• The Journal of the Korea Contents Association
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• v.6 no.9
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• pp.28-41
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• 2006

Recently, Baruah et.al. proposed an optimal Pfair scheduling algorithm in the hard real-time multiprocessor environments, and several variants of it were presented. All these algorithms assume the fixed unit quantum size, and this assumption has two problems in the mode change environments. If the quantum size is too large, it results in the scheduling failure due to the decreased processor utilization. If it is too small, it increases the frequency of scheduling points, and it incurs the task switching overheads. In this paper, we propose several methods that determine the maximum quantum size dynamically such that the task set can be scheduled in the mode change environments.

• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.309-319
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• 2000

The purpose of a parallel scheduling under a multiprocessor environment is to carry out the scheduling with the minimum synchronization overhead, and to perform load balance for a parallel application program. The processors calculate the chunk of iteration and are allocated to carry out the parallel iteration. At this time, it frequently accesses mutually exclusive global memory so that there are a lot of scheduling overhead and bottleneck imposed. And also, when the distribution of the parallel iteration in the allocated chunk to the processor is different, the different execution time of each chunk causes the load imbalance and badly affects the capability of the all scheduling. In the paper. we investigate the problems on the conventional algorithms in order to achieve the minimum scheduling overhead and load balance. we then present a new parallel loop scheduling algorithm, considering the locality of the data and processor affinity.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1741-1751
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• 2007

The imprecise computation technique ensures that all time-critical tasks produce their results before their deadlines by trading off the quality of the results for the computation time requirements of the tasks. In the imprecise computation, most scheduling problems of satisfying both 0/1 constraints and timing constraints, while the total error is minimized, are NP-complete when the optional tasks have arbitrary processing times. In the previous studies, the reasonable strategies of scheduling tasks with the 0/1 constraints on uniprocessors and multiprocessors for minimizing the total error are proposed. But, these algorithms are all off-line algorithms. Then, in the on-line scheduling, NORA(No Off-line tasks and on-line tasks Ready upon Arrival) algorithm can find a schedule with the minimum total error. In NORA algorithm, EDF(Earliest Deadline First) strategy is adopted in the scheduling of optional tasks. On the other hand, for the task system with 0/1 constraints, NORA algorithm may not suitable any more for minimizing total error of the imprecise tasks. Therefore, in this paper, an on-line algorithm is proposed to minimize total error for the imprecise real-time task system with 0/1 constraints. This algorithm is suitable for the imprecise on-line system with 0/1 constraints. Next, to evaluate performance of this algorithm, a series of experiments are done. As a consequence of the performance comparison, it has been concluded that IOSMTE(Imprecise On-line Scheduling to Minimize Total Error) algorithm proposed in this paper outperforms LOF(Longest Optional First) strategy and SOF(Shortest Optional First) strategy for the most cases.

• Journal of Computing Science and Engineering
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• v.8 no.1
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• pp.43-56
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• 2014

Semi-partitioned scheduling is a new approach for allocating tasks on multiprocessor platforms. By splitting some tasks between processors, semi-partitioned scheduling is used to improve processor utilization. In this paper, a new semi-partitioned scheduling algorithm called SS-DRM is proposed for multiprocessor platforms. The scheduling policy used in SS-DRM is based on the delayed rate monotonic algorithm, which is a modified version of the rate monotonic algorithm that can achieve higher processor utilization. This algorithm can safely schedule any system composed of two tasks with total utilization less than or equal to that on a single processor. First, it is formally proven that any task which is feasible under the rate monotonic algorithm will be feasible under the delayed rate monotonic algorithm as well. Then, the existing allocation method is extended to the delayed rate monotonic algorithm. After that, two improvements are proposed to achieve more processor utilization with the SS-DRM algorithm than with the rate monotonic algorithm. According to the simulation results, SS-DRM improves the scheduling performance compared with previous work in terms of processor utilization, the number of required processors, and the number of created subtasks.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.3
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• pp.322-333
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• 2012

