• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.29-38
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• 2009

In the computing environment with heterogeneous resources, a job scheduling model is necessary for effective resource utilization and high-speed data processing. And, the job scheduling model has to cope with a dynamic change in the condition of resources. There have been lots of researches on resource estimation methods and heuristic algorithms about how to distribute and allocate jobs to heterogeneous resources. But, existing researches have a weakness for system compatibility and scalability because they do not support the standard language. Also, they are impossible to process jobs effectively and deal with a variety of computing situations in which the condition of resources is dynamically changed in real-time. In order to solve the problems of existing researches, this paper proposes a semantic computing-based dynamic job scheduling model that defines various knowledge-based rules for job scheduling methods adaptable to changes in resource condition and allocate a job to the best suited resource through inference. This paper also constructs a resource ontology to manage information about heterogeneous resources without difficulty as using the OWL, the standard ontology language established by W3C. Experimental results shows that the proposed scheduling model outperforms existing scheduling models, in terms of throughput, job loss, and turn around time.

• Journal of Satellite, Information and Communications
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• v.11 no.2
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• pp.55-59
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• 2016

Conventional RF energy harvesting systems can harvest energy and decode information from same source as an Hybirid-AP (H-AP). However, harvesting efficiency is seriously dependent on distance between users and H-AP. Therefore, in this paper, we proposed a transmission model for RF harvesting consisting of information and power source separately called Decoupled RF Energy harvesting networks. Main purpose of this paper is to maximize energy efficiency under various constraints of transmit power from H-AP and power beacon (PB), minimum quality of service and quality of harvested power of each users. To measure proposed model's performance, we proposed optimal time scheduling algorithms for energy efficiency (EE) maximization using Lagrangian dual decomposition theory that locally maximizes the EE by obtaining suboptimal values of three arguments : transmit power of H-AP, transmit power of PB, frame splitting factor. Experiment results show that the proposed energy-efficient algorithms converge within a few iterations with its optimality and greatly improve the EE compared to that of baseline schemes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9A
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• pp.1091-1100
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• 2004

In this paper, we present a reducing power conswnption of a scheduling for module selection under the time constraint. The proposed low power scheduling executes FDS_LP considering low power to exist the FDS scheduling by inputted the behavioral language. The proposed FDS_LP perfonns lower power consumption with dynamic power which is minimized the switching activity, based on force conception In the time step of module selection, an optimal RT(Register Transfer) library is composed by exploration of the parameters such as power, area, and delay. To find optimal parameters of RT library, an optimal module selection algorithm using Branch and Bound algorithm is also proposed. In the comparison and experimental results, The proposed FDS_LP algorithm reduce maximum power saving up to 23.9% comparing to previous FDS algorithm.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.251-252
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• 2019

본 논문에서는 기존의 AGV보다 높은 안전성과 Task Scheduling을 바탕으로 한 효율적인 AGV를 제안하였다. AGV는 객체인식 알고리즘인 YOLO로 다른 AGV를 인식하여 자동으로 피난처로 들어간다. 또한 마커인식 알고리즘인 ar_markers를 이용하여 그 위치가 적재소인지 생산 공정인지를 판단하여 각 마커마다 멈추고 피난처에 해당하는 Marker가 인식되고 다른 AGV가 인식되면 피난처로 들어가는 동작을 한다. 이 모든 로그는 Mobius를 이용해 Spring기반의 웹 홈페이지로 확인할 수 있으며, 작업스케줄 명령 또한 웹 홈페이지에서 내리게 된다. 위 작업스케줄은 외판원, 벨만-포드 알고리즘을 적용한 뒤 강화학습알고리즘 중 하나인 DQN을 이용해 최적 값을 도출해 내고 그 값을 DB에 저장해 AGV가 움직일 수 있도록 한다. 본 논문에서는 YOLO와 Marker 그리고 웹을 사용하는 AGV가 기존의 AGV에 비해 더욱 가볍고 큰 시설이 필요하지 않다는 점에서 우수함을 보인다.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.10
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• pp.221-230
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• 2020

Supercomputer that shares limited resources to multiple users needs a way to optimize the execution of application. For this, it is useful for system administrators to get prior information and hint about the applications to be executed. In most high-performance computing system operations, system administrators strive to increase system productivity by receiving information about execution duration and resource requirements from users when executing tasks. They are also using profiling techniques that generates the necessary information using statistics such as system usage to increase system utilization. In a previous study, we have proposed a scheduling optimization technique by developing a hardware performance counter-based profiling technique that enables characterization of applications without further understanding of the source code. In this paper, we constructed a profiling testbed cluster to support optimal execution of the supercomputer and experimented with the scalability of the profiling method to analyze application characteristics in the built cluster environment. Also, we experimented that the profiling method can be utilized in actual scheduling optimization with scalability even if the application class is reduced or the number of nodes for profiling is minimized. Even though the number of nodes used for profiling was reduced to 1/4, the execution time of the application increased by 1.08% compared to profiling using all nodes, and the scheduling optimization performance improved by up to 37% compared to sequential execution. In addition, profiling by reducing the size of the problem resulted in a quarter of the cost of collecting profiling data and a performance improvement of up to 35%.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.9_10
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• pp.417-420
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• 2008

