By Topic

An Adaptive Resource Allocation Scheme for Large-scale Distributed Simulation System

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Boukerche, A. ; Univ. of Ottawa, Ottawa ; Yunfeng Gu

The goal of this paper is to provide an optimal solution for data distribution management (DDM) in large-scale distributed simulations. Until now, all existing DDM approaches have tried to make DDM more efficient in different ways; however, none has been able to optimize performance. The main reason for this inability is that these approaches manipulate the data generated in a simulation without evaluating the size of it. We propose a novel resource allocation scheme, the adaptive resource allocation control scheme (ARAC). The ARAC scheme is designed to optimize resource allocations for local and distributed processing work at each federate according to the size of the simulation. Efficiency is achieved by applying the analysis results of a static probability model, which we call the matching model. Performance comparisons between the existing grid-based approaches and the new adaptive approach show that the new scheme is much more flexible in adapting to various simulation sizes and comes much closer to an optimal solution. The novelty of the ARAC scheme is that it is able to scale the size of a simulation and control the simulation itself by running it in the most appropriate mode to achieve the desired efficiency. As a final result, the optimum performance is best approached.

Published in:

Distributed Simulation and Real-Time Applications, 2007. DS-RT 2007. 11th IEEE International Symposium

Date of Conference:

22-26 Oct. 2007