• Journal of Information Processing Systems
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• v.2 no.2
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• pp.130-134
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• 2006

In the distributed replicating server model, the provision of replicated services will improve the performance of the providing service and efficiency for clients. Efficiently composing the server selection algorithm decreases the retrieval time for replicated data. In this paper, we define the system model that selects and connects the replicated server that provides an optimal service using the server-side downstream measurement and propose a server selection algorithm.

• Journal of the Korea Society for Simulation
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• v.14 no.2
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• pp.1-13
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• 2005

In distributed replicating server model, providing replicated services is able to improve the performance of providing a service and efficiency for several clients. And, the composition of the server selection algorithm is efficiently able to decrease the retrieval time for replicated data. In this paper, we define the system model that selects and connects the replicated server that provides optimal service using server-side downstream measurement and proposes an applicable algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1084-1089
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• 2004

This paper describes the improvement of web cache server by scoping in replacement algorithm of data which are collected from the clients. We have found that each replacement algorithm is suitable for each type of data in the web pages. Therefore, we introduce the mechanism to select the replacement algorithm depending on the size of data called the Replacement Algorithm Selection Mechanism (RASM). RASM allows the web cache server to have the suitable replacement algorithm for each type of data. As the result, the byte hit ratio of web cache server can be increased and the congestion in the network can be alleviated.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.65-65
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• 2000

For Video on Demand(VoD) servers, a design of an efficient scheduler is important to the support a large number of clients having various playback speeds and receiving rates. In this paper, we propose the scheduling algorithm to handle establishing deadlines and selection using the earliest deadline first. To establish deadlines and selections, the period of the receiving rates for each client is located between the over-max receiving rate and the over-playback rate. To avoid video starvation and the buffer overflow of each client, the proposed algorithm guarantees providing the admission control. Because of establishing deadlines and selection, period of each client receiving is between one over max receiving rate and one over play back rate. Using Virtual Buffer in server, scheduling load is reduced. The efficiency of the proposed algorithm is verified using a Petri Net_Based simulation tool, Exspect.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.217-228
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• 2011

In centralize CDN systems, the content server selection is performed by service node for every user request, and the selected node is notified to the user. In this paper, we present distributed CDN architecture and algorithm in which the request from a user is delivered to the content source by a P2P algorithm utilizing DHT(distributed hash table) through the overlay network and the user selects one of the source nodes based on real-time user-centric criteria. For this purpose, we propose a modified Pastry algorithm for contents registration, search and selection, in addition to the distributed architecture. The proposed architecture has the advantages of load balancing, traffic balancing, scalability, fault-tolerance due to the self-configuration, self-healing attributes of distributed architecture. Various simulation shows the feasibility of the proposed architecture and algorithm, and the performance is compared and discussed for the variations of the proposed scheme.

• Journal of IKEEE
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• v.22 no.3
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• pp.570-577
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• 2018

With the increasing number of network nodes, traditional client/server node selection mechanisms are under tremendous pressure. In order to select efficient cooperative nodes in a highly dynamic P2P network topology, this article uses greedy algorithm to translate the overall optimization into multiple local optimal problems, and to quickly select service nodes. Therefore, the service node with the largest comprehensive capacity is selected to reduce the transmission delay and improve the throughput of the service node. The final simulation results show that the node selection strategy based on greedy algorithm can effectively improve the overall performance of P2P streaming media system.

• The KIPS Transactions:PartA
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• v.11A no.6
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• pp.413-418
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• 2004

According as computer hardware became smaller and its performing power became improved due to the developed computer technology, clients demand more powerful accessibility to various information through getting the information at every time and every place. These needs led to the Ubiquitous computing technology which makes it possible to have access to a specific information regardless of clients' physical location. In this paper, we propose an approach that mobile host can get data from proper contents server by applying Contents Distributing Network to wireless network. By simulation, this paper proves that this approach can Improve performance by using contents server selection in wireless network. This research will contribute to realizing the Ubiquitous computing technology.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.118-120
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• 2015

현재, 급격한 IT 기술 발전의 산물인 스마트 디바이스에서 발생되는 데이터의 양이 기하급수적으로 증가함에 따라 효율적으로 저장하기 위한 다양한 연구 및 기술 개발이 진행되고 있다. 이 중에 Desktop Storage Virtualization(DSV)은 비가용 데스크톱 스토리지 자원을 하나의 통합된 스토리지로 만들고 이를 스토리지 사용자 니즈에 따라 스토리지를 제공한다. DSV는 분산된 데스크탑의 비가용 자원을 통합함에 따라 신뢰성이 매우 중요시된다. 그러나 DSV 환경에서 산발적 서버다운 및 기타 장애시 대응체계가 미흡하다. 본 논문에서는 이러한 상황을 고려한 Server Selection Algorithm(SSA)를 제시한다. SSA는 서버 장애 발생시 대체 서버를 분산된 데스크탑이 적응적 인지 및 서버를 변경함으로써 고가용성을 제공한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.921-940
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• 2015

In this paper, we investigate the multi-resource allocation problem, a unique feature of which is that the multiple resources can compensate each other while achieving the desired system performance. In particular, power and time allocations are jointly optimized with the target of energy efficiency under the resource-limited constraints. Different from previous studies on the power-time tradeoff, we consider a multi-server case where the concurrent serving users are quantitatively restricted. Therefore user selection is investigated accompanying the resource allocation, making the power-time tradeoff occur not only between the users in the same server but also in different servers. The complex multivariate optimization problem can be modeled as a variant of 2-Dimension Bin Packing Problem (V2D-BPP), which is a joint non-linear and integer programming problem. Though we use state decomposition model to transform it into a convex optimization problem, the variables are still coupled. Therefore, we propose an Iterative Dual Optimization (IDO) algorithm to obtain its optimal solution. Simulations show that the joint multi-resource allocation algorithm outperforms two existing non-joint algorithms from the perspective of energy efficiency.

• Journal of Communications and Networks
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• v.17 no.3
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• pp.306-320
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• 2015

Due to an explosive growth in services running in different datacenters, there is need for service selection and routing to deliver user requests to the best service instance. In current solutions, it is generally the client that must first select a datacenter to forward the request to before an internal load-balancer of the selected datacenter can select the optimal instance. An optimal selection requires knowledge of both network and server characteristics, making clients less suitable to make this decision. Information-Centric Networking (ICN) research solved a similar selection problem for static data retrieval by integrating content delivery as a native network feature. We address the selection problem for services by extending the ICN-principles for services. In this paper we present Queue and Latency, a network-driven service selection algorithm which maps user demand to service instances, taking into account both network and server metrics. To reduce the size of service router forwarding tables, we present a statistical method to approximate an optimal load distribution with minimized router state required. Simulation results show that our statistical routing approach approximates the average system response time of source-based routing with minimized state in forwarding tables.