• Journal of Computing Science and Engineering
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• v.7 no.1
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• pp.30-43
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• 2013

Multicore and multiprocessor systems with dynamic voltage scaling architectures are being used as one of the solutions to satisfy the growing needs of high performance applications with low power constraints. An important aspect that has propelled this solution is effective task/application scheduling and mapping algorithms for multiprocessor systems. This work proposes an energy aware, offline, probability-based unified scheduling and mapping algorithm for multiprocessor systems, to minimize the number of processors used, maximize the utilization of the processors, and optimize the energy consumption of the multiprocessor system. The proposed algorithm is implemented, simulated and evaluated with synthetic task graphs, and compared with classical scheduling algorithms for the number of processors required, utilization of processors, and energy consumed by the processors for execution of the application task graphs.

• Proceedings of the IEEK Conference
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• summer
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• pp.39-41
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• 2004

In this paper, I propose a thread scheduling policy for faster packet processing on the network processors with multithreaded multiprocessor architecture. To implement the proposed policy, I derived several basic parameters related to the thread scheduling and included a new parameter representing the packet contents and the features of the multithreaded architecture. Through the empirical study using a network processor, I proved the proposed scheduling ploicy provides better throughput and load balancing compared to the generally used thread scheduling policy.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.421-428
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• 2008

The DVS(Dynamic Voltage Scaling) technique is the method to reduce the dynamic energy consumption. As using slack times, it extends the execution time of the big load operations by changing the frequency and the voltage of variable voltage processors. Researches, that controlling the energy consumption of the processors and the data transmission among processors by controlling the bandwidth to reduce the energy consumption of the entire system, have been going on. Since operations in multiprocessor systems have the data dependency between processors, however, the DVS techniques devised for single processors are not suitable to improve the energy efficiency of multiprocessor systems. We propose the new scheduling algorithm based on DVS for increasing energy efficiency of multiprocessor systems. The proposed DVS algorithm can improve the energy efficiency of the entire system because it controls frequency and voltages having the data dependency among processors.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.6 s.336
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• pp.49-58
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• 2005

In this paper, we propose an optimal ILP scheduling algorithm for multiprocessor design on LDFG(Linear Data-Flow Graph) that can be represented by homogeneous synchronous data-flow. The proposed computation in this paper does not contain data-dependent, all scheduling decisions for such algorithms can be taken at compile time, only fully static overlapped schedules are considered. It means that all linear have the same schedule and the same processor assignment. In this paper, the resource-constrained problem is addressed, for the LDFG optimization for multiprocessor design problem formulating ILP solution available to provide optimal solution. The results show that the scheduling method is able to find good quality schedules in reasonable time.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.140.3-140
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• 2001

This paper analyzes a blocking that is due to shared resource in multiprocessor system. A proposed analysis for shared resource suggests a scalable and amendable scheduling method about task allocation. An equation of shared resource blocking is proposed by a throughput at common bus and a ratio of throughput during time period, it is included a parameter of tasks scheduling. Using this equation, a new guideline for task allocation of multiprocessor is presented. Finally, in proposed system a model simulations for the proposed blocking model is given by a deterministic ratio of shared resource.

• 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 Information and Communication Engineering
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• v.19 no.2
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• pp.351-356
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• 2015

Synthetic utilization on multiprocessor system is not considered periodic tasks, except scheduling methods for aperiodic tasks where one of the real-time 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 improved synthetic utilization that is presented in this paper. The new method shows that schedulability increases aperiodic server method.

• 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.