• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.161-168
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• 2024

As the size of big data models grows, distributed training is emerging as an essential element for large-scale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.12C
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• pp.1707-1721
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• 2004

In this paper, we propose a distributed communication model of intrusion detection system(IDS) in which integrated security management at networks level is possible, model it at a security node and distributed system levels, design and implement a simulator. At the node level, we evaluate the transfer capability of alert message based on the analysis of giga-bit security node architecture which performs hardware-based intrusion detection. At the distributed system level, we perform the evaluation of transfer capability of detection and alert informations between components of distributed IDS. In the proposed model, we carry out the performance evaluation considering decision factors of communication mechanism and present the results in order to gain some quantitative understanding of the system.

• Journal of the Korea Computer Industry Society
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• v.3 no.8
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• pp.1019-1026
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• 2002

The Distributed system environments are developing into a structure of the opening information communication network based on object-oriented concepts and distributed technologies. Such a reason is that it can not only decrease the complexity of distributed softwares, but also support object oriented application services to distributed environments without changes of its own communication network structure. In this paper, we designed object group model, that is, an extended middleware, we developed before. The distributed environment suggested consists of the CTS server and clients. As our results, we showed the interaction procedures between/among object groups and service procedures of distributed application in given distributed system.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1621-1624
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• 2003

As the entities participating Grid become larger, performance requirement for the LDAP-based GIS(Grid Information Service) goes beyond that provided by a stand-alone single LDAP server. This entails the exploration of distributed LDAP systems. This paper presents the performance evaluation respectively for a distribution-based and a replication-based LDAP model. The analysis is based on an analytic performance model for each distributed system which is obtained by applying the M/M/1 queuing model. The performance evaluation made to these analytic models reveals that the distribution-based and the replication-based model show a significant tradeoff in their performance with respect to the system size as well as the amount of system load.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3771-3788
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• 2015

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.

• Korean Journal of Computational Design and Engineering
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• v.5 no.1
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• pp.12-22
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• 2000

Network and Internet technology opens up another domain for building future CAD/CAM environment. The environment will be global, network-centric, and spatially distributed. In this paper, we present an approach for network-centric feature-based modeling in a distributed design environment. The presented approach combines the current feature-based modeling technique with distributed computing and communication technology for supporting product modeling and collaborative design activities over the network. The approach is implemented in a client/server architecture, in which Web-enabled feature modeling clients, neutral feature model server, and other applications communicate with one another via a standard communication protocol. The paper discusses how the neutral feature model supports multiple views and maintains naming consistency between geometric entities of the server and clients. Moreover, it explains how to minimize the network delay between the server and client according to incremental feature modeling operations.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.73-85
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• 2022

In the era of the 4th industrial revolution, where automation and connectivity are maximized with artificial intelligence, the importance of data collection and utilization for model update is increasing. In order to create a model using artificial intelligence technology, it is usually necessary to gather data in one place so that it can be updated, but this can infringe users' privacy. In this paper, we introduce federated learning, a distributed machine learning method that can update models in cooperation without directly sharing distributed stored data, and introduce a study to optimize distributed consensus among participants without an existing server. In addition, we propose a pattern and group-based distributed consensus optimization algorithm that uses an algorithm for generating patterns and groups based on the Kirkman Triple System, and performs parallel updates and communication. This algorithm guarantees more privacy than the existing distributed consensus optimization algorithm and reduces the communication time until the model converges.

• Proceedings of the IEEK Conference
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• summer
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• pp.47-51
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• 2004

In this paper we propose a fair and secure e-commerce model for P2P network, in which communication entities can buy and sell products by P2P contract. In particular, we focus on a fair transaction protocol that is based on a collaboration with distributed communication entities. This feature makes our model very attractive in P2P networking environment which does not depend on any central trusted authority for managing communication entities.

• Journal of Internet Computing and Services
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• v.8 no.1
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• pp.133-142
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• 2007

Large database processing is one of the most important technique in the information society, Since most large database is regionally distributed, the distributed database processing has been brought into relief. Communications and data compressions are the basic technologies for large database processing. In order to maximize those technologies, the execution time for the task, the size of data, and communication time between processors should be considered. In this paper, the dataflow scheme and vertically layered allocation algorithm have been used to optimize the distributed large database processing. The basic concept of this method is rearrangement of processes considering the communication time between processors. The paper also introduces measurement model of the execution time, the size of output data, and the communication time in order to implement the proposed scheme.

• The Transactions of the Korea Information Processing Society
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• v.6 no.1
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• pp.225-233
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• 1999

One of the reasons that debugging distributed programs is much more difficult than sequential programs is the communication among processes. Even though there are many debuggers constructed on distributed system environment, no available debugger provides an efficient way to debug communication events.. In this paper, we show the way to debug RPC communication, which is regarded as one of the most popular communication protocol in distributed system development. This paper presents the implementation of the RPC event trace function based on the RPC event model, which is proposed to accommodate communication events into debugging objects. Analyzing conventional RPC protocols, we formalized library function calls as corresponding actions to sending and receiving of messages. B recognizing the locations of library calls the debugger can detect all occurrences of communication events, This RPC event trace function is implemented on the on-line distributed debugger UniVIEW system.