue arises because of the many-to-one communication pattern that commonly exists in the modern cloud data centers. In many-to-one communication pattern, multiple senders simultaneously send data to a single receiver. This causes packet loss at the switch buffer which results in TCP throughput collapse that leads to high Flow Completion Time (FCT). Recently, Software-Defined Networking (SDN) has been used by many researchers to mitigate the Incast issue. In this paper, a detailed survey of various SDN based solutions to the Incast issue is carried out. In this survey, various SDN based solutions are classified into four categories i.e. TCP Receive Window based solutions, Tuning TCP Parameters based solutions, Quick Recovery based solutions and Application Layer based solutions. All the solutions are critically evaluated in terms of their principles, advantages, and shortcomings. Another important feature of this survey is to compare various SDN based solutions with respect to different performance metrics e.g. maximum number of concurrent senders supported, calculation of delay at the controller etc. These performance metrics are important for deployment of any SDN based solution in modern cloud data centers. In addition, future research directions are also discussed in this survey that can be explored to design and develop better SDN based solutions to the Incast issue.

• Convergence Security Journal
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• v.23 no.5
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• pp.21-27
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• 2023

In the cloud computing environment, servers and applications can be set up within minutes, and recovery in case of fail ures has also become easier. Particularly, using virtual servers in the cloud is not only convenient but also cost-effective compared to the traditional approach of setting up physical servers just for temporary services. However, most of the und erlying networks and security systems that serve as the foundation for such servers and applications are primarily hardwa re-based, posing challenges when it comes to implementing cloud virtualization. Even within the cloud, there is a growing need for virtualization-based security and protection measures for elements like networks and security infrastructure. This paper discusses research on enhancing the security of cloud networks using network virtualization technology. I configured a secure network by leveraging virtualization technology, creating virtual servers and networks to provide various security benefits. Link virtualization and router virtualization were implemented to enhance security, utilizing the capabilities of virt ualization technology. The application of virtual firewall functionality to the configured network allowed for the isolation of the network. It is expected that based on these results, there will be a contribution towards overcoming security vulnerabil ities in the virtualized environment and proposing a management strategy for establishing a secure network.