• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.2003-2021
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• 2020

Today's computer systems are widely integrated with CPU and GPU to achieve considerable performance, but energy consumption of such system directly affects operational cost, maintainability and environmental problem, which has been aroused wide concern by researchers, computer architects, and developers. To cope with energy problem, we propose a task-scheduling framework to reduce energy under performance constraint by rationally allocating the tasks across the CPU and GPU. The framework first collects the estimated energy consumption of programs and performance information. Next, we use above information to formalize the scheduling problem as the 0-1 knapsack problem. Then, we elaborate our experiment on typical platform to verify proposed scheduling framework. The experimental results show that our proposed algorithm saves 14.97% energy compared with that of the time-oriented policy and yields 37.23% performance improvement than that of energy-oriented scheme on average.

• 전기학회논문지
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• 제68권3호
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• pp.405-415
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• 2019

The considerations of the electrical device' characteristics and the customers' satisfaction have been important criteria for efficient smart grid systems. In general, an electrical device is classified into a non-interruptible device or an interruptible device. The consideration of the type is an essential information for the efficient smart grid scheduling. In addition, customers' scheduling preferences or satisfactions have to be considered simultaneously. However, the existing research studies failed to consider both criteria. This paper proposes a new and efficient smart grid scheduling framework considering both criteria. The framework consists of two modules - 1) A day-head smart grid scheduling algorithm and 2) Real-time sudden demand negotiation framework. The first method generates the smart grid schedule efficiently using an embedded genetic algorithm with the consideration of the device's characteristics. Then, in case of sudden electrical demands, the second method generates the more efficient real-time smart grid schedules considering both criteria. In order to show the effectiveness of the proposed framework, comparisons with the existing relevant research studies are provided under various electricity demand scenarios.

• Industrial Engineering and Management Systems
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• 제11권1호
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• pp.94-102
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• 2012

In this paper, an interactive planning and scheduling framework are proposed for optimising operations from pits to crushers in ore mining industry. Series of theoretical and practical operations research techniques are investigated to improve the overall efficiency of mining systems due to the facts that mining managers need to tackle optimisation problems within different horizons and with different levels of detail. Under this framework, mine design planning, mine production sequencing and mine transportation scheduling models are integrated and interacted within a whole optimisation system. The proposed integrated framework could be used by mining industry for reducing equipment costs, improving the production efficiency and maximising the net present value.

• Journal of Electrical Engineering and Technology
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• 제12권5호
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• pp.1709-1718
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• 2017

This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The prime objective of the DASO is the minimization of the daily production cost in power systems with high penetration scenarios of variable generation. Furthermore, energy storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO). DASO simulation results are used as the base starting-point values in the hour-ahead online rolling RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid flexibility, to balance out the deviation between predicted and actual net load demand values. Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind forecasting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.5805-5825
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• 2019

Smartphones are important platforms because of their sophisticated computation, communication, and sensing capabilities, which enable a variety of applications in the Internet of Things (IoT) systems. Moreover, advancements in hardware have enabled sensors on smartphones such as environmental and chemical sensors that make sensor data collection readily accessible for a wide range of applications. However, dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users that vary throughout the day, which greatly affects the efficacy of sensor data collection. Therefore, it is necessary to consider phone users mobility patterns to design data collection schedules that can reduce the loss of sensor data. In this paper, we propose a mobility-based weighted adaptive opportunistic scheduling framework that can adaptively adjust to the dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users and provide prioritized scheduling based on various application scenarios, such as velocity, region of interest, and sensor type. The performance of the proposed framework is compared with other scheduling frameworks in various heterogeneous smartphone user mobility scenarios. Simulation results show that the proposed scheduling improves the transmission rate by 8 percent and can also improve the collection of higher-priority sensor data compared with other scheduling approaches.

• 제어로봇시스템학회논문지
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• 제12권12호
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• pp.1215-1219
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• 2006

Scheduling plays an important role in the process management in terms of providing profit-maximizing operation sequence of multiple orders and estimating completion times of them. In order to takes its full potential, varying conditions should be properly reflected in computing the schedule. The adjustment of scheduling decisions has to be made frequently in response to the occurrence of variations. It is often challenging because their model has to be adjusted and their solutions have to be computed within short time period. This paper employs Model Predictive Control(MPC) principles for updating the process condition in the scheduling model. The solutions of the resulting problems considering variations are computed using parametric programming techniques. The key advantage of the proposed framework is that repetition of solving similar programming problems with decreasing dimensionis avoided and all potential schedules are obtained before the execution of the actual processes. Therefore, the proposed framework contributes to constructing a robust decision-support tool in the face of varying environment. An example is solved to illustrate the potential of the proposed framework with remarks on potential wide applications.