We consider the problem of optimizing the performance of a system shared by selfish non-cooperative users. In this problem, small jobs which the users request should be scheduled on a set of shared machines with their speed functions, each of which dependson the amount of jobs allocated on a machine. The performance of the system is measured by the maximum of the completion times when the machines complete the jobs allocated on them. The selfish users can choose a machine on which their jobs are executed, and they choose the fastest machine. But it typically results in suboptimal system performance. The Price of Anarchy(PoA) was introduced as a measure of the performance degradation due to the user's selfish behavior. The PoA is the worst-case ratio of the cost of a Nash equilibrium to the optimal cost. In this paper, we estimate the PoA for the above scheduling problem.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.1-7
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• 2009

At a Internet service provider(ISP), field human resources visit many customers sites to provide multimedia services to customers in ubiquitous environment. Scheduling the resources is a hard problem because there are many tasks which have to be done by the resources and the number of the resources is not sufficient. To tackle this problem, ISPs had used team-based scheduling system. However, this aporoach was not sufficient to reduce the number of frequent changes of arrival time and could not increase operational efficiency. We also developed an optimal resource selection algorithm based on statistical information when an operator assigns a task to the resource. After we adopted this algorithm to the real world, the resources can do more tasks in a day.

• The Journal of Engineering Research
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• v.6 no.1
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• pp.5-14
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• 2004

Finding a solution to a constraint satisfaction problem requires choosing values for variables so as to satisfy the constraints of the problem. A constraint is simply a logical relation among several unknowns (or variables), each taking a value in a given domain. Conventional constraint-based scheduling systems are not enough to satisfy the all constraints because there are conflicts between them. In this paper, we propose a method for interactive scheduling, which have two major problems: first, it is hard to satisfy concretely all various kinds of constraints, and second, the search space is extremely large and requires a long execution time. To solve the above problems, we introduce the Interactive Scheduling System (ISS) that interacts with the user to exchange suggestions each other. Our system is performed in two steps: knowledge base generation of an initial assignment and iterative improvement with the user(host or internet client) to satisfy the constraints. Applying our system, we actually constructed timetables for our university, where the scheduling time is considerably reduced.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.367-372
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• 2008

최근 사용자들의 특성에 맞게 개인화되고 세분화된 위치 기반 서비스를 개발하기 위한 목적으로 이동 객체의 다양한 패턴들 중 의미있는 지식인 유용한 이동 패턴을 탐사하는 문제가 주요 이슈로 부각되고 있다. 이에 본 논문에서는 방대한 이동 객체의 이력 데이터 집합으로부터 특정 지점들 간의 최적 이동 경로나 정해진 시간내의 스케줄링 경로 탐색과 같이 복합적인 시간 및 공간 제약을 갖는 최적 이동 패턴을 탐사하는 문제에 대해 정의하고, 다양한 이동 패턴들 중 가장 빈발하게 발생하는 패턴이 최적의 비용을 소요할 것이라는 가정을 기반으로 최대 빈발 2-시퀀스를 추출하는 방법을 제안한다. 후보 시퀀스 집합으로부터 지지도 계산을 통해 추출되는 빈발 2-시퀀스들의 순차적인 조합은 패턴 탐사를 수행하는 각 패스 진행 시 후보 시퀀스 항목의 차수가 점차 감소하여 최적 이동 패턴 탐사 방법에 효과적으로 적용된다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.147-148
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• 2015

현재 발전기 스케줄링은 제한된 발전 자원, 에너지 수요 및 연료비용 증가, 그리고 불규칙적인 부하 등 최적리스케줄링에 있어 많은 문제들을 해결하기 위한 새로운 변화가 요구된다. 본 논문에서는 Multi-Terminal Voltage Source Converter High Voltage Direct Current (VSC-MTDC)와 Bettary Energy Storage System (BESS)가 결합된 Hybrid Integrated system (HIS)를 통해 발전기 리스케줄링을 위한 최적화 기법을 제안한다. 최적 발전기 리스케줄링을 위한 HIS 퍼포먼스를 위해 VSC station limit과 충 방전을 기반으로 하는 최적화 과정을 수행한다. 최적조류계산 문제는 VSC-MTDC 시스템과 BESS룰 고려하여 정식화되며, 충 방전 전략은 24시간 수요 정보를 사용하여 발전기 리스케줄링을 위한 하이브리드 통합 시스템의 최적알고리즘을 보여준다. 제안된 최적 리스케줄링 기법을 수정된 IEEE 14 모선에 적용하여 효율성을 입증하고자 한다.