• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3046-3070
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• 2017

Nowadays, the utilization of multiprocessor environments has been increased due to the increase in time complexity of application programs and decrease in hardware costs. In such architectures during the compilation step, each program is decomposed into the smaller and maybe dependent segments so-called tasks. Precedence constraints, required execution times of the tasks, and communication costs among them are modeled using a directed acyclic graph (DAG) named task-graph. All the tasks in the task-graph must be assigned to a predefined number of processors in such a way that the precedence constraints are preserved, and the program's completion time is minimized, and this is an NP-hard problem from the time-complexity point of view. The results obtained by different approaches are dominated by two major factors; first, which order of tasks should be selected (sequence subproblem), and second, how the selected sequence should be assigned to the processors (assigning subproblem). In this paper, a hybrid proposed approach has been presented, in which two different artificial ant colonies cooperate to solve the multiprocessor task-scheduling problem; one colony to tackle the sequence subproblem, and another to cope with assigning subproblem. The utilization of background knowledge about the problem (different priority measurements of the tasks) has made the proposed approach very robust and efficient. 125 different task-graphs with various shape parameters such as size, communication-to-computation ratio and parallelism have been utilized for a comprehensive evaluation of the proposed approach, and the results show its superiority versus the other conventional methods from the performance point of view.

• Journal of KIISE
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• v.43 no.6
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• pp.613-620
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• 2016

This paper presents a MOHA(Many-Task Computing on Hadoop) framework which aims to effectively apply the Many-Task Computing(MTC) technologies originally developed for high-performance processing of many tasks, to the existing Big Data processing platform Hadoop. We present basic concepts, motivation, preliminary results of PoC based on distributed message queue, and future research directions of MOHA. MTC applications may have relatively low I/O requirements per task. However, a very large number of tasks should be efficiently processed with potentially heavy inter-communications based on files. Therefore, MTC applications can show another pattern of data-intensive workloads compared to existing Hadoop applications, typically based on relatively large data block sizes. Through an effective convergence of MTC and Big Data technologies, we can introduce a new MOHA framework which can support the large-scale scientific applications along with the Hadoop ecosystem, which is evolving into a multi-application platform.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.677-684
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• 2009

With various sensor network and ubiquitous technologies, we can extend interaction area from a virtual domain to physical space domain. This spatial interaction is differ in that traditional interaction is mainly processed by direct interaction with the computer machine which is a target machine or provides interaction tools and the spatial interaction is performed indirectly between users with smart interaction tools and many distributed components of space. So, this interaction gives methods to users to control whole manageable space components by registering and recognizing objects. Finally, this paper provides an effective spatial interaction method with template-based task mapping algorithm which is sorted by historical interaction data for support of users' intended task. And then, we analyze how much the system performance would be improved with the task mapping algorithm and conclude with an introduction of a GUI method to visualize results of spatial interaction.

• Journal of Information Processing Systems
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• v.7 no.2
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• pp.299-320
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• 2011

The overhead of processing fine-grain tasks on a grid induces the need for batch processing or task group deployment in order to minimise overall application turnaround time. When deciding the granularity of a batch, the processing requirements of each task should be considered as well as the utilisation constraints of the interconnecting network and the designated resources. However, the dynamic nature of a grid requires the batch size to be adaptable to the latest grid status. In this paper, we describe the policies and the specific techniques involved in the batch resizing process. We explain the nuts and bolts of these techniques in order to maximise the resulting benefits of batch processing. We conduct experiments to determine the nature of the policies and techniques in response to a real grid environment. The techniques are further investigated to highlight the important parameters for obtaining the appropriate task granularity for a grid resource.

• Journal of Information Technology Application
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• v.2 no.1
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• pp.139-160
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• 2000

This paper suggests a technique for evaluating cognitive process when a working group performs its group tasks. First, it review a theory of distributed cognition which provides a theoretical background for investigating group's cognitive process. Then, it presents a procedure for DGOMS(Distributed GOMS) evaluation which is an extension from GOMS. GOMS is an analytica evalutation technique that has been used at the individual level. DGOMS analyzes task completion time and compares workload among group members on the basis of each member's task execution time, communication time, and cognitive workload. DGOMS can be applied to a situation where a group of people are working together for a common goal using a technical subsystem such as information systems.

