• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.747-752
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• 2004

Researches about scheduling of the distributed real-time systems have been proposed. However, they have some weak points, not scheduling both sporadic and periodic tasks and messages or being unable to guaranteeing the end-to-end constraints due to omitting precedence relations between sporadic tasks. So this paper proposes a new scheduling method for distributed real-time systems consisting of sporadic and periodic tasks with precedence relations and sporadic and periodic messages, guaranteeing end-to-end constraints. The proposed method is based on a binary search-based period assignment algorithm, an end-to-end laxity-based priority assignment algorithm, and three kinds of schedulability analysis, node, network, and end-to-end schedulability analysis. In addition, this paper describes the application model of sporadic tasks with precedence constraints in a distributed real-time system, shows that existing scheduling methods such as Rate Monotonic (RM) scheduling are not proper to be applied to the system having sporadic tasks with precedence constraints, and proposes an end-to-end laxity-based priority assignment algorithm.

• Journal of Industrial Technology
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• v.18
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• pp.139-148
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• 1998

Traditionally, the problems of manufacturing technology and manufacturing management have been treated independently. In this research, we endeavor to integrate the process planning and scheduling activities as an attempt to integrate the two realms. To draw up a plan of process planning and scheduling in real manufacturing environment is not an easy task because available time to plan could be limited and the shop status could change frequently. So we propose an architecture of integrated process planing and scheduling problem within the allowed time even if sheep situations change rather frequently. We argue that we can obtain a better and practical scheduling solution by dynamically changing the processing machines and operations as the shop condition changes. The proposed system takes the initial information for alternative machines and operations represented by an AND/OR graph as its input. Other informational inputs to the system are part order and shop statues. The system then generates new process plan and schedules during permitted time. Experimental results show that the proposed scheme provides a viable solution for real world scheduling problems.

• The Journal of the Korea Contents Association
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• v.6 no.9
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• pp.28-41
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• 2006

Recently, Baruah et.al. proposed an optimal Pfair scheduling algorithm in the hard real-time multiprocessor environments, and several variants of it were presented. All these algorithms assume the fixed unit quantum size, and this assumption has two problems in the mode change environments. If the quantum size is too large, it results in the scheduling failure due to the decreased processor utilization. If it is too small, it increases the frequency of scheduling points, and it incurs the task switching overheads. In this paper, we propose several methods that determine the maximum quantum size dynamically such that the task set can be scheduled in the mode change environments.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.1
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• pp.22-32
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• 2003

Many of multimedia applications in distributed environments generate the packets which have the real-time characteristics for continuous audio/video data and transmit them according to the teal-time task scheduling theories. In this paper, we model the traffic for continuous media in the distributed multimedia applications based on the high-bandwidth networks and introduce the PDMA algorithm which is the hard real-time task scheduling theory for guaranteeing QoS requested by the clients. Furthermore, we propose the admission control to control the new request not to interfere the current services for maintaining the high quality of services of the applications. Since the proposed admission control is sufficient for the PDMA algorithm, the PDMA algorithm is always able to find the feasible schedule for the set of messages which satisfies it. Therefore, if the set of messages including the new request to generate the new traffic. Otherwise, it rejects the new request. In final, we present the simulation results for showing that the scheduling with the proposed admission control is of practical use.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.9
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• pp.1544-1572
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• 2011

Task placement and scheduling are traditionally studied in following aspects: resource utilization, application throughput, application execution latency and starvation, and recently, the studies are more on application scalability and application performance. A methodology for task placement and scheduling centered on tasks based on virtual machines is studied in this paper to improve the performances of systems and dynamic adaptability in applications development and deployment oriented parallel computing. For parallel applications with no real-time constraints, we describe a thought of feature model and make a formal description for four layers of task placement and scheduling. To place the tasks to different layers of virtual computing systems, we take the performances of four layers as the goal function in the model of task placement and scheduling. Furthermore, we take the personal preference, the application scalability for a designer in his (her) development and deployment, as the constraint of this model. The workflow of task placement and scheduling based on virtual machines has been discussed. Then, an algorithm TPVM is designed to work out the optimal scheme of the model, and an algorithm TEVM completes the execution of tasks in four layers. The experiments have been performed to validate the effectiveness of time estimated method and the feasibility and rationality of algorithms. It is seen from the experiments that our algorithms are better than other four algorithms in performance. The results show that the methodology presented in this paper has guiding significance to improve the efficiency of virtual computing systems.

• The KIPS Transactions:PartA
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• v.18A no.4
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• pp.143-150
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• 2011

This paper presents an EDF based scheduling algorithm for scheduling imprecise computation model where each task consists of mandatory part and optional part. Imprecise computation is useful to manage overload condition. In overload situation, some optional parts should be removed. The proposed DOP algorithm removes optional parts of earlier deadline tasks to enhance flexibly for newly arriving tasks. A simulation result shows that DOP has better performance than other algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4A
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• pp.393-401
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• 2006

Energy consumption is an important design parameter for battery-operated embedded systems. Dynamic power management is one of the most well-known low-power design techniques. This paper proposes an online realtime scheduling algorithm, which we call energy-aware realtime scheduling using slack stealing (EARSS). The proposed algorithm gives the highest priority to the task with the largest degree of device overlap when the slack time exists. Scheduling result enables an efficient power management by reducing the number of state transitions. Experimental results show that the proposed algorithm can save the energy by 23% on average compared to the DPM-enabled system scheduled by the EDF algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.399-406
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• 2023

Cloud computing has been evolved to support edge computing architecture that combines fog management layer with edge servers. The main reason why it is received much attention is low communication latency for real-time IoT applications. At the same time, various cloud task scheduling techniques based on artificial intelligence have been proposed. Artificial intelligence-based cloud task scheduling techniques show better performance in comparison to existing methods, but it has relatively high scheduling time. In this paper, we propose a deep learning-based dynamic scheduling with multi-agents supporting scalability in edge computing environments. The proposed method shows low scheduling time than previous artificial intelligence-based scheduling techniques. To show the effectiveness of the proposed method, we compare the performance between previous and proposed methods in a scalable experimental environment. The results show that our method supports real-time IoT applications with low scheduling time, and shows better performance in terms of the number of completed cloud tasks in a scalable experimental environment.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.1
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• pp.62-68
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• 2005

This paper presents a scheme to enhance fixed priority scheduling algorithms on multiprocessors. This scheme gives the highest priority to jobs with zero laxity and schedules them Prior to other jobs. A fixed priority algorithm employing this scheme strictly dominates the original one; it can schedule all task sets schedulable by the fixed priority algorithm and some task sets not schedulable by the fixed priority algorithm. Simulation results show that the proposed scheme improves fixed priority algorithms in terms of the number of schedulable task sets and schedulable utilization bound.

• 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.