• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.143-146
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• 2015

Software Defined Network (SDN) separates control plane and data plane to achieve benefits such as centralized management, centralized provisioning, lower device cost and more flexibility. In SDN, scalability is an important issue. Centralized controller can be a bottle neck and many research tried to solve this issue on the control plan. However, scalability issue does not only happen in the control plane, but also happen in the data plane. In the data plane, flow table is an important component and its size is limited. In a large network operated by SDN technology, the performance of the network can be highly degraded because of the size limitation of a flow table. In this paper, we propose a ranking-based flow replacement method, Flow Table Management (FTM), to overcome this problem.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.240-244
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• 2022

The common goals of designing a routing algorithm are not only to reduce control packet overhead, maximize throughput and minimize the end-to-end delay, but also take into consideration the energy consumption. Scalability is an important factor in designing an efficient routing protocol for wireless sensor networks (WSN's). Three metrics (power consumption, time of transmission and packet loss rate) are used in order to compare three routing protocols which are AODV, DSDV and LEACH.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.361-370
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• 2023

Swarm has recently become a critical component of offensive and defensive systems. Multi-agent reinforcement learning(MARL) empowers swarm systems to handle a wide range of scenarios. However, the main challenge lies in MARL's scalability issue - as the number of agents increases, the performance of the learning decreases. In this study, transfer learning is applied to advanced MARL algorithm to resolve the scalability issue. Validation results show that the training efficiency has significantly improved, reducing computational time by 31 %.

• The KIPS Transactions:PartC
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• v.11C no.4
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• pp.527-538
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• 2004

In this paper, we present the flow aggregation method and the FLTC(flow lasting time control) algorithm to reduce the number of flows and solve the scalability problem in high speed IP switching networks. The flow aggregation based on the destination address could reduce the total number of flows, improve the label efficiency, and increase the total amount of the switched packets. The FLTC algorithm also eliminates the waste of label by deleting the flow binding efficiently. With the traces of real Internet traffics, we evaluate the performance of these schemes by simulation. The label efficiency, the average number of label used, and the percentage of packets switched and the number of packets switched are used as performance measures for this simulation.

• Journal of Korea Game Society
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• v.2 no.1
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• pp.42-48
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• 2002

Many web-based simulation environments have been proposed to create models and execute simulation through the Web. Although web-based simulation has the advantage of utilizing many computers connected to Internet, overhead of the web server and heavy traffic caused by Java RMI degrade the performance and scalability of simulation. In this paper, we design and implement web-based distributed simulator using Java for the scalability and performance. In addition, we can apply the proposed simulator to create model server and client engines in online game. We apply the proposed simulator to simulate ATM switch queueing model.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.157-172
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• 2013

There are many recommendation systems available to provide users with personalized services. Among them, the most frequently used in electronic commerce is 'collaborative filtering', which is a technique that provides a process of filtering customer information for the preparation of profiles and making recommendations of products that are expected to be preferred by other users, based on such information profiles. Collaborative filtering systems, however, have in their nature both technical issues such as sparsity, scalability, and transparency, as well as security issues in the collection of the information that becomes the basis for preparation of the profiles. In this paper, we suggest a movie recommendation system, based on the selection of optimal personal propensity variables and the utilization of a secure collaborating filtering system, in order to provide a solution to such sparsity and scalability issues. At the same time, we adopt 'push attack' principles to deal with the security vulnerability of collaborative filtering systems. Furthermore, we assess the system's applicability by using the open database MovieLens, and present a personal propensity framework for improvement in the performance of recommender systems. We successfully come up with a movie recommendation system through the selection of optimal personalization factors and the embodiment of a safe collaborative filtering system.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.5
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• pp.58-70
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• 2008

Earlier researches on Sensor Networks preferred symmetric key-based authentication schemes in consideration of limitations in network resources. However, recent advancements in cryptographic algorithms and sensor-node manufacturing techniques have opened suggestion to public key-based solutions such as Merkle tree-based schemes. These previous schemes, however, must perform the authentication process one-by-one in hierarchical manner and thus are not fit to be used as primary authentication methods in sensor networks which require mass of multiple authentications at any given time. This paper proposes a new concept of public key-based authentication that can be effectively applied to sensor networks. This scheme is based on exponential distributed data concept, a derivative from Shamir's (t, n) threshold scheme, in which the authentication of neighbouring nodes are done simultaneously while minimising resources of sensor nodes and providing network scalability. The performance advantages of this scheme on memory usage, communication overload and scalability compared to Merkle tree-based authentication are clearly demonstrated using performance analysis.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.121-136
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• 2023

Wireless Sensor Networks (WSNs) have many potential applications and unique challenges. Some problems of WSNs are: severe resources' constraints, low reliability and fault tolerant, low throughput, low scalability, low Quality of Service (QoS) and insecure operational environments. One significant solution against mentioned problems is hierarchical and clustering-based multipath routing. But, existent algorithms have many weaknesses such as: high overhead, security vulnerabilities, address-centric, low-scalability, permanent usage of optimal paths and severe resources' consumption. As a result, this paper is proposed an energy-aware, congestion-aware, location-based, data-centric, scalable, hierarchical and clustering-based multipath routing algorithm based on Numerical Taxonomy technique for homogenous WSNs. Finally, performance of the proposed algorithm has been compared with performance of LEACH routing algorithm; results of simulations and statistical-mathematical analysis are showing the proposed algorithm has been improved in terms of parameters like balanced resources' consumption such as energy and bandwidth, throughput, reliability and fault tolerant, accuracy, QoS such as average rate of packet delivery and WSNs' lifetime.

• Journal of Creative Information Culture
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• v.4 no.3
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• pp.221-230
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• 2018

Collaborative filtering technique is a major method of recommender systems and has been successfully implemented and serviced in real commercial online systems. However, this technique has several inherent drawbacks, such as data sparsity, cold-start, and scalability problem. Clustering-based collaborative filtering has been studied in order to handle scalability problem. This study suggests a collaborative filtering system which utilizes genetic algorithms to improve shortcomings of K-means algorithm, one of the widely used clustering techniques. Moreover, different from the previous studies that have targeted for optimized clustering results, the proposed method targets the optimization of performance of the collaborative filtering system using the clustering results, which practically can enhance the system performance.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.397-399
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• 2004

Generally microkernel has properties of Portability, reusability and scalability. In particular microkernel technique has been applied to development of real time kernel on embedded system because life cycle of micro processor is shortened. In this paper we study Properties of micro kernel and Comparative Performance of Nucleus and ${\mu}ITRON$ based on microkernel.