Performance estimation and scheduling of parallel computing programs in virtualized clusters ; 가상 클러스터에서 실행되는 병렬 컴퓨팅 프로그램의 성능 예측 및 배치 연구
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| Názov: | Performance estimation and scheduling of parallel computing programs in virtualized clusters ; 가상 클러스터에서 실행되는 병렬 컴퓨팅 프로그램의 성능 예측 및 배치 연구 |
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| Autori: | Han, Jaeung, 한재웅 |
| Prispievatelia: | Huh, Jaehyuk, 허재혁 |
| Informácie o vydavateľovi: | 한국과학기술원 |
| Rok vydania: | 2016 |
| Zbierka: | Korea Advanced Institute of Science and Technology: KOASAS - KAIST Open Access Self-Archiving System |
| Predmety: | Cloud Computing, Scientific Applications, Virtual Cluster configuration, performance modeling and prediction, interference, 클라우드 컴퓨팅, 과학응용프로그램, 가상 클러스터 설정, 성능 모형 및 예측, 간섭 현상 |
| Popis: | 학위논문(박사) - 한국과학기술원 : 전산학부, 2016.2 ,[vii, 77 p. :] ; With the advancement of cloud computing, there has been a growing interest in exploiting demand-based cloud resources for parallel scientific applications. To satisfy different needs for computing resources, cloud providers provide many different types of virtual machines (VMs) with various numbers of computing cores and amounts of memory. The cost and execution time of a scientific application vary depending on the types of VMs, number of VMs, and current status of the cloud due to interference among VMs. However, currently, cloud users are solely responsible for selecting the most effective VM configuration for their needs, but often end up with sub-optimal selections. In this dissertation, using molecular dynamics simulations as a case study, we propose a framework to guide users to select the optimal VM configurations that satisfy their equirements for scientific parallel computing in virtualized clusters. For molecular dynamics computation on a cluster of VMs, the guidance framework uses artificial neural networks which are trained to predict its execution times for various inputs, VM configurations, and status of interference among VMs. Using our performance prediction mechanisms, the guidance framework helps users choose an optimal or near-optimal VM cluster configuration under cost and runtime constraints. However, estimating the execution time with status of interference does not guarantee the optimal utilization of entire cloud. Each application have different pattern of shared resource and it causes the performance variabilty of co-running application. Despite many prior studies on interference in single-node systems, the interference behaviors of distributed applications have not been investigated thoroughly. In distributed parallel applications, a local interference in a node can affect the whole execution of the application spanning many nodes. This dissertation studies an interference modeling methodology for distributed applications to predict ... |
| Druh dokumentu: | doctoral or postdoctoral thesis |
| Jazyk: | English |
| Relation: | 648287; http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=648287&flag=dissertation; http://hdl.handle.net/10203/222425; 325007; 1474 |
| Dostupnosť: | http://hdl.handle.net/10203/222425 http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=648287&flag=dissertation |
| Prístupové číslo: | edsbas.D9C649A6 |
| Databáza: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | 학위논문(박사) - 한국과학기술원 : 전산학부, 2016.2 ,[vii, 77 p. :] ; With the advancement of cloud computing, there has been a growing interest in exploiting demand-based cloud resources for parallel scientific applications. To satisfy different needs for computing resources, cloud providers provide many different types of virtual machines (VMs) with various numbers of computing cores and amounts of memory. The cost and execution time of a scientific application vary depending on the types of VMs, number of VMs, and current status of the cloud due to interference among VMs. However, currently, cloud users are solely responsible for selecting the most effective VM configuration for their needs, but often end up with sub-optimal selections. In this dissertation, using molecular dynamics simulations as a case study, we propose a framework to guide users to select the optimal VM configurations that satisfy their equirements for scientific parallel computing in virtualized clusters. For molecular dynamics computation on a cluster of VMs, the guidance framework uses artificial neural networks which are trained to predict its execution times for various inputs, VM configurations, and status of interference among VMs. Using our performance prediction mechanisms, the guidance framework helps users choose an optimal or near-optimal VM cluster configuration under cost and runtime constraints. However, estimating the execution time with status of interference does not guarantee the optimal utilization of entire cloud. Each application have different pattern of shared resource and it causes the performance variabilty of co-running application. Despite many prior studies on interference in single-node systems, the interference behaviors of distributed applications have not been investigated thoroughly. In distributed parallel applications, a local interference in a node can affect the whole execution of the application spanning many nodes. This dissertation studies an interference modeling methodology for distributed applications to predict ... |
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