• Title/Summary/Keyword: weighted adaptive scheduling

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A Study on the Delay Adaptive Traffic Scheduling for QoS of Traffic Type (트래픽 유형에 따른 QoS 보장을 위한 지연 적응적인 스케줄링에 관한 연구)

  • 이상호;오영환
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.25 no.12B
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    • pp.1988-1995
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    • 2000
  • ATM 망에서 제공되고 있는 음성, 영상, 데이터와 같은 다양한 서비스는 사용자의 만족도를 수용할 수 있어야 한다는 것을 전제조건으로 한다. 이러한 기본적인 요구사항을 충족시키기 위해서는 노드대 노드간의 자원관리와 오류제어 및 다양한 트래픽의 특성을 고려한 전송 순서의 결정에 해당하는 스케줄링 방법이 요구되어 진다. 본 논문에서는 이러한 기술 요소 중에서 트래픽 설정 단계에서 제공되는 트래픽 특성 및 QoS(Quality of Service) 정보를 바탕으로 교환 노드에서 발생되는 전달 지연 시간에 적응적인 스케줄링 방식을 제시하였다. 이 방식은 멀티미디어 서비스와 같이 혼합된 트래픽 특성을 갖는 구조에서 트래픽 구성비율에 따라 적용되는 지연 여유치를 매우 융통성 있고 효과적으로 조절할 수 있다. 성능분석을 위하여 기존의 스케줄링 방식인 WFQ (Weighted Fair Queueing) 방식과 제안한 스케줄링 방식의 수학적인 분석을 수행하였으며 이 두 방식의 결과식을 비교하여 교환노드에서의 평균 지연과 셀 처리에 관한 성능을 분석하였다. 그리고 수학적 분석에 대한 검증으로는 Simulation tool ARENA 3.0을 이용하여 제안한 알고리즘의 Worst case와 기존의 알고리즘의 성능을 비교하였다.

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A Spatial Adaptation Procedure for Determining Robust Dispatching Rule in Wafer Fabrication (공간적응절차를 통한 웨이퍼 가공 공정의 로버스트한 작업배정규칙 결정)

  • Baek, Dong-Hyun;Yoon, Wan-Chul;Park, Sang-Chan
    • Journal of Korean Institute of Industrial Engineers
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    • v.23 no.1
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    • pp.129-146
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    • 1997
  • In traditional approaches to scheduling problems, a single dispatching rule was used by all machines in a system. However, since the situation of each machine generally differs from those of other machines, it is reasonable to apply a different dispatching rule to each machine responding to its given situation. In this regard, we introduce the concept of spatial adaptation and examine its effectiveness by simulation. In the spatial adaptation, each machine in a system selects an appropriate dispatching rule in order to improve productivity while it strives to be in harmony with other machines. This study proposes an adaptive procedure which produces a reliable dispatching rule for each machine beginning with the bottleneck machine. The dispatching rule is composed of several criteria of which priorities are adaptively weighted. The weights are learned for each machine through systematic simulations. The simulations are conducted according to a Taguchi experimental design in order to find appropriate sets of criteria weights in an efficient and robust way in the context of environmental variations. The proposed method was evaluated in an application to a semiconductor wafer fabrication system. The method achieved reliable performance compared to traditional dispatching rules, and the performance quickly approached the peak after learning for only a few bottleneck machines.

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A study on the performance improvement of an adaptive, real-time traffic assignment scheduler using the TP coefficient (TP 계수를 이용한 적응적 실시간 트래픽 할당 스케듈러의 성능 향상에 관한 연구)

  • Park, Nho-Kyung;Jin, Hyun-Joon;Yun, Eui-Jung
    • Journal of Internet Computing and Services
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    • v.11 no.4
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    • pp.1-10
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    • 2010
  • As recent fusion industry and ubiquitous technology have grown fast, network contents, which require high load, are provided in various infrastructures and facilities such as u-city and smart phones. Therefore, it is anticipated that the playback quality of multimedia compared to network loads degrades dramatically due to the drastic increment of real-time reference of conventional high load contents (eg. multimedia data). In this paper, we improved the method of the traffic assignment based on MPP which elevated the playback quality of multimedia by assigning discriminately the possible traffic of MMS with TP coefficients. When the TP coefficient which combines content preference with media preference was applied to a real-time traffic assignment scheduler, the simulation results showed that the multimedia playback stream was assigned within the possible traffic of a server. The real-time scheduling algorithm was improved by using the TP coefficient that combines the time-dependent image contents and the weighted value of media preference. It was observed from the experiment that the loss of the possible traffic decreases to 3.91% and 3.88% for three and four clients respectively.