• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.203-206
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• 2001

This Paper suggests real-time video streaming method by using DCT-based scalability, and evaluates and analyzes the function. It is similar to using lowpass filter. That is, as following figure, this method is to split the encoded data in splitter and transmit it, and to decode the data according to the situation. This method can be applied to any video CODEC which is based on DCT. Therefore, this thesis suggests easy video streaming method by using DCT-based scalability, and shows the result of experiment. By using suggested scalability, calculations are reduced, and spacial scalability is realized. Moreover, the objective data which meet user's need according to the network condition and choose the appropriate scalability according to the capability of terming can be extracted. And it is possible to apply any resources according to the specificity of image.

• The KIPS Transactions:PartA
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• v.14A no.7
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• pp.443-450
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• 2007

Performance scalability and storage scalability become important in a large scale cluster of wireless internet proxy cache servers. Performance scalability means that the whole performance of the cluster increases linearly according as servers are added. Storage scalability means that the total size of cache storage in the cluster is constant, regardless of the number of cache servers used, if the whole cache data are partitioned and each partition is stored in each server, respectively. The Round-Robin based load balancing method generally used in a large scale server cluster shows the performance scalability but no storage scalability because all the requested URL data need to be stored in each server. The hashing based load balancing method shows storage scalability because all the requested URL data are partitioned and each partition is stored in each server, respectively. but, it shows no performance scalability in case of uneven pattern of client requests or Hot-Spot. In this paper, we propose a novel dynamic hashing method with performance and storage scalability. In a time interval, the proposed scheme keeps to find some of requested URLs allocated to overloaded servers and dynamically reallocate them to other less-loaded servers. We performed experiments using 16 PCs and experimental results show that the proposed method has the performance and storage scalability as different from the existing hashing method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.762-777
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• 2014

With the increasing usage of cloud applications such as MapReduce and social networking, the amount of data traffic in data center networks continues to grow. Moreover, these appli-cations follow the incast traffic pattern, where a large burst of traffic sent by a number of senders, accumulates simultaneously at the shallow-buffered data center switches. This causes severe packet losses. The currently deployed TCP is custom-tailored for the wide-area Internet. This causes cloud applications to suffer long completion times towing to the packet losses, and hence, results in a poor quality of service. An Explicit Congestion Notification (ECN)-based approach is an attractive solution that conservatively adjusts to the network congestion in advance. This legacy approach, however, lacks scalability in terms of the number of flows. In this paper, we reveal the primary cause of the scalability issue through analysis, and propose a new congestion-control algorithm called FaST. FaST employs a novel, virtual congestion window to conduct fine-grained congestion control that results in improved scalability. Fur-thermore, FaST is easy to deploy since it requires only a few software modifications at the server-side. Through ns-3 simulations, we show that FaST improves the scalability of data center networks compared with the existing approaches.

• Journal of Information Processing Systems
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• v.3 no.1
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• pp.1-7
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• 2007

In mobile ad hoc networks, an application scenario requires mostly collaborative mobility behavior. The key problem of those applications is scalability with regard to the number of multicast members as well as the number of the multicast group. To enhance scalability with group mobility, we have proposed a multicast protocol based on a new framework for hierarchical multicasting that is suitable for the group mobility model in MANET. The key design goal of this protocol is to solve the problem of reflecting the node's mobility in the overlay multicast tree, the efficient data delivery within the sub-group with group mobility support, and the scalability problem for the large multicast group size. The results obtained through simulations show that our approach supports scalability and efficient data transmission utilizing the characteristic of group mobility.

• Journal of Information Technology Applications and Management
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• v.12 no.3
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• pp.41-56
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• 2005

Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability Problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.

• World Technopolis Review
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• v.8 no.1
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• pp.21-42
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• 2019

