Stochastic simulation is an important aid for the design and performance engineering of computer networks. The credibility of simulative results can, however, be seriously affected by human errors (e.g., inconsistencies in the parameter selection, poor initialization of random generators, bugs in the scripts used for post-processing), which become more and more likely and numerous as the dimension of the set of simulated scenarios (simulation campaign) increases. The occurrence of such errors can be limited by using reliable automation tools, i.e. tools which take care of the above mentioned tasks by using state-of-the-art methodologies. This work describes ANSWER (Automated NS-2 Workflow managER), a simulation workflow automation tool for the Network Simulator (ns-2), explicitly designed for facilitating large-scale simulation campaigns, i.e. those involving many factors. Our framework reduces the space for errors when defining scenarios, controls the execution of a large number of scenarios, and reduces the time overhead required for output data analysis.

A Framework for Large-scale Simulations and Output Result Analysis with ns-2

ANDREOZZI, MATTEO MARIA;STEA, GIOVANNI;VALLATI, CARLO
2009-01-01

Abstract

Stochastic simulation is an important aid for the design and performance engineering of computer networks. The credibility of simulative results can, however, be seriously affected by human errors (e.g., inconsistencies in the parameter selection, poor initialization of random generators, bugs in the scripts used for post-processing), which become more and more likely and numerous as the dimension of the set of simulated scenarios (simulation campaign) increases. The occurrence of such errors can be limited by using reliable automation tools, i.e. tools which take care of the above mentioned tasks by using state-of-the-art methodologies. This work describes ANSWER (Automated NS-2 Workflow managER), a simulation workflow automation tool for the Network Simulator (ns-2), explicitly designed for facilitating large-scale simulation campaigns, i.e. those involving many factors. Our framework reduces the space for errors when defining scenarios, controls the execution of a large number of scenarios, and reduces the time overhead required for output data analysis.
2009
9789639799455
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/131539
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