• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.733-734
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• 2009

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.473-474
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• 2010

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4174-4181
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• 2010

This paper presents an adaptive scheduling algorithm for manufacturing processes with non-stationary rework probabilities. The adaptive scheduling scheme named by hybrid Q-learning algorithm is proposed in this paper making use of the non-stationary rework probability and coupling with artificial neural networks. The proposed algorithm is measured by mean tardiness and the extensive computational results show that the presented algorithm gives very efficient schedules superior to the existing dispatching algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2371-2377
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• 2010

As the substantial increment in program complexity and appearance of mega program, the programs need to be divided to small tasks with multiple partitions and performed with a priority based scheduling. And also, a program development has to be progressed according to diversify of development environment. For instance, there are some restrictions upon O/S environment such as embedded O/S or windows. Therefore, the adaptive scheduling technique which performs multiple task partitioning process, regardless environment or O/S, is suggested. In this study, In this study, the adaptive scheduling technique algorithm and its applied examples are described.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5243-5263
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• 2017

Data center networks provide critical bandwidth for the continuous growth of cloud computing, multimedia storage, data analysis and other businesses. The problem of low link bandwidth utilization in data center network is gradually addressed in more hot fields. However, the current scheduling strategies applied in data center network do not adapt to the real-time dynamic change of the traffic in the network. Thus, they fail to distribute resources due to the lack of intelligent management. In this paper, we present an improved adaptive traffic scheduling strategy utilizing the simulated annealing genetic algorithm (SAGA). Inspired by the idea of software defined network, when a flow arrives, our strategy changes the bandwidth demand dynamically to filter out the flow. Then, SAGA distributes the path for the flow by considering the scheduling of the different pods as well as the same pod. It is implemented through software defined network technology. Simulation results show that the bisection bandwidth of our strategy is higher than state-of-the-art mechanisms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.499-502
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• 2010

Because of the substantial increase in program complexity and appearance of mega program, the needs to devide the program into small task with multiple partitions, and perform a scheduling based on the priority is required. And also, a program can be developed on specific environment according to the diversify of development environment. for instance, there are some restrictions upon O/S environment such as Embedded or Windows. therefore, the adaptive scheduling technique which perform multiple task partitioning process regardless environment or O/S is suggested. In this study, Adaptive scheduling technique algorithm and its application to be described.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.349-358
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• 2010

Adaptability is an important issue in video streaming over mobile environments, since the clients may request videos with great differences in their workload. In this paper, we propose the issues in limited bandwidth scheduling for efficient MPEG-4 video stream transmission over a mobile or wireless network. In the phase of admission control, the amount of bandwidth allocated to serve a video request is the mean bandwidth requirement of its requested video. The dynamic allocation of bandwidth in the phase of scheduling depends on the playback buffer levels of the clients with an objective to make it more adaptive to the playback situation of individual clients. In the proposed RTA scheduling protocol, more bandwidth may be allocated temporarily to the client whose buffer level is low. By employing the buffer level based scheduling policy, this protocol attempts to maximize the real-time performance of individual playback while minimizing the impact of transient overloading. Extensive simulation experiments have been performed to investigate the performance characteristics of the RTA protocol as comparing with BSBA protocol. This RTA protocol shows the better performance by transferring more frames than BSBA protocol.Computer simulations reveals that the standard deviation of the bit rate error of the proposed scheme is 50% less than that of the conventional method.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.1
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• pp.57-57
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• 1988

This paper develops an adaptive scheduling policy for flexible manufacturing systems. The inductive learning methodology used for constructing this state-dependent scheduling policy provides and understanding of the relative importance of the various system parameters in determining the appropriate scheduling rule. Experimental studies indicated the superiority of the suggested approach over the alternative approach involving the repeated application of a single scheduling rule for randomly generated test problems as well as a real system, and under both stationary and nonstationary conditions. In particular, its relative performance improves further when there are frequent disruptions, and when disruptions are caused by the introduction of tiiight due date jobs, one of the most common surces of disruptions in most manufacturing systems.

• Korean Management Science Review
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• v.13 no.1
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• pp.57-70
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• 1996

This paper develops an adaptive scheduling policy for flexible manufacturing systems. The inductive learning methodology used for constructing this state-dependent scheduling policy provides and understanding of the relative importance of the various system parameters in determining the appropriate scheduling rule. Experimental studies indicated the superiority of the suggested approach over the alternative approach involving the repeated application of a single scheduling rule for randomly generated test problems as well as a real system, and under both stationary and nonstationary conditions. In particular, its relative performance improves further when there are frequent disruptions, and when disruptions are caused by the introduction of tiiight due date jobs, one of the most common surces of disruptions in most manufacturing systems.

• International Journal of Contents
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• v.8 no.4
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• pp.7-11
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• 2012

Cloud Computing can be viewed as a dynamically-scalable pool of resources. Virtualization is one of the key technologies enabling Cloud Computing functionalities. Virtual machines (VMs) scheduling and allocation is essential in Cloud Computing environment. In this paper, two dynamic VMs scheduling and allocating schemes are presented and compared. One dynamically on-demand allocates VMs while the other deploys optimal threshold to control the scheduling and allocating of VMs. The aim is to dynamically allocate the virtual resources among the Cloud Computing applications based on their load changes to improve resource utilization and reduce the user usage cost. The schemes are implemented by using SimPy, and the simulation results show that the proposed adaptive scheme with one threshold can be effectively applied in a Cloud Computing environment both performance-wise and cost-wise.