CausalSim: MIT’s New Tool for Accurately Simulating Complex Systems — The system they developed |
A new technique eliminates a source of bias in a popular simulation method, which could enable scientists to create new algorithms that are more accurate and boost the performance of applications and networks. Credit: Jose-Luis Olivares/MIT
MIT researchers have developed a new method that eliminates this source of bias in trace-driven simulation. By enabling unbiased trace-driven simulations, the new technique could help researchers design better algorithms for a variety of applications, including improving video quality on the internet and increasing the performance of data processing systems.
He is joined on the paper by co-lead authors and fellow EECS graduate students Abdullah Alomar and Pouya Hamadanian; recent graduate student Anish Agarwal PhD ’21; and senior authors Mohammad Alizadeh, an associate professor of electrical engineering and computer science; and Devavrat Shah, the Andrew and Erna Viterbi Professor in EECS and a member of the Institute for Data, Systems, and Society and of the Laboratory for Information and Decision Systems.
But this is often a false assumption that results in biases about the behavior of new algorithms, making the simulation invalid, Alizadeh explains. “Our task is to disentangle these two effects, to try to understand what aspects of the behavior we are seeing are intrinsic to the system and how much of what we are observing is based on the actions that were taken. If we can disentangle these two effects, then we can do unbiased simulations,” he says.But researchers often cannot directly observe intrinsic properties. This is where the new tool, called CausalSim, comes in.
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