• 한국정보과학회논문지:시스템및이론
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• 제29권4호
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• pp.201-212
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• 2002

본 연구에서는 다양한 실시간 스케줄링 알고리즘들을 구현할 수 있는 재구성 가능한 스케줄러모델을 제안한다. 제안 모델은 기본적인 작업(job) 디스패처(dispatcher)와 소프트웨어 타이머를 제공하는 하위 계층의 스케줄링 틀(framework)과 이를 기반으로 응용에 적합한 특정 스케줄링 알고리즘을 구현하는 상위 계층의 태스크 스케줄러로 구성된다. 시스템 개발자는 상하 구성 요소간 정보 교환을 위한 커널 내부 인터페이스만 준수한다면, 커널 하부 메커니즘과는 독립적으로 새로운 스케줄링 알고리즘을 구현할 수 있다. 한번 구현된 태스크 스케줄러는 향후 새로운 시스템 구축시 재사용 가능하다. 실시간 리눅스 (Real-Time Linux) (5)에 제안된 스케줄링 틀을 구현한 후, 이를 기반으로 대표적인 실시간 스케줄링 알고리즘들을 시험적으로 구현하여 보았다. 이를 통해 다양한 스케줄링 알고리즘들을 하부의 복잡한 커널 메커니즘 수정 없이 독립적으로 개발할 수 있음을 확인하였다. 또한 실험을 통해 두 단계 분리된 구조를 가진 제안 모델의 스케줄링 오버 헤드가 하나로 통합된 기존 일체형 스케줄러와 큰 차이가 없음을 확인할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• 한국산업정보학회논문지
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• 제8권3호
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• pp.19-31
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• 2003

대규모 환경의 고성능 그리드 구현을 위해서는 기존 그리드 자원 스케줄링 파라다임이 갖 성능 확장성 측면의 제한성을 극복할 수 있는 새로운 자원 스케줄링 프레임워크가 요구된다. 본 연구에서는 자원 스케줄링 성능 최적화를 목표로 자원인자 그래프(Resource Parameter Graph), 자원인자 인덱스 트리(Resource Parameter Index Tree), 그리고 정적 자원 정보 리포지터리로 구성되는 자원인자 스케줄링 프레임워크를 제안한다. 자원인자 그래프는 자원간의 관계 및 자원의 계층적 구성을 나타낼 수 있는 자원표현기법이며 이러한 표현을 기술하기 위한 XML 기반 자원정보 및 자원요청 기술 스키마를 설계하였다. 또한 자원인자 인덱스 트리는 자원 스케줄링의 자원탐색 및 자원할당, 상태정보 공지 등의 알고리즘의 효율적인 지원을 위한 메모리 기반 인덱스의 데이터 구조이다. 본 논문에서는 이러한 자원인자 스케줄링 프레임워크의 구성 내용에 대하여 기술한다.

• 한국시뮬레이션학회논문지
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• 제19권3호
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• pp.45-54
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• 2010

반도체 제조공정에 내재된 복잡성은 작업일정(job scheduling) 문제를 해석적 방법으로는 풀기 어렵기 때문에 보통 시스템 파라미터의 변화에 대한 효과를 이산사건 모델링 시뮬레이션에 의존하여 왔다. 한편 장비 고장 등 예측 불가능한 사건들은 고정된 작업일정 기법을 사용할 경우 전체 공정의 효율을 악화시킨다. 따라서 이러한 불확실성에 대해 최적의 성능을 내기 위해서는 작업일정을 실시간으로 대처 변경하는 것이 필요하다. 본 논문은 반도체 제조 공정에 대해 시스템 제어관점의 접근방법을 적용하여 이 문제에 적응형 실시간 작업제어 틀을 제안하고, DEVS 모델링 시뮬레이션 환경을 기반으로 제안된 틀을 설계 구현하였다. 제안된 방법은 기존의 임기응변적인 소프트웨어적인 방법에 비추어볼 때 전체 시스템을 이해하기 쉬우면서도 또한 추가되는 작업제어 규칙도 쉽게 추가 적용할 수 있는 유연성을 장점으로 가지고 있다. 여러 가지 실험결과 제안된 적응형 실시간 작업제어 프레임워크는 고정 작업규칙 방법에 비해 훨씬 나은 결과를 보여주어 그 효용성을 입증하였다.