• Journal of Information Processing Systems
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• 제10권2호
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• pp.193-214
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• 2014

Load balancing is the major benefit of any distributed system. To facilitate this advantage, task duplication and migration methodologies are employed. As this paper deals with dependent tasks (DAG), we used duplication. Task duplication reduces the overall schedule length of DAG along-with load balancing. This paper proposes a new task duplication algorithm at the time of tasks assignment on various processors. With the intention of conducting proposed algorithm performance computation; simulation has been done on the Netbeans IDE. The mesh topology of a distributed system is simulated at this juncture. For task duplication, overall schedule length of DAG is the main parameter that decides the performance of a proposed duplication algorithm. After obtaining the results we compared our performance with arbitrary task assignment, CAWF and HEFT-TD algorithms. Additionally, we also compared the complexity of the proposed algorithm with the Duplication Based Bottom Up scheduling (DBUS) and Heterogeneous Earliest Finish Time with Task Duplication (HEFT-TD).

• Management Science and Financial Engineering
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• 제2권1호
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• pp.19-51
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• 1996

We suggest a distributed framework for task assignment in the computer-controlled shop floor where each of the resource agents and part agents acts like an independent profit maker. The job allocation problem is formulated as a linear programming problem. The LP formulation is analyzed to provide a rationale for the distributed task assignment procedure. We suggest an auction based negotiation procedure including a price-based bid construction and a price revising mechanism. The performance of the suggested procedure is compared with those of an LP formulation and conventional dispatching procedures by simulation experiments.

• Journal of Positioning, Navigation, and Timing
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• 제10권3호
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• pp.215-220
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• 2021

As the role of robots expands, flexible task planning methods are attracting attention from various domains. Many task planning frameworks are introduced to efficiently work in a wide range of areas. In order to work well in a broad region with multiple robots, various communication conditions should be controlled by task planning frameworks. However, few methods are proposed. In this paper, we propose mission planning methods according to the communication status of robots. The proposed method was verified through experiments assuming different communication states with a multi-robot system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.219-221
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• 1995

This paper proposes a model for simulation and performance evaluation of distributed computer systems(DCS). The model is composed of operating system(OS), resource, task, environment submodel. Task Flow Graph(TFG) is suggested to describe the relation between tasks. This paper considers task response time, the scheduler's ready queue length, utilization of each resource as performance indices. The distributed system of Continuous Annealing Line(CAL) in iron process is simulated with the proposed model.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.441-452
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• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3663-3679
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• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• Journal of information and communication convergence engineering
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• 제9권2호
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• pp.141-149
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• 2011

We consider the problem of assigning tasks to homogeneous nodes in the distributed system, so as to minimize the amount of communication, while balancing the processors' loads. This issue can be posed as the graph partitioning problem. Given an undirected graph G=(nodes, edges), where nodes represent task modules and edges represent communication, the goal is to divide n, the number of processors, as to balance the processors' loads, while minimizing the capacity of edges cut. Since these two optimization criteria conflict each other, one has to make a compromise between them according to the given task type. We propose a new cost function to evaluate static task assignments and a heuristic algorithm to solve the transformed problem, explicitly describing the tradeoff between the two goals. Simulation results show that our approach outperforms an existing representative approach for a variety of task and processing systems.

• 한국콘텐츠학회논문지
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• 제19권11호
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• pp.1-11
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• 2019

현대 경영환경에서 팀 과업의 증가에 따른 집단성과급의 도입이 일반화되는 추세이다. 선행연구에서 집단성과급의 효과는 과업의 상호의존성에 따라 달라질 수 있다는 논의가 진행되어 왔는데, 이를 실증적으로 분석한 연구는 드물고 결과가 일관적이지 않았다. 또한, 선행연구의 과업구조가 모두 상이했다. 본 연구의 목적은 과업의 상호의존성에 따라 집단 성과급 분배 방식이 수행에 미치는 효과를 검증하는 것이었다. 이를 위해 서울소재 대학 교내 게시판 및 홈페이지를 통하여 대학생 및 대학원생, 교직원 120명을 모집하였고, 120명을 대상으로 실험을 실시하였다. 본 연구에서 사용된 실험 과제는 정해진 양식에 따라 글자, 숫자 그리고 기호를 입력하는 것이었다. 본 연구의 독립변인은 과업의 상호의존성 정도(개인과업과 상호의존과업)와 성과급 분배방식(동등분배 집단 성과급과 차등분배 집단 성과급)이었으며, 종속변인은 정확하게 입력한 문자의 수였다. 실험 설계는 2 × 2 요인설계였으며, 각 집단에 30명씩 무작위로 할당되었다. 분석은 개인차의 영향력을 최소화하기 위해 공변량 분석을 실시하였으며, 전체 회기는 공변량 분석을 위한 사전회기 1회기, 실험회기 4회기 총 5회기였으며, 각 회기는 20분으로 구성되었다. 연구 결과, 개인과업에서 차등분배 성과급과 동등분배 성과급이 수행에 미치는 효과는 거의 동일하게 나타났으나, 상호의존 과업에서는 동등분배 성과급이 차등분배 성과급보다 더 효과적으로 수행을 향상시키는 것으로 나타났다. 이는 과업의 상호의존성에 따라 집단 성과급 효과가 달라 질 수 있음을 시사하는 결과이다.

• 디지털산업정보학회논문지
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• 제13권4호
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• pp.153-162
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• 2017

Advances in communication technologies and the decreasing cost of computers have made distributed computer systems an attractive alternative for satisfying the information needs of large organizations. This paper presents a distributed algorithm for performance improvement through load balancing and file migration in distributed systems. We employed a sender initiated strategy for task migration and used learning automata with several internal states for file migration. A task can be migrated according to the load information of a computer. A file is migrated to the destination processor when it is in the right boundary state. We also described an analytical model for load balancing with file migration to verify the proposed algorithm. Analytical and simulation results show that our algorithm is very well-suited for distributed system environments.

• 한국항공우주학회지
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• 제40권11호
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• pp.988-996
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• 2012

본 논문에서는 CBBA 알고리듬을 이용하여 SEAD 임무를 위한 이종무인기의 분산형 임무할당 알고리듬을 다룬다. SEAD 임무는 다수의 무인기를 다수의 대공 방어망 목표물에 할당 시키는 임무할당문제로 정의 할 수 있으며, 작전에 참여하는 무인기는 대공 방어망 파괴를 주목표 하는 위즐(weasel)과 주요 작전 및 전투 피해 평가를 수행하는 스트라이커(striker)로 구성된다. 본 논문에서는 최단경로생성 알고리듬과 CBBA 알고리듬을 이용하여 지형 장애물(terrain obstacle)이 있는 환경에서의 경로계획이 고려 된 이종 무인기의 분산형 임무할당 기법을 개발하고 SEAD 임무에 적용한다. 수치 시뮬레이션을 통하여 개발 된 기법의 성능과 적용가능성에 대해 검토한다.