• 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 Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.369-381
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• 1999

Recently, the researches of real-time scheduling that manage flexibly system movement are being progressed by using simulation. But because the existing researches are focused on state change of whole system and selected dispatching with real-time, it could not reflect the state changes of each work center accurately, Therefore, the main core of this paper is to examine the state of workcenter, to select the optimal dispatching dynamically through simulation, and to present the algorithm that can manage actively on state of workcenter. Also, performance measure was used the mean tardiness and mean flow time that are used existing researches.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.1
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• pp.31-40
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• 2012

Distributed arrival time control (DATC) is a distributed feedback control algorithm for real-time scheduling problems in dynamic operational environment. Even though DATC has provided excellent performance for dynamic scheduling problems, it can be improved by considering the following considerations. First, the original DATC heavily depends on the quality of initial solution. In this paper, well-known dispatching rules are incorporated DATC algorithm to enhance its performance. Second, DATC improves its solution with adjusting virtual arrival times of jobs to be scheduled in proportion to the gap between completion time and due date iteratively. Since this approach assigns the same weight to all gaps generated with iterations, it fails to utilize significantly more the latest information (gap) than the previous ones. To overcome this issue we consider exponential smoothing which enable to assign different weight to different gaps. Using these two consideration This paper proposes A-DATC (Advanced-DATC). We demonstrate the effectiveness of the proposed scheduling algorithm through computational results.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.11
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• pp.611-621
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• 2002

For real-time systems, multiprocessor support is indispensable to handle the large number of requests. Existing real-time on-line scheduling algorithms such as Earliest Deadline First Algorithm (EDF) and Least Laxity Algorithm (LLA) may not be suitable for scheduling real-time tasks in multiprocessor systems. Although EDF has low context switching overhead, it suffers from "multiple processor anomalies." LLA has been shown as suboptimal, but has the potential for higher context switching overhead. Earliest Deadline Zero Laxity (EDZL) solved somewhat the problems of those algorithms, however is suboptimal for only two processors. Another algorithm EDA2 shows very good performance in overload phase, however, is not suboptimal for muitiprocessors. We propose two on-line scheduling algorithms, Earliest Deadline/Least Laxity (ED/LL) and ED2/LL. ED/LL is suboptimal for multiprocessors, and has low context switching overhead and low deadline miss rate in normal load phase. However, ED/LL is ineffective when the system is overloaded. To solve this problem, ED2/LL uses ED/LL or EDZL in normal load phase and uses EDA2 in overload phase. Experimental results show that ED2/LL achieves good performance in overload phase as wet] as in normal load phase.oad phase.

• Industrial Engineering and Management Systems
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• v.10 no.4
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• pp.247-254
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• 2011

In this paper, we study a single-machine scheduling problem with deteriorating processing time of jobs and multiple rate-modifying activities which reset deteriorated processing time to the original processing time. In this situation, the objective function is to minimize total completion time. First, we formulate an integer programming model. Since the model is difficult to solve as the size of real problem being very large, we design an improved genetic algorithm called adaptive genetic algorithm (AGA) with spontaneously adjusting crossover and mutation rate depending upon the status of current population. Finally, we conduct some computational experiments to evaluate the performance of AGA with the conventional GAs with various combinations of crossover and mutation rates.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.7-8
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• 2019

In this paper, we applied the migration technique to real-time tasks that have relatively low criticality but still important to be dropped by the mixed-criticality scheduling algorithms. The proposed drop and migrate algorithm analyzes the schedulability by calculating CPU utilization and response time of using task migration. We provide analysis to guarantee the deadline of LO-tasks, by transforming the response time equation specified with migration time. The transformed response time equation was able to analyze the migration schedulability. This algorithm can be used with various mixed-criticality schedulers as a supplementary method. We expect this algorithm will be used for scheduling LO-tasks such as communication task that requires safety guarantee especially in platooning and autonomous driving by utilizing the advantages of multiple node connectivities.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1580-1588
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• 1999

This paper evaluates the real-time performance of the Train Communication Network (TCN) that consists of WTB and MVB. A run-time scheduling algorithm for the hard-real time communication was proposed and its performance was evaluated. Also, a new addressing method and the adaptive tree algorithm were suggested to enhance performance. The overall performance was evaluated by computer simulation using Arena.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.135-140
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• 2020

Recently, due to the rapid diffusion of intelligent systems and IoT technologies, power saving techniques in real-time embedded systems has become important. In this paper, we propose P-GA (Parallel Genetic Algorithm), a scheduling algorithm aims at reducing the power consumption of real-time systems in multi-core hybrid memory environments. P-GA improves the Proportional-Fairness (PF) algorithm devised for multi-core environments by combining the dynamic voltage/frequency scaling of the processor with the nonvolatile memory technologies. Specifically, P-GA applies genetic algorithms for optimizing the voltage and frequency modes of processors and the memory types, thereby minimizing the power consumptions of the task set. Simulation experiments show that the power consumption of P-GA is reduced by 2.85 times compared to the conventional schemes.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1678-1685
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• 1999

In most of the existing real-time schedulers producing the total value as large as possible, the service times for all schedulable tasks are computed at each time a new task arrives. If all scheduled tasks would be executed completely before a new task arrives, the schedule may produce the greatest total value. But this is not always true in real situations. In many cases, (a) new tasks arrive(s) before all the scheduled tasks are executed completely. In this paper, we propose a unique scheduling algorithm for real-time tasks. The proposed algorithm determines the service times only for some tasks with earlier deadlines while the existing algorithms determine the service times for all tasks. This partial computation decreases the average scheduling complexity ramatically, even though, in the worst case, the complexity of the proposed algorithm becomes O(N2), which is equal to that of a previous algorithm that has been known as a less complicated one.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2760-2765
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• 2009

Pfair scheduling algorithm, which is an optimal scheduling algorithm in the hard real-time multiprocessor environments, is based on the fixed quantum size. Recently, several methods that determine the maximum quantum size dynamically were proposed in the mode change environments. But these methods considered the case in which the period of a task can only be decreased. In this paper, we consider the case in which the period of a task can be decreased or increased, and propose an improved method that determine the maximum quantum size dynamically in the mode change environments. A simulation shows that the proposed method is effective.