• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.117-135
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• 2016

Performance enhancement is one of the most important issues in high performance distributed computing systems. In such computing systems, online users submit their jobs anytime and anywhere to a set of dynamic resources. Jobs arrival and processes execution times are stochastic. The performance of a distributed computing system can be improved by using an effective load balancing strategy to redistribute the user tasks among computing resources for efficient utilization. This paper presents a multi-class load balancing strategy that balances different classes of user tasks on multiple heterogeneous computing nodes to minimize the per-class mean response time. For a wide range of system parameters, the performance of the proposed multi-class load balancing strategy is compared with that of the random distribution load balancing, and uniform distribution load balancing strategies using simulation. The results show that, the proposed strategy outperforms the other two studied strategies in terms of average task response time, and average computing nodes utilization.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.69-75
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• 2006

Internet-based distributed computing environment have been developed for advanced science and engineering by sharing large-scale resources. Therefore efficient scheduling algorithms for allocating user job to resources in the Internet-based distributed computing environment are required. Many scheduling algorithms have been proposed. but these algorithms are not suitable for the Internet-based Distributed computing environment. That is the previous scheduling algorithm does not consider peer self-control. In this paper, we propose a Peer Availability Period Prediction Strategy for Internet-based distributed computing environment and show that our Strategy has better performance than other Strategy through extensive simulation.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.615-629
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• 2021

With the development of mobile edge computing, how to utilize the computing power of edge computing to effectively and efficiently offload data and to compute offloading is of great research value. This paper studies the computation offloading problem of multi-user and multi-server in mobile edge computing. Firstly, in order to minimize system energy consumption, the problem is modeled by considering the joint optimization of the offloading strategy and the wireless and computing resource allocation in a multi-user and multi-server scenario. Additionally, this paper explores the computation offloading scheme to optimize the overall cost. As the centralized optimization method is an NP problem, the game method is used to achieve effective computation offloading in a distributed manner. The decision problem of distributed computation offloading between the mobile equipment is modeled as a multi-user computation offloading game. There is a Nash equilibrium in this game, and it can be achieved by a limited number of iterations. Then, we propose a distributed computation offloading algorithm, which first calculates offloading weights, and then distributedly iterates by the time slot to update the computation offloading decision. Finally, the algorithm is verified by simulation experiments. Simulation results show that our proposed algorithm can achieve the balance by a limited number of iterations. At the same time, the algorithm outperforms several other advanced computation offloading algorithms in terms of the number of users and overall overheads for beneficial decision-making.

• Smart Structures and Systems
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• v.3 no.4
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• pp.455-474
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• 2007

A flexibility-based distributed computing strategy (DCS) for structural health monitoring (SHM) has recently been proposed which is suitable for implementation on a network of densely distributed smart sensors. This approach uses a hierarchical strategy in which adjacent smart sensors are grouped together to form sensor communities. A flexibility-based damage detection method is employed to evaluate the condition of the local elements within the communities by utilizing only locally measured information. The damage detection results in these communities are then communicated with the surrounding communities and sent back to a central station. Structural health monitoring can be done without relying on central data acquisition and processing. The main purpose of this paper is to experimentally verify this flexibility-based DCS approach using wired sensors; such verification is essential prior to implementation on a smart sensor platform. The damage locating vector method that forms foundation of the DCS approach is briefly reviewed, followed by an overview of the DCS approach. This flexibility-based approach is then experimentally verified employing a 5.6 m long three-dimensional truss structure. To simulate damage in the structure, the original truss members are replaced by ones with a reduced cross section. Both single and multiple damage scenarios are studied. Experimental results show that the DCS approach can successfully detect the damage at local elements using only locally measured information.

• Journal of Korea Multimedia Society
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• v.7 no.5
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• pp.755-765
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• 2004

Recently, the Internet-based distributed/parallel computing using many of idle hosts has been demonstrated its usefulness for processings of a large-scale task and involving several important issues. While executing a large-scale task, the realtime monitoring is required for adaptive strategy of the performance and state change of host. This paper provides the realtime monitoring and visualization on global computing infrastructure called PDP(Parallel Distributed Processing) which is a parallel computing framework implemented with Jana for parallel computing on the Internet.

