• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.52-63
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• 2009

The problem of scheduling simply periodic task systems upon a uniform multiprocessor is considered. Partitioning of periodic task systems requires solving the bin-packing problem, which is known to be intractable (NP-hard in the strong sense). This paper presents a global scheduling algorithm which transforms a given simply periodic task system into another using a "task-splitting" technique. Each transformed simply periodic task system is guaranteed to be successfully scheduled upon any uniform multiprocessor using a partitioned scheduling algorithm. It is proven that the proposed algorithm achieves the theoretical maximum utilization bound upon any uniform multiprocessor platform.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.119-126
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• 2006

The task scheduling in multiprocessor system is one of the key elements in the effective utilization of multiprocessor systems. The optimal assignment of tasks to multiprocessor is, in almost practical cases, an NP-hard problem. Consequently algorithms based on various modern heuristics have been proposed for practical reason. This paper proposes a new task scheduling algorithm using Genetic Algorithm which combines simulated annealing (GA+SA) in multiprocessor environment. In solution algorithms, the Genetic Algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The objective of proposed scheduling algorithm is to minimize makespan. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the result of proposed algorithm is better than that of any other algorithms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.4
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• pp.42-48
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• 2004

This paper concerns on a multiprocessor task scheduling problem with precedence relation, in which each task requires several processors simultaneously. Meta-heuristic generally finds a good solution if it starts from a good solution. In this paper, a tabu search is presented to find a schedule of minimal time to complete all tasks. A modified tabu search is also presented which uses a new initial solution based on the best solution during the previous run as the new starting solution for the next iteration. Numerical results show that a tabu search and a modified tabu search yield a better performance than the previous studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3046-3070
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• 2017

Nowadays, the utilization of multiprocessor environments has been increased due to the increase in time complexity of application programs and decrease in hardware costs. In such architectures during the compilation step, each program is decomposed into the smaller and maybe dependent segments so-called tasks. Precedence constraints, required execution times of the tasks, and communication costs among them are modeled using a directed acyclic graph (DAG) named task-graph. All the tasks in the task-graph must be assigned to a predefined number of processors in such a way that the precedence constraints are preserved, and the program's completion time is minimized, and this is an NP-hard problem from the time-complexity point of view. The results obtained by different approaches are dominated by two major factors; first, which order of tasks should be selected (sequence subproblem), and second, how the selected sequence should be assigned to the processors (assigning subproblem). In this paper, a hybrid proposed approach has been presented, in which two different artificial ant colonies cooperate to solve the multiprocessor task-scheduling problem; one colony to tackle the sequence subproblem, and another to cope with assigning subproblem. The utilization of background knowledge about the problem (different priority measurements of the tasks) has made the proposed approach very robust and efficient. 125 different task-graphs with various shape parameters such as size, communication-to-computation ratio and parallelism have been utilized for a comprehensive evaluation of the proposed approach, and the results show its superiority versus the other conventional methods from the performance point of view.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2050-2059
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• 1999

Multiprocessor scheduling problem has been an important research area for the past decades. The problem is defined as finding an optimal schedule which minimizes the parallel execution time of an application on a target multiprocessor system. Duplication Based Scheduling (DBS) is a relatively new approach for solving multiprocessor scheduling problems. This paper classifies DBS algorithms into two categories according to the task duplication method used. The paper then presents a new DBS algorithm that extracts the strong features of the two categories of DBS algorithms. The simulation study shows that the proposed algorithm achieves considerable performance improvement over existing DBS algorithms with similar time complexity.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.147-153
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• 2007

Task scheduling is an essential part of any computer system for allocating tasks to a processor of the system among various competitors. As we know, in real-time system, the failure of scheduling a hard real-time task my lead to disastrous consequence. Besides efficiency, resource and speed, real-time system has to take time constraint in serious consideration. This paper proposes a priority-driven scheduling algorithm for real-time multiprocessor system. which is called L-RE coordinates algorithm. L-RE coordinates is a new way of describing the task scheduling problem. In the algorithm, we take both deadline and laxity into consideration for allocating the priority. The simulation result shows that the new algorithm is viable and performance better than EDF and LLF algorithm on schedulability and context switch respectively.

• Journal of Internet Computing and Services
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• v.6 no.6
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• pp.99-106
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• 2005

The task scheduling in multiprocessor system Is one of the key elements in the effective utilization of multiprocessor systems. The optimal assignment of tasks to multiprocessor is, in almost all practical cases, an NP hard problem. Consequently various modern heuristics based algorithms have been proposed for practical reason. Recently, several approaches using Genetic Algorithm (GA) are proposed. However, these algorithms have only one objective such as minimizing cost and makespan. This paper proposes a new task scheduling algorithm using Genetic Algorithm combined simulated annealing (GA+SA) on multiprocessor environment. In solution algorithms, the Genetic Algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method. the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The objective of proposed scheduling algorithm is to minimize makespan and total number of processors used. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the results of proposed algorithm show better than that of other algorithms.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.2
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• pp.68-77
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• 2003

The Least-Laxity First(or LLF) scheduling algorithm assigns the highest priority to a task with the least laxity, and has been proved to be optimal for a uni-processor and sub-optimal for a multi-processor. However, this algorithm Is Impractical to implement because laxity tie results in the frequent context switches among tasks. In this paper, a Modified Least-Laxity First on Multiprocessor(or MLLF/MP) scheduling algorithm is proposed to solve this problem, i.e., laxity tie results in the excessive scheduling overheads. The MLLF/MP is based on the LLF, but allows the laxity inversion. MLLF/MP continues executing the current running task as far as other tasks do not miss their deadlines. Consequently, it avoids the frequent context switches. We prove that the MLLF/MP is also sub-optimal in multiprocessor systems. By simulation results, we show that the MLLF/MP has less scheduling overheads than LLF.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.4
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• pp.26-36
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• 2000

This paper proposes a neural network algorithm for real-time multiprocessor scheduling problem. The proposed algorithm is developed base on Hopfield neural network for a benefit of parallel processing, in order to finish a requested task within a deadline time. To compare the performance of the proposed algorithm, we used EDA and LLA algorithm that has studied real-time multiprocessor scheduling before. The proposed algorithm is implemented hardware using VHDL.

• The Journal of the Korea Contents Association
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• v.7 no.7
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• pp.39-49
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• 2007

Since the PF scheduling algorithm[13], which is optimal in the hard real-time multiprocessor environments, several scheduling algorithms have been proposed. All these algorithms assume the fixed unit quantum size, and this assumption has problems in the mode change environments. To settle the problem, we already proposed a method for deciding the optimal quantum size[2]. In this paper, we propose improved methods considering the task set whose utilization e is less than or equal to p/3+1. As far as the numbers of computations used to determine the optimal quantum size are concerned, newly proposed methods are proved to be more efficient than our previous ones.