AESA radar is able to instantaneously and adaptively position and control the beam, and such adaptive beam pointing of AESA radar enables to remarkably improve the multi-mission capability. For this reason, Radar Resource Management(RRM) becomes new challenging issue. RRM is a technique efficiently allocating finite resources, such as energy and time to each task in an optimal and intelligent way. Especially radar beam scheduling is the most critical component for the success of RRM. In this paper, we proposed a rule-based scheduling algorithm and Simulated Annealing(SA) based scheduling algorithm, which are alternatively selected and applied to beam scheduler according radar load status in real-time. The performance of the proposed algorithm was evaluated on the multi-function radar scenario. As a result, we showed that our proposed algorithm can process a lot of beams at the right time with real time capability, compared with applying only rule-based scheduling algorithm. Additionally, we showed that the proposed algorithm can save scheduling time remarkably, compared with applying only SA-based scheduling algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2277-2282
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• 2010

O(1) time synthetic utilization is not considered periodic tasks, except scheduling methods for aperiodic tasks where one of the aperiodic tasks is a scheduling method. But really aperiodic tasks scheduling method is composed of mixed task types. Aperiodic task scheduling method guarantee an analysis of the schedualibility of aperiodic task. The set of mixed tasks periodic and aperiodic tasks scheduling method uses synthetic utilization that is presented in this paper. The new method shows that schedulability increases 20% aperiodic server method.

• Journal of Information Processing Systems
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• v.5 no.4
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• pp.175-186
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• 2009

Power consumed by modern computer systems, particularly servers in data centers has almost reached an unacceptable level. However, their energy consumption is often not justifiable when their utilization is considered; that is, they tend to consume more energy than needed for their computing related jobs. Task scheduling in distributed computing systems (DCSs) can play a crucial role in increasing utilization; this will lead to the reduction in energy consumption. In this paper, we address the problem of scheduling precedence-constrained parallel applications in DCSs, and present two energy- conscious scheduling algorithms. Our scheduling algorithms adopt dynamic voltage and frequency scaling (DVFS) to minimize energy consumption. DVFS, as an efficient power management technology, has been increasingly integrated into many recent commodity processors. DVFS enables these processors to operate with different voltage supply levels at the expense of sacrificing clock frequencies. In the context of scheduling, this multiple voltage facility implies that there is a trade-off between the quality of schedules and energy consumption. Our algorithms effectively balance these two performance goals using a novel objective function and its variant, which take into account both goals; this claim is verified by the results obtained from our extensive comparative evaluation study.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.2
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• pp.129-139
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• 2016

This paper presented a robust ship scheduling model using the quadratic programming problem. Given a set of available carriers under control and a set of cargoes to be transported from origin to destination, a robust ship scheduling that can minimize the mean-variance objective function with the required level of profit can be modeled. Computational experiments concerning relevant maritime transportation problems are performed on randomly generated configurations of tanker scheduling in bulk trade. In the first stage, the optimal transportation problem to achieve maximum revenue is solved through the traditional set-packing model that includes all feasible schedules for each carrier. In the second stage, the robust ship scheduling problem is formulated as mentioned in the quadratic programming. Single index model is used to efficiently calculate the variance-covariance matrix of objective function. Significant results are reported to validate that the proposed model can be utilized in the decision problem of ship scheduling after considering robustness and the required level of profit.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.208-215
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• 1995

Numerous solution methods for scheduling problems such as part dispatching problem, operation sequence problem have been suggested as a means to be embedded in hierarchical or centralized shop floor control. Under the preceding control philosophies, however, response to changes in the shop floor status is quite slow and timely decision is sometimes impossible. Moreover, the control software becomes too large and it is almost impossible to modify the control software when the configuration of the shop floor changes. In agile manufacturing which emerged recently to cope with quick response and easy modifiability when unexpected changes occur, a new control policy is needed. CSCW[Computer Supported Cooperative Work] based shop floor control casts a different view on scheduling problems. Decisions are made locally when requested and useful information is scattered among agents for its efficient use. Adaptation is easy because agents are -'plug compatible or portable. In this paper, scheduling problems occurring under CSCW based shop floor control are identified and characterized. Traditional scheduling problems are reviewed from the CSCW viewpoint. All the control entities involved in the shop floor can be found and used to defined agents. With these entities and CSCW concept, possible scheduling problems are identified.