• Proceedings of the Korea Database Society Conference
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• 1994.09a
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• pp.49-69
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• 1994

A client/server system has become the computing architecture for the business organization which seeks competitive edges. Technically, a client/server system places application processing close to the user and thus increases performance. This paper's two primary goals are (i) to present a performance model for client/server database systems and (ii) to demonstrate analytically the effectiveness of client/server computing in comparison with other computing architectures via an illustrative example. The model is most likely to be used as a practical performance guide for client/server computing.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.489-504
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• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

• Proceedings of the Korea Database Society Conference
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• 1994.09a
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• pp.105-129
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• 1994

The information system of corporations are facing a new environment expressed by miniaturization, decentralization and Open System. It is therefore of utmost importance for corporations to adapt flexibly th such new environment by providing for corresponding changes to their existing information systems. The objectives of this study are to identify this new environment faced by today′s information system and develop effective methods for data resource management under this new environment. In this study, it is assumed that the new environment faced by information systems can be specified as Distributed Computing Environment, and in order to achieve such system, presents Client/server architecture as its representative computing structure, This study defines Client/server architecture as a computing architecture which specialize the fuctionality of the client system and the server system in order to have an application distribute and perform cooperative processing at the best platform. Furthermore, from among the five structures utilized in Client/server architecture for distribution and cooperative processing of application between server and client this study presents two different data management methods under the Client/server environment; one is "Remote Data Management Method" which uses file server or database server and. the other is "Distributed Data Management Method" using distributed database management system. The result of this study leads to the conclusion that in the client/server environment although distributed application is assumed, the data could become centralized (in the case of file server or database server) or decentralized (in the case of distributed database system) and the data management method through a distributed database system where complete responsibility and powers with respect to control of data used by the user are given not only is it more adaptable to modern flexible corporate environment, but in terms of system operation, it presents a more efficient data management alternative compared to existing data management methods in terms of cutting costs.

• Smart Structures and Systems
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• v.3 no.3
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• pp.299-320
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• 2007

Smart sensors densely distributed over structures can provide rich information for structural monitoring using their onboard wireless communication and computational capabilities. However, issues such as time synchronization error, data loss, and dealing with large amounts of harvested data have limited the implementation of full-fledged systems. Limited network resources (e.g. battery power, storage space, bandwidth, etc.) make these issues quite challenging. This paper first investigates the effects of time synchronization error and data loss, aiming to clarify requirements on synchronization accuracy and communication reliability in SHM applications. Coordinated computing is then examined as a way to manage large amounts of data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4063-4086
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• 2016

Cloud computing is a robust technology, which facilitate to resolve many parallel distributed computing issues in the modern Big Data environment. Hadoop is an ecosystem, which process large data-sets in distributed computing environment. The HDFS is a filesystem of Hadoop, which process data blocks to the cluster nodes. The data block placement has become a bottleneck to overall performance in a Hadoop cluster. The current placement policy assumes that, all Datanodes have equal computing capacity to process data blocks. This computing capacity includes availability of same storage media and same processing performances of a node. As a result, Hadoop cluster performance gets effected with unbalanced workloads, inefficient storage-tier, network traffic congestion and HDFS integrity issues. This paper proposes a storage-tier-aware Robust Data Placement (RDP) scheme, which systematically resolves unbalanced workloads, reduces network congestion to an optimal state, utilizes storage-tier in a useful manner and minimizes the HDFS integrity issues. The experimental results show that the proposed approach reduced unbalanced workload issue to 72%. Moreover, the presented approach resolve storage-tier compatibility problem to 81% by predicting storage for block jobs and improved overall data block placement by 78% through pre-calculated computing capacity allocations and execution of map files over respective Namenode and Datanodes.