Adaptive Distributed Simulation for Computationally Intensive Modelling

The potential gain in speeding up the execution time of computationally intensive simulation in workstation clusters is tremendous. One of the challenges for exploring this gain is adaptation of the dynamic workload conditions and the heterogeneity of the workstation clusters. Static schemes of allo...

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Main Author: SHUM, Kam Hong
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Language:English
Published: Institutional Knowledge at Singapore Management University 1995
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Online Access:https://ink.library.smu.edu.sg/sis_research/1058
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spelling sg-smu-ink.sis_research-20572010-12-22T08:24:06Z Adaptive Distributed Simulation for Computationally Intensive Modelling SHUM, Kam Hong The potential gain in speeding up the execution time of computationally intensive simulation in workstation clusters is tremendous. One of the challenges for exploring this gain is adaptation of the dynamic workload conditions and the heterogeneity of the workstation clusters. Static schemes of allocating simulation workload to distributed workstations are inadequate because the assigned workload may change over time and the workstation usage may vary as a result of the workload of other users. An adaptive distributed simulation should also take account of the differences in workstation architectures and networking technologies among workstation clusters. Hence, we propose an adaptive scheme that integrates a local approach and a global approach of workload distribution to achieve automatic adaptation at runtime. In our experiments, the effectiveness of this scheme is demonstrated by the execution profile of a distributed simulation of a broadband communication network. We observe that... 1995-09-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/1058 info:doi/10.1007/978-1-4471-1007-1_20 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Databases and Information Systems
Numerical Analysis and Scientific Computing
SHUM, Kam Hong
Adaptive Distributed Simulation for Computationally Intensive Modelling
description The potential gain in speeding up the execution time of computationally intensive simulation in workstation clusters is tremendous. One of the challenges for exploring this gain is adaptation of the dynamic workload conditions and the heterogeneity of the workstation clusters. Static schemes of allocating simulation workload to distributed workstations are inadequate because the assigned workload may change over time and the workstation usage may vary as a result of the workload of other users. An adaptive distributed simulation should also take account of the differences in workstation architectures and networking technologies among workstation clusters. Hence, we propose an adaptive scheme that integrates a local approach and a global approach of workload distribution to achieve automatic adaptation at runtime. In our experiments, the effectiveness of this scheme is demonstrated by the execution profile of a distributed simulation of a broadband communication network. We observe that...
format text
author SHUM, Kam Hong
author_facet SHUM, Kam Hong
author_sort SHUM, Kam Hong
title Adaptive Distributed Simulation for Computationally Intensive Modelling
title_short Adaptive Distributed Simulation for Computationally Intensive Modelling
title_full Adaptive Distributed Simulation for Computationally Intensive Modelling
title_fullStr Adaptive Distributed Simulation for Computationally Intensive Modelling
title_full_unstemmed Adaptive Distributed Simulation for Computationally Intensive Modelling
title_sort adaptive distributed simulation for computationally intensive modelling
publisher Institutional Knowledge at Singapore Management University
publishDate 1995
url https://ink.library.smu.edu.sg/sis_research/1058
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