• 품질경영학회지
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• 제39권3호
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• pp.412-421
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• 2011

In this paper, we introduce Quality of Service(QoS) analytic model of a distributed system that decentralizes the process nodes performing each task and communicates through a network for cooperation. The model advances a service reliability model of Dai et a1.(2003) by means of considering the processing time. The service is assumed to be provided by a centralized heterogeneous distributed system which is composed of some subsystems managed by a control center. The QoS is defined as the probability that a service is provided successfully in an allowed time, we consider the hardware/software reliability and the processing time which include program execution time, data transfer time. We derive the processing time distribution for a required service through convolution of corresponding probability density function. An application example is used to explain the procedure of computing quality of service.

• Advances in robotics research
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• 제1권2호
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• pp.171-183
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• 2014

Distributed planning and decision making can be beneficial from the robustness, adaptability and fault tolerance in multi-robot systems. Distributed mechanisms have not been employed in three dimensional transportation systems namely aerial and underwater environments. This paper presents a distributed cooperation mechanism on multi robot transportation problem in three dimensional environments. The cooperation mechanism is based on artificial capital market, a newly introduced market based negotiation protocol. In the proposed mechanism contributing in transportation task is defined as asset. Each robot is considered as an investor who decides if he is going to invest on some assets. The decision is made based on environmental constraint including fuel limitation and distances those are modeled as capital and cost. Simulations show effectiveness of the algorithm in terms of robustness, speed and adaptability.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2017년도 추계학술발표대회
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• pp.70-71
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• 2017

Volunteer Computing은 대규모 프로젝트를 많은 분산된 데스크탑, 랩탑 PC, 그리고 스마트 폰과 같은 모바일 디바이스의 idle(유휴 상태)인 컴퓨팅 자원을 기여(Volunteer)받아 수행하는 환경이다. 그러나 기여된 컴퓨팅 자원이 idle과 used(사용 상태) 간의 상태전이가 빈번히 일어나거나, idle로 지속되는 시간이 짧은 자원의 경우, 프로젝트를 수행하는데 비효율적이다. 따라서 본 논문에서는 이러한 자원을 구분하고, 해당 자원의 다음 상태전이확률테이블을 계산하여 결과에 따라 해당 자원에 적합한 작업을 할당하는 방법을 제안한다.

• 전자공학회논문지S
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• 제34S권8호
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• pp.56-64
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• 1997

In distributed autonomous robotic systems, each robot must behaves by itself according to the its states and environements, and if necessary, must cooperates with other orbots in order to carray out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforement learning having delayed reward ability and distributed genectic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the perfodrmance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper, we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2006년도 학술발표회 논문집
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• pp.563-570
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• 2006

Distributed control architecture is based on sharing control and data between multiple nodes on a network Communication and task sharing can be distributed between multiple control computers. Although many communication protocols exist, such as TCP/IP and UDP, they do not have the determinism that realtime control demands. Fiber-optic reflective shared memory creates the opportunity for realtime distributed control. This architecture allows control and computational tasks to be divided between multiple systems and operate in a deterministic realtime environment. One such shared memory architecture is based on Curtiss-Wright ScramNET family of fiber-optic reflective memory. MTS has built seismic and structural control software and hardware capable of utilizing ScramNET shared memory, opening up infinite possibilities in research and new capabilities in Hybrid and Model-In-The-Loop control.

• 한국물환경학회지
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• 제31권5호
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• pp.491-506
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• 2015

Distributed models represent watersheds using a network of numerous, uniform calculation units to provide spatially detailed and consistent evaluations across the watershed. However, these models have a disadvantage in general requiring a high computing cost. Semi-distributed models, on the other hand, delineate watersheds using a simplified network of non-uniform calculation units requiring a much lower computing cost than distributed models. Employing a simplified network of non-uniform units, however, semi-distributed models cannot but have limitations in spatially-consistent simulations of hydrogeochemical processes and are often not favoured for such a task as identifying critical source areas within a watershed. Aiming to overcome these shortcomings of both groups of models, a hybrid watershed model STREAM (Spatio-Temporal River-basin Ecohydrology Analysis Model) was developed in this study. Like a distributed model, STREAM divides a watershed into square grid cells of a same size each of which may have a different set of hydrogeochemical parameters reflecting the spatial heterogeneity. Like many semi-distributed models, STREAM groups individual cells of similar hydrogeochemical properties into representative cells for which real computations of the model are carried out. With this hybrid structure, STREAM requires a relatively small computational cost although it still keeps the critical advantage of distributed models.

• Structural Engineering and Mechanics
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• 제37권1호
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• pp.17-37
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• 2011

This paper presents a new Internet-based computational framework for the realistic simulation of multi-scale response of structural systems. Two levels of parallel processing are involved in this frame work: multiple local distributed computing environments connected by the Internet to form a cluster-to-cluster distributed computing environment. To utilize such a computing environment for a realistic simulation, the simulation task of a structural system has been separated into a simulation of a simplified global model in association with several detailed component models using various scales. These related multi-scale simulation tasks are distributed amongst clusters and connected to form a multi-level hierarchy. The Internet is used to coordinate geographically distributed simulation tasks. This paper also presents the development of a software framework that can support the multi-level hierarchical simulation approach, in a cluster-to-cluster distributed computing environment. The architectural design of the program also allows the integration of several multi-scale models to be clients and servers under a single platform. Such integration can combine geographically distributed computing resources to produce realistic simulations of structural systems.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2017년도 춘계학술대회
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• pp.103-105
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• 2017

하둡 맵리듀스(MapReduce)는 사용자가 요청한 잡을 하둡 클러스터에서 효과적으로 병렬 분산 처리하기 위한 프레임워크이다. 맵리듀스의 태스크 스케쥴러는 사용자의 잡 태스크들을 여러 노드에 할당하기 위한 기법이다. 하지만, 기존의 스케쥴러는 노드의 가용 상태에 따라 규모가 동적으로 변화하는 하둡 클러스터를 고려하지 않음으로써 클러스터의 자원을 충분히 활용하지 못하는 문제가 있다. 본 논문에서는 노드의 가용성을 고려하여 잡 태스크를 효과적으로 할당함으로써 하둡 클러스터의 활용성을 높이는 태스크 할당 정책을 제시한다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 춘계학술대회
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• pp.636-638
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• 2010

Task scheduling is an integral part of parallel and distributed computing. Extensive research has been conducted in this area leading to significant theoretical and practical results. Stochastic reward nets (SRN) is an extension of stochastic Petri nets and provides compact modeling facilities for system analysis. In this paper, we address task scheduling model using extended timed marked graph, which is a special case of SRNs. And we analyze this model by giving reward measures in SRN.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.327-347
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• 2024

Edge computing is frequently employed in the Internet of Vehicles, although the computation and communication capabilities of roadside units with edge servers are limited. As a result, to perform distributed machine learning on resource-limited MEC systems, resources have to be allocated sensibly. This paper presents an Improved MADDPG algorithm to overcome the current IoV concerns of high delay and limited offloading utility. Firstly, we employ the MADDPG algorithm for task offloading. Secondly, the edge server aggregates the updated model and modifies the aggregation model parameters to achieve optimal policy learning. Finally, the new approach is contrasted with current reinforcement learning techniques. The simulation results show that compared with MADDPG and MAA2C algorithms, our algorithm improves offloading utility by 2% and 9%, and reduces delay by 29.6%.