  Cloud computing accelerates design space exploration in logic synthesis, and
parameter tuning in physical design. However, deploying EDA jobs on the cloud
requires EDA teams to deeply understand the characteristics of their jobs in
cloud environments. Unfortunately, there has been little to no public
information on these characteristics. Thus, in this paper, we formulate the
problem of migrating EDA jobs to the cloud. First, we characterize the
performance of four main EDA applications, namely: synthesis, placement,
routing and static timing analysis. We show that