• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.05a
• /
• pp.649-651
• /
• 2017

A clustering problem is one of the organizational problems to improve the network lifetime and scalability in wireless ad-hoc networks. This problem is a difficult combinatorial optimization problem associated with the design and operation of these networks. In this paper, we propose an efficient clustering algorithm to maximize the network lifetime and consider scalability in wireless ad-hoc networks. The clustering problem is known to be NP-hard. We thus solve the problem by using optimization approaches that are able to efficiently obtain high quality solutions within a reasonable time for a large size network. The proposed algorithm selects clusterheads and configures clusters by considering both nodes' power and the clustering cost. We evaluate this performance through some experiments in terms of nodes' transmission energy. Simulation results indicate that the proposed algorithm performs much better than the existing algorithms.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.5
• /
• pp.99-106
• /
• 2020

The scalability problem inherent in collaborative filtering-based recommender systems has been an issue in related studies during past decades. Clustering is a well-known technique for handling this problem, but has not been actively studied due to its low performance. This paper adopts a clustering method to overcome the scalability problem, inherent drawback of collaborative filtering systems. Furthermore, in order to handle performance degradation caused by applying clustering into collaborative filtering, we take two strategies into account. First, we use fuzzy clustering and secondly, we propose and apply a similarity estimation method based on user preference for movie genres. The proposed method of this study is evaluated through experiments and compared with several previous relevant methods in terms of major performance metrics. Experimental results show that the proposed demonstrated superior performance in prediction and rank accuracies and comparable performance to the best method in our experiments in recommendation accuracy.

• Journal of KIISE:Software and Applications
• /
• v.37 no.7
• /
• pp.503-517
• /
• 2010

Service-oriented computing, the effective paradigm for developing service applications by using reusable services, becomes popular. In service-oriented computing, service consumer has no responsibility for managing services, just invokes services what service providers are producing. On the other hand, service providers should manage any resources and data for service consumers can use the service anytime and anywhere. However, it is hard service providers manage the quality of the services because an unspecified number of service consumers. Therefore, service scalability for providing services with higher quality of services specified in a service level agreement becomes a potential problem in service-oriented computing. There have been many researches for scalability in network, database, and distributed computing area. But a research about a definition of service scalability and metrics of measuring service scalability is still not mature in service engineering area. In this paper, we construct a service network which connects multiple service nodes, and integrate all the resources to manage it. And we also present a service scalability framework for managing service scalability by using a mechanism of service migration or replication. In section 3, we, firstly, present the structure of the scalability management framework and basic functionalities. In section 4, we propose scalability enhancement mechanism which is needed to release functionality of the framework. In section 5, we design and implement the framework by using proposed mechanism. In section 6, we demonstrate the result of our case study which dynamically manages services in multi-nodes environment by applying our framework. Through the case study, we show the applicability of our scalability management framework and mechanism.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
• /
• 2002.11a
• /
• pp.195-199
• /
• 2002

Requirements of a well-designed VPNs(Virtual Private Networks) are scalability, performance, reliability, ease of management, interoperability and security. Tunneling is a important technology to support these. This paper researches VPNs tunneling technologies used currently and proposes VPN service models for the scalability that is a problem in VPNs and for the resource limit of Mobile Station in Mobile VPNs environment.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.11a
• /
• pp.1050-1052
• /
• 2010

Locator and Identifier Split is considered as the solution to the scalability problem Internet is facing today. The separation approach of Locator and Identifier requires a third party called mapping system. The mapping system enables the inter-domain routing between two different edge networks. The design of this third party has generated many proposals, among them one approach use Border Gateway Protocol (BGP) for effective mapping information updates distribution. In this paper, we take advantage of this approach by considering the scalability in term of mapping information storage. Our goal is to provide scalability in term of mapping information storage as well as effective mapping information updates distribution.

• The KIPS Transactions:PartC
• /
• v.9C no.5
• /
• pp.725-732
• /
• 2002

Presented is a Genetic Algorithm (GA) for dynamic partitioning an ATM LANE(LAN Emulation) network. LANE proves to be one of the best solutions to provide guaranteed Quality of Service (QoS) for mid-size campus or enterprise networks with minor modification of legacy LAN facilities. However, there are few researches on the efficient LANE network operations to deal with scalability issues arising from broadcast traffic delivery. To cope with this scalability issue, proposed is a decision model named LANE Partitioning Problem (LPP) which aims at partitioning the entire LANE network into multiple Emulated LANs (ELANS), each of which works as an independent virtual LAN.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.6 no.2
• /
• pp.110-115
• /
• 2006

In pure P2P environment, it is possible to provide service by using a little real-time information without using accumulated information. But in case of using only a little information that was locally collected, quality of recommendation service can be fallen-off. Therefore, it is necessary to study a method to improve qualify of recommendation service by using users' context information. But because a great volume of users' context information can be recognized in a moment, there can be a scalability problem and there are limitations in supporting differentiated services according to fields and items. In this paper, we solved the scalability problem by clustering context information per each service field and classifying it per each user, using SOM. In addition, we could recommend proper services for users by quantifying the context information of the users belonging to the similar classification to the service requester among classified data and then using collaborative filtering.

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

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.7
• /
• pp.887-892
• /
• 2005

Under pure P2P environments, collaborative filtering must be provided with only a few service items by real time information without accumulated data. However, in case of collaborative filtering with only a few service items collected locally, quality of recommended service becomes low. Therefore, it is necessary to research a method to improve quality of recommended service by users' context information. But because a great volume of users' context information can be recognized in a moment, there can be a scalability problem and there are limitations in supporting differentiated services according to fields and items. In this paper, we solved the scalability problem by clustering context information Per each service field and classifying il per each user, using SOM. In addition, we could recommend proper services for users by measuring the context information of the users belonging to the similar classification to the service requester among classified data and then using collaborative filtering.

• Journal of Korea Society of Industrial Information Systems
• /
• v.27 no.2
• /
• pp.61-69
• /
• 2022

A multi-agent system is a system that aims at achieving the best-coordinated decision based on each agent's local decision. In this paper, we consider a multi agent-multi task assignment problem. Each agent is assigned to only one task and there is a completion probability for performing. The objective is to determine an assignment that maximizes the sum of the completion probabilities for all tasks. The problem, expressed as a non-linear objective function and combinatorial optimization, is NP-hard. It is necessary to design an effective and efficient solution methodology. This paper presents an approximation algorithm using submodularity, which means a marginal gain diminishing, and demonstrates the scalability and robustness of the algorithm in theoretical and experimental ways.