• The Journal of Society for e-Business Studies
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• v.1 no.1
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• pp.227-247
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• 1996

Presented in this paper is a control model for developing virtual factory simulator, which is being operated under the distributed environment. The control model consists of production activity plan and information flows. To incorporate elements of the characteristics of the distributed control system, we suggested a collaboration model. This model is working under the client/server architecture, and also designed for cooperative-distributed shop control(CDSC) system in order to exploit several advantages of client/server architecture. Collaboration among each agent(or client) is done through negotiation and task sharing. Based on a contract net model, the CDSC system has three kinds of agents-order agent, resource agent, and communication forwarding agent. Each agent performs shop scheduling and control through negotiation on contract net. No node in CDSC system can have authority over other node. A bidding scheme is employed far negotiation between order agent and resource agents. The CDSC system can support re-negotiation among resource agents and an algorithm for re-negotiation is also developed. Experimental results are shown to advocate the effectiveness of the CDSC system for CIM environments.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1424-1427
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• 1996

OTS(Operator Training System) requires computation for the systematic training in real-time. So we have developed a distributed operator training system that is composed of workstation based server and PC based user modules. Sever and OM(OTS Manager) modules are located in the workstation server and user modules are located in PCs. User modules have DCS-like user interfaces and transfer data with OM over the coaxial ethernet. This paper delineates a total system architecture and definition of data transferring between OM and User module. Having applied this system to a batch process, we could analyze operator's tasks.

• ETRI Journal
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• v.39 no.2
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• pp.202-212
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• 2017

With the advent of the Internet-of-Things paradigm, the amount of data production has grown exponentially and the user demand for responsive consumption of data has increased significantly. Herein, we present DART, a fast and lightweight stream processing framework for the IoT environment. Because the DART framework targets a geospatially distributed environment of heterogeneous devices, the framework provides (1) an end-user tool for device registration and application authoring, (2) automatic worker node monitoring and task allocations, and (3) runtime management of user applications with fault tolerance. To maximize performance, the DART framework adopts an actor model in which applications are segmented into microtasks and assigned to an actor following a single responsibility. To prove the feasibility of the proposed framework, we implemented the DART system. We also conducted experiments to show that the system can significantly reduce computing burdens and alleviate network load by utilizing the idle resources of intermediate edge devices.

• International Journal of Contents
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• v.2 no.1
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• pp.39-44
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• 2006

Load sharing is a critical resource in computer system. In sender-initiated load sharing algorithms, the sender continues to send unnecessary request messages for load transfer until a receiver is found while the system load is heavy. Meanwhile, in the receiver initiated load sharing algorithms, the receiver continues to send an unnecessary request message for load acquisition until a sender is found while the system load is light. These unnecessary request messages result in inefficient communications, low CPU utilization, and low system throughput in distributed systems. To solve these problems, we propose a genetic algorithm based approach for improved sender-initiated and receiver-initiated load sharing in distributed systems. And we expand this algorithm to an adaptive load sharing algorithm. Compared with the conventional sender-initiated and receiver-initiated algorithms, the proposed algorithm decreases the response time and task processing time.

• Journal of information and communication convergence engineering
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• v.13 no.4
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• pp.248-256
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• 2015

With the dramatic increase in mobile traffic in recent years, some of the limitations of mobility management frameworks have magnified. The current centralized mobility management (CMM) strategy has various problems, such as a suboptimal routing path, low scalability, signaling overhead, and a single point of failure. To overcome these weaknesses in the CMM strategy, the Internet Engineering Task Force has been discussing distributed mobility management (DMM) strategies. The fundamental concept of a DMM strategy is to distribute the mobility anchors closer to the users. While the distribution of mobility anchors results in low-cost traffic delivery, it increases the signaling cost. To reduce this higher signaling cost, we propose a new DMM strategy applying the pointer forwarding technique. The proposed strategy keeps the existing tunnels and extends the traffic path as much as possible. In this paper, we analyze the performance of the pointer forwarding-DMM strategy and discuss its pros and cons.