``Meta-Simulation Design and Analysis for Large Scale Networks'' David Bauer, Jr. Ph.D. Thesis, Department of Computer Science, Rensselaer Polytechnic Institute, December 2005.

ABSTRACT


Performance analysis techniques are fundamental to the process of network protocol design and operations. A variety of techniques have been used by researchers in different contexts: analytic models (eg: TCP models, web models, self-similar models, topology models), simulation platforms (eg: ns-2, SSFnet, GloMoSim, Genesis), prototyping platforms (eg: MIT Click Router toolkit~\cite{click}, XORP~\cite{xorp}), tools for systematic design-of-experiments and exploring parameter state spaces (eg: Recursive Random Search, STRESS~\cite{stress}), experimental emulation platforms (eg: Emulab), real-world overlay deployment platforms (eg: Planetlab), and real-world measurement and data-sets (eg: CAIDA~\cite{caida}, Rocketfuel~\cite{SMW}). The high-level motivation behind the use of these tools is simple: to gain varying degrees of qualitative and quantitative understanding of the behavior of the system-under-test. This high-level purpose translates into a number of specific lower-level objectives, such as: validation of protocol design and performance for a wide range of parameter values (parameter sensitivity), understanding of protocol stability and dynamics, and studying feature interactions between protocols. Broadly, we may summarize the objective as a quest for general invariant relationships between network parameters and protocol dynamics. To address these needs, we developed an experiment design platform that will allow us to empirically model and heuristically search for optimizing protocol response. In general the protocol response is a function of a large vector of parameters, i.e., is a response surface in a large-dimensional parameter space (perhaps tens of thousands or more dimensions). We build off recent work at Rensselaer on an efficient search algorithm (called Recursive Random Search) for large-dimensional parameter optimization, and empirical modeling of protocol performance characteristics especially in ``interesting'' regions of the parameter state space. The result of this work includes a unified search, empirical modeling and optimization framework with demonstrated ability to pose meaningful large-scale network design questions and provide ``good'' models rapidly.

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