• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6B
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• pp.559-567
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• 2009

Distributed Content Delivering Network (DCDN) will make use of the existing resources of the common Internet users in terms of storage space, bandwidth and Internet connectivity to create it. However DCDN has some limitations that are inefficient using of storage space, reliability and having special load balancing (LB) algorithm. So, this paper proposes Re-distribution based CDN (RDCDN) that overcomes the limitations of DCDN. RDCDN has the content re-distribution algorithm and separates surrogates to main surrogate and sub surrogates. Main surrogate can help service reliability be improved by storing all contents as back-up system. And content re-distribution algorithm also can help storage space be saved because all contents are not stored in every surrogate. Especially, when RDCwDN uses content re-distribution algorithm, it can work active load balancing function without extra LB algorithm like as DCDN. Results of simulation show that the proposed architecture can improve reliability and efficiency of storage space, and it also can offer the same performance as that of commercial CDN and DCDN.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.2
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• pp.134-145
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• 2004

With rapid growth in the amount of data transferred on the Internet, traditional storage systems have reached the limits of their capacity and performance. SAN (Storage Area Network), which connects hosts to disk with the Fibre Channel switches, provides one of the powerful solutions to scale the data storage and servers. In this environment, the maintenance of data consistency among hosts is an important issue because multiple hosts share the files on disks attached to the SAN. To preserve data consistency, each host can execute the disk I/O whenever disk read and write operations are requested. However, frequent disk I/O requests cause the deterioration of the overall performance of a SAN cluster. In this paper, we introduce a SANtopia global buffer manager to improve the performance of a SAN cluster reducing the number of disk I/Os. We describe the design and algorithms of the SANtopia global buffer manager, which provides a buffer cache sharing mechanism among the hosts in the SAN cluster. Micro-benchmark results to measure the performance of block I/O operations show that the global buffer manager achieves speed-up by the factor of 1.8-12.8 compared with the existing method using disk I/O operations. Also, File system micro-benchmark results show that SANtopia file system with the global buffer manager improves performance by the factor of 1.06 in case of directories and 1.14 in case of files compared with the file system without a global buffer manager.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3B
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• pp.225-234
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• 2009

Recently IPTV service is penetrating to the ordinary home users very swiftly. One of the first phase of IPTV service is considered to be VoD, and a nationwide availability of the VoD service imposes tremendous pressure to the network resource due to its requirements for the broad bandwidth, the inherent nature of unicast technology, and the large scalability, etc. This work suggests a novel and practical method to the design of network resource for the VoD service. Especially, we explore the distributed content storage problem that takes into account the popularity of the video contents and its corresponding link dimensioning problem that takes into account the grade of service for the flow level quality of service about the VoD service. By assuming a realistic topology for the nationwide IP backbone network of Korea, which is a typical tree topology, we suggest an analytic method for the design of VoD service.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.421-424
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• 2003

This paper proposes the generalized predictive control(GPC) method of chaotic systems using a self-recurrent wavelet neural network(SRWNN). The reposed SRWNN, a modified model of a wavelet neural network(WNN), has the attractive ability such as dynamic attractor, information storage for later use. Unlike a WNN, since the SRWNN has the mother wavelet layer which is composed of self-feedback neurons, mother wavelet nodes of the SRWNN can store the past information of the network. Thus the SRWNN can be used as a good tool for predicting the dynamic property of nonlinear dynamic systems. In our method, the gradient-descent(GD) method is used to train the SRWNN structure. Finally, the effectiveness and feasibility of the SRWNN based GPC is demonstrated with applications to a chaotic system.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1019-1029
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• 2020

In this paper, we propose a novel knowledge distillation algorithm to create an compressed deep ensemble network coupled with the combined use of local and global features of face images. In order to transfer the capability of high-level recognition performances of the ensemble deep networks to a single deep network, the probability for class prediction, which is the softmax output of the ensemble network, is used as soft target for training a single deep network. By applying the knowledge distillation algorithm, the local feature informations obtained by training the deep ensemble network using facial subregions of the face image as input are transmitted to a single deep network to create a so-called compressed ensemble DCNN. The experimental results demonstrate that our proposed compressed ensemble deep network can maintain the recognition performance of the complex ensemble deep networks and is superior to the recognition performance of a single deep network. In addition, our proposed method can significantly reduce the storage(memory) space and execution time, compared to the conventional ensemble deep networks developed for face recognition.

