• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.12
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• pp.96-104
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• 2009

In the real system environments, the performance of the application is not linearly proportional to the clock frequency of the microprocessor, in contrast to the general assumption of conventional dynamic voltage scaling. In this paper, we analytically model the relation between the performance of the application and the clock frequency of the microprocessor, and introduce the energy-optimal scheduling algorithm for a task set with distinct application characteristics. In addition, we present a theorem for the energy-optimal scheduling, which the derivative of the energy consumption with respect to the execution time should be the same for all the tasks. The proposed scheduling algorithm always generates the energy-optimal scaling factor thanks to the theorem for energy-optimal scheduling. We achieved about 7% additional energy reduction in the experiments using synthetic task sets.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.1
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• pp.25-37
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• 2015

This paper suggests a decision tree based approach for flexible job shop scheduling with multiple process plans. The problem is to determine the operation/machine pairs and the sequence of the jobs assigned to each machine. Two decision tree based scheduling mechanisms are developed for static and dynamic flexible job shops. In the static case, all jobs are given in advance and the decision tree is used to select a priority dispatching rule to process all the jobs. Also, in the dynamic case, the jobs arrive over time and the decision tree, updated regularly, is used to select a priority rule in real-time according to a rescheduling strategy. The two decision tree based mechanisms were applied to a flexible job shop case with reconfigurable manufacturing cells and a conventional job shop, and the results are reported for various system performance measures.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1B
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• pp.11-17
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• 2003

In this paper, we presented the dynamic random access channel allocation method under the priority based scheduling policy in order to improve the system performance of HIPERLAN/2 standardized by ETSI According to the scheduling policy, AP scheduler primarily allocates the resource to the collision MT This scheduling policy bring about decreasing the transmission delay of collision MT Dynamic RCH(random access channel) allocation method decreases the collision probability by increasing the number of RCH slots in case of low traffic. While it increases the maximum throughput by increasing the number of the data transmission slots in case of high traffic Therefore dynamic allocation method of RCH slots decreases the scheduling delay and increases the throughput When we evaluate the performance of presented method based on standards, we saw that the presented method improve the performance of the MAC protocol in terms of throughput and transmission delay.

• 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.

• 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.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1481-1494
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• 1997

In hard real-time systems, a timing fault may yield catastrophic results. Dynamic scheduling provides the flexibility to compensate for unexpected events at runtime; however, scheduling overhead at runtime is relatively large, constraining both the accuracy of the timing and the complexity of the scheduling analysis. In contrast, static scheduling need not have any runtime overhead. Thus, it has the potential to guarantee the precise time at which each instruction implementing a control action will execute. This paper presents a new approach to the problem of analyzing high-level language code, augmented by arbitrary before and after timing constraints, to provide a valid static schedule. Our technique is based on instruction-level complier code scheduling and timing analysis, and can ensure the timing of control operations to within a single instruction clock cycle. Because the search space for a valid static schedule is very large, a novel adaptive genetic search algorithm was developed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.615-619
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• 2004

This paper considers a variation of customer order scheduling problems. The variation is the case where machine-job assignment is fixed, and the objective is to minimize the sum of the completion times of the batches. In customer order scheduling problems, jobs are dispatched in batches. While a machine can process only one job at a time, multiple machines can simultaneously process jobs in a batch. We first establish a couple of lower bounds. Then, we develop a dynamic programming (DP) algorithm that runs in exponential time on the number of batches when there exist two machines. For the same problem with arbitrary number of machines, we present two simple heuristics, which use simple scheduling rules such as shortest batch first and shortest makespan batch first rules. Finally, we empirically evaluate the heuristics.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.3
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• pp.113-121
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• 2014

This paper considers a scheduling problem in a two-machine flowshop with outsourcing strategy incorporated. The jobs can be either processed in the first machine or outsourced to outside subcontractors. This paper wants to determine which jobs to be processed in-house and which jobs to be outsourced. If any job is decided to be outsourced, then an additional outsourcing cost is charged The objective of this paper is to minimize the sum of scheduling cost and outsourcing cost under a budget constraint. At first this paper characterizes some solution properties, and then it derives solution procedure including DP (Dynamic Programming) and B&B (Branch-and-Bound) algorithms and a greedy-type heuristic. Finally the performance of the algorithms are evaluated with some numerical tests.

• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.889-900
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• 2008

The problem of scheduling n jobs on a single machine is considered when the machine is subject to stochastic breakdowns. The objective is to minimize the weighted squared deviation of job completion times from a common due date. Two versions of the problem are addressed. In the first one the common due date is a given constant, whereas in the second one the common due date is a decision variable. In each case, a general form of deterministic equivalent of the stochastic scheduling problem is obtained when the counting process N(t) related to the machine uptimes is a Poisson process. It is proved that an optimal schedule must be V-shaped in terms of weighted processing time when the agreeable weight condition is satisfied. Based on the V-shape property, two dynamic programming algorithms are proposed to solve both versions of the problem.