• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.68-75
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• 2016

As the number of cores and IPs increase in multiprocessor system-on-chip (MPSoC), network-on-chip (NoC) has emerged as a promising novel interconnection architecture for its parallelism and scalability. However, minimization of the latency in NoC with legacy bus IPs must be addressed. In this paper, we focus on the latency minimization problem in NoC which accommodates legacy bus protocol based IPs considering the trade-offs between hop counts and path collisions. To resolve this problem, we propose separated address/data network for independent address and data phases of bus protocol. Compared to Mesh and irregular topologies generated by TopGen, experimental results show that average latency and execution time are reduced by 19.46% and 10.55%, respectively.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.1
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• pp.77-86
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• 2001

With the growth of the EC(Electronic Commerce), Buying and selling software through the internet are expanded. Among the ESD(Electronic Software Distribution) methods, Buy-first method and TrY-before-bu1 method can not solve the illegal copy problem. Recently developed EL(Electronic License) model solve the illegal copy problem by separating the software and license. But this method also can not support various ways for payment. In this paper we propose the software license management system that is a newly form like ESD model. This system proposes NL(New License) to support various payment methods and SC(Software Charge) to insure that a seller takes the software price. Agent of the proposed system offers scalability to other systems and illegal copy protection function bv managing NL and SC.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.9
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• pp.503-513
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• 2002

Since CC-NUMA architecture has to access remote memory, the interconnection network determines the performance of the CC-NUMA system. Bus which has been used as a popular interconnection network has many limits in a large-scale system because of the limited physical scalability and bandwidth. The dual ring interconnection network, composed of high-speed point-to-point links, is made to resolve the defects of the bus for the large-scale system. However, it also has a problem, in that the response latency is rapidly increased when many nodes are attached to the snooping based CC-NUMA system with the dual ring. In this paper, we propose a ring architecture with repeater nodes in order to overcome the problem of the dual ring on a snooping based CC-NUMA system, and design a repeater node adapted to this architecture. We will also analyze the effects of proposed architecture on the system performance and the response latency by using a probability-driven simulator.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.507-520
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• 2017

The set-based similar sequence matching method measures similarity not for an individual data item but for a set grouping multiple data items. In the method, the similarity of two sets is represented as the size of intersection between them. However, there is a critical performances issue for the method in twofold: 1) calculating intersection size is a time consuming process, and 2) the number of set pairs that should be calculated the intersection size is quite large. In this paper, we propose an index-based search method for improving performance of set-based similar sequence matching in order to solve these performance issues. Our method consists of two parts. In the first part, we convert the set similarity problem into the intersection size comparison problem, and then, provide an index structure that accelerates the intersection size calculation. Second, we propose an efficient set-based similar sequence matching method which exploits the proposed index structure. Through experiments, we show that the proposed method reduces the execution time by 30 to 50 times then the existing methods. We also show that the proposed method has scalability since the performance gap becomes larger as the number of data sequences increases.

• Journal of KIISE:Information Networking
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• v.28 no.3
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• pp.321-335
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• 2001

The explosive growth in wireless networking increasingly urges the demand to support mobility within the Internet which is what Mobile-IP aims to provide. Because the transmission of signals through open-air s easy to be attacked, it is important to provide secure transmission for mobile users and make them responsible for what they have done in networks. Although IETF provides a secret-key based security mechanism, those mechanisms suffer from scalability, efficiency and non-repudiation service problem. The proposed mechanism uses public-key based authentication optimizing the performance. It includes non-repudiation service on the side of mobile for airtight security in wireless network. The simulation results show that the proposed authentication reduces the total registration time. It especially minimizes the computation cost on the side of the mobile node and solves the power problem. In practice, the proposed authentication is feasible with reasonable performance and security service in macro mobility that Mobile-IP is intended to solve.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6A
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• pp.870-884
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• 2000

Among OLAP(On-Line Analytical Processing) approaches, ROLAP(Relational OLAP) based on the star, snowflake schema which offer the multidimensional analytical method has performance problem and MOLAP (Multidimensional OLAP) based on Multidimensional Database System has scalability problem. In this paper, to solve the limitaions of previous approaches, design and implementation of multidimensional data model based on Object-Relation DBMS was proposed. With the extensibility of Object-Relation DBMS, it is possible to advent multidimensional data model which more expressively define multidimensional concept and analysis functions that are optimized for the defined multidimensional data model. In addition, through the hierarchy between data objects supported by Object-Relation DBMS, the aggregated data model which is inherited from the super-table, multidimensional data model, was designed. One these data models and functions are defined, they behave just like a built-in function, w th the full performance characteristics of Object-Relation DBMS engine.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.7
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• pp.209-216
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• 2023

Modern HPC systems are equipped with many-core CPUs with dozens of cores. When performing parallel I/O in such a system, there is a limit to scalability due to the problem of the LRU lock management policy of the Linux system. The study proposes an improved FinerLRU to solve this problem. Our new FinerLRU improves the parallel write performance of file systems using the buffer cache through granular lock management by increasing the number of LRU locks upto the maximum number of cores. The proposed method was implemented in Linux 5.18.11, and the performance was measured on two types of CPUs, Intel Icelake Xeon and Intel Knights landing, with different characteristics, and it was found that a performance improvement of about two times can be obtained in both types of systems.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.65-73
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• 2016

Peer-to-Peer (P2P) systems with super-peer overlay networks show combined advantages of both hybrid and pure P2P systems. Super-peer is a special peer acting as a server to a cluster of generic peers. Organizing a super-peer network is one of important issues for P2P systems with super-peer networks. Conventional P2P systems are based on two-level hierarchies of peers. One is a layer for generic peers and the other is for super-peers. And it is usual that super-peer networks have forms of random graphs. However, for accommodating a large-scale collection of generic peers, the super-peer network has also to be extended. In this paper, we propose a scheme of hierarchically constructing super-peer networks for large-scale P2P systems. At first, a two-level tree, called a simple super-peer network, is proposed, and then a scheme of generalizing and then extending the simple super-peer network to multi-level super-peer network is presented to construct a large-scale super-peer network. We call it an extended super-peer network. The simple super-peer network has several good features, but due to the fixed number of levels, it may have a scalability problem. Thus, it is extended to k-level tree of a super-peer network, called extended super-peer network. It shows good scalability and easy management of generic peers for large scale P2P system.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.12
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• pp.623-634
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• 2016

Most of existing frequent pattern mining methods address time efficiency and greatly rely on the primary memory. However, in the era of big data, the size of real-world databases to mined is exponentially increasing, and hence the primary memory is not sufficient enough to mine for frequent patterns from large real-world data sets. To solve this problem, there are some researches for frequent pattern mining method based on disk, but the processing time compared to the memory based methods took very time consuming. There are some researches to improve scalability of frequent pattern mining, but their processes are very time consuming compare to the memory based methods. In this paper, we present PPFP as a novel disk-based approach for mining frequent itemset from big data; and hence we reduced the main memory size bottleneck. PPFP algorithm is based on FP-growth method which is one of the most popular and efficient frequent pattern mining approaches. The mining with PPFP consists of two setps. (1) Constructing an IFP-tree: After construct FP-tree, we assign index number for each node in FP-tree with novel index numbering method, and then insert the indexed FP-tree (IFP-tree) into disk as IFP-table. (2) Mining frequent patterns with PPFP: Mine frequent patterns by expending patterns using stack based PUSH-POP method (PPFP method). Through this new approach, by using a very small amount of memory for recursive and time consuming operation in mining process, we improved the scalability and time efficiency of the frequent pattern mining. And the reported test results demonstrate them.