• Journal of KIISE
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• v.43 no.8
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• pp.841-847
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• 2016

As network technology advances, a larger number of devices are connected through the Internet. Recently, cloud storage services are gaining popularity, as they are convenient to access anytime and anywhere. Among cloud storage services, object storage is the representative one due to their characteristics of low cost, high availability, and high durability. One limitation of object storage services is that they can access data on the cloud only through the HTTP-based RESTful APIs. In our work, we resolve this limitation with the in-memory file system which provides a POSIX interface to the file system users and communicates with cloud object storages with RESTful APIs. In particular, our flush mechanism is compatible with existing file systems, as it is based on the swap mechanism of the Linux kernel. Our in-memory file system backed by cloud storage reduces the performance overheads and shows a better performance than S3QL by 57% in write operations. It also shows a comparable performance to tmpfs in read operations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.324-327
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• 2013

Recently, due to evolving cloud computing technology, we can easily and transparently utilize both local computing resource and remote computing resource in real life. Especially, enhancing smart device technologies and network infrastructures promote an increase of needs to share files between local smart devices and cloud storages. However, since smart devices have a limited storage space, storing files on cloud storage causes a starvation problem of local storage. It means that users can face a storage-lack problem even a cloud storage service provide a huge file storing space. In this research, we propose a method to manage files between smart devices and cloud storages. Our approach calculate file usage pattern based on recently used date, and then this approach determines local files being migrated. As a result, our approach is sufficient for handling data synchronization between big data storage farm and local thin client which contains limited storage space.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.871-874
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• 2011

The fast emerging of network technology and the high demand of computing resources have prompted many organizations to outsource their storage and computing needs. Cloud based storage services such as Microsoft's Azure and Amazon's S3 allow customers to store and retrieve any amount of data, at anytime from anywhere via internet. The scalable and dynamic of the cloud storage services help their customer to reduce IT administration and maintenance costs. No doubt, cloud based storage services brought a lot of benefits to its customer by significantly reducing cost through optimization increased operating and economic efficiencies. However without appropriate security and privacy solution in place, it could become major issues to the organization. As data get produced, transferred and stored at off premise and multi tenant cloud based storage, it becomes vulnerable to unauthorized disclosure and unauthorized modification. An attacker able to change or modify data while data inflight or when data is stored on disk, so it is very important to secure data during its entire life-cycle. The traditional cryptography primitives for the purpose of data security protection cannot be directly adopted due to user's lose control of data under off premises cloud server. Secondly cloud based storage is not just a third party data warehouse, the data stored in cloud are frequently update by the users and lastly cloud computing is running in a simultaneous, cooperated and distributed manner. In our proposed mechanism we protect the integrity, authentication and confidentiality of cloud based data with the encrypt- then-upload concept. We modified and applied proxy re-encryption protocol in our proposed scheme. The whole process does not reveal the clear data to any third party including the cloud provider at any stage, this helps to make sure only the authorized user who own corresponding token able to access the data as well as preventing data from being shared without any permission from data owner. Besides, preventing the cloud storage providers from unauthorized access and making illegal authorization to access the data, our scheme also protect the data integrity by using hash function.

• Economic and Environmental Geology
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• v.53 no.3
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• pp.245-258
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• 2020

In this study, a data-driven response surface method using the results acquired from the numerical simulation is developed to evaluate the potential storage capacity of groundwater due to the construction of a groundwater dam. The hydraulic conductivities of alluvium and basement rock, depth and slope of the channel are considered as the natural conditions of the location for groundwater dam construction. In particular, the probability models of the hydraulic conductivities and the various types of geometry of the channel are considered to ensure the reliability of the numerical simulation and the generality of the developed estimation model. As the results of multiple simulations, it can be seen that the hydraulic conductivity of basement rock and the depth of the channel greatly influence to the groundwater storage capacity. In contrast, the slope of the channel along the groundwater flow direction shows a relatively lower impact on the storage capacity. Based on the considered natural conditions and the corresponding numerical simulation results, the storage capacity estimation model is developed applying an artificial neural network as the nonlinear regression model for training. The developed estimation model shows a high correlation coefficient (>0.9) between the simulated and the estimated storage amount. This result indicates the superiority of the developed model in evaluating the storage capacity of the potential location for groundwater dam construction without the numerical simulation. Therefore, a more objective and efficient comparison for the storage capacity between the different potential locations can be possibly made based on the developed estimation model. In line with this, the proposed method can be an effective tool to assess the optimal location of groundwater dam construction across Korea.

• Journal of KIISE:Databases
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• v.36 no.2
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• pp.99-111
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• 2009

In wireless sensor networks, various schemes have been proposed to store and process sensed data efficiently. A Data-Centric Storage(DCS) scheme assigns distributed data regions to sensors and stores sensed data to the sensor which is responsible for the data region overlapping the data. The DCS schemes have been proposed to reduce the communication cost for transmitting data and process exact queries and range queries efficiently. Recently, KDDCS that readjusts the distributed data regions dynamically to sensors based on K-D tree was proposed to overcome the storage hot-spots. However, the existing DCS schemes including KDDCS suffer from Query Hot-Spots that are formed if the query regions are not uniformly distributed. As a result, it causes reducing the life time of the sensor network. In this paper, we propose a new DCS scheme, called TPDCS(Time-Parameterized DCS), that avoids the problems of storage hot-spots and query hot-spots. To decentralize the skewed. data and queries, the data regions are assigned by a time dimension as well as data dimensions in our proposed scheme. Therefore, TPDCS extends the life time of sensor networks. It is shown through various experiments that our scheme outperform the existing schemes.