The purpose of this research was to investigate the factors influencing global expansion/scalability of Kenyan Small and Medium Enterprises (SMEs). Factor analysis and multiple/multivariate regression analysis to determine the functional relationship between independent variables (factors) and the dependent variable was used. The independent variables were: innovation & technology, fitness/appropriateness of management, global marketing strategy; and support environment and the dependent variable, global expansion/scalability. Data was collected from a survey of randomly selected firms of 205, drawn from a population of 440 firms from Kenya Manufacturers Directory, with 175 firms responding. The key findings from the research in relation to Kenyan SMEs were that: there is a functional relationship between global market strategy and global expansion; there is a functional relationship between innovation and technology orientation and global expansion, there is no significant functional relationship between supportive environment of firms and their global expansion; and there is no significant functional relationship between fitness/appropriateness of management and global expansion/scalability. The implications for practice is that the ranking of the factors in order of priority supports focusing concern on the orientation of business strategy toward global market strategy, market research geared at obtaining foreign market intelligence, innovation and technology, product adaptation, service orientation, collaborative ventures, and long-range vision as key factors in making Kenyan firms successful in the international market. The implication for policy and practice is that there is need for collaboration between industry and government in pursuing policies for global expansion/scalability and among SMEs and large enterprises particularly in areas of rapid technological change.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.1-9
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• 2020

In this paper, we propose a method of in-depth analysis of internal operation process by recording library calls and related information that occur in the operation process of NoSQL database. It observes and records the specified library calls, compares the internal behavior differences between the NoSQL databases through recorded library call information, and evaluates the characteristics and scalability of each database by observing changes in the number of input data. The development of computing performance and the activation of big data have led to the emergence of different types of NoSQL databases for recording and analyzing various and large amounts of data, and it is necessary to evaluate the scalability of each database in order to select a database suitable for each environment. However, it is difficult to analyze or predict how a database operates in traditional ways, such as benchmarking, observing external behavior through performance models, or analyzing structural features based on design. Therefore, it is necessary to utilize the techniques proposed in this paper to understand the scalability of NoSQL databases with high accuracy.

• Journal of Industrial Convergence
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• v.21 no.8
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• pp.51-58
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• 2023

Blockchains have recently struggled to design for the ideal distributed trust networks by solving scalability trilemma. However, local conflicts between some countries lead to imbalance on energy distribution. Besides, blockchain networks (e.g., Bitcoin) currently consume enormous energy for transaction and mining. The existing data volume based trust model evaluated an increasing blockchain size better than Lubin's trust model in scalability trilemma. In this paper, we propose a scalability based energy model to evaluate sustainability for blockchain networks, considering energy consumption for transaction, time duration, and the blockchain size of growing blockchain networks. Through the rigorous numerical analysis, we compare the proposed scalability based energy model with the existing model for the satisfaction and optimal blockchain size. Thus, the scalability based energy model will provide an assessment tool to choose the proper blockchain networks to solve scalability trilemma problem and prove sustainability.

• KNOM Review
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• v.22 no.2
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• pp.22-28
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• 2019

A Science DeMilitarized Zone (DMZ) is an optimized network technology tailored to research data nature. The Science DMZ guarantees end-to-end network performance by forming a closed research network without redundant networking and security devices for the authorized researchers. Data Transfer Node (DTN) is an essential component for the high performance and security of the Science DMZ, since only transfer functions of research data are allowed to the DTN without any security- and performance-threatening functions such as commercial internet service. Current Science DMZ requires per-user DTN server installation which turns out a scalability limitation of the networks in terms of management overhead, entry barrier of the user, and networks-wise CAPEX. In order to relax the aforementioned scalability issues, this paper suggests a centralized DTN design where end users in a group can share the centralized DTN. We evaluate the effectiveness of the suggested sharable DTN design by comparing CAPEX against to that of current design with respect to the diverse network load and the state-of-the-art computing machine.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.567-582
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• 2011

The growing popularity of the Internet has caused the size and complexity of communications and computing systems to greatly increase in recent years. To alleviate this increased management complexity, novel autonomic management architectures have emerged, in which many automated components manage the network's resources in a distributed fashion. However, in order to achieve effective collaboration between these management components, they need to be able to efficiently exchange information in a timely fashion. In this article, we propose a context dissemination framework that addresses this problem. To achieve scalability, the management components are structured in a hierarchy. The framework facilitates the aggregation and translation of information as it is propagated through the hierarchy. Additionally, by way of semantics, context is filtered based on meaning and is disseminated intelligently according to dynamically changing context requirements. This significantly reduces the exchange of superfluous context and thus further increases scalability. The large size of modern federated cloud computing infrastructures, makes the presented context dissemination framework ideally suited to improve their management efficiency and scalability. The specific context requirements for the management of a cloud data center are identified, and our context dissemination approach is applied to it. Additionally, an extensive evaluation of the framework in a large-scale cloud data center scenario was performed in order to characterize the benefits of our approach, in terms of scalability and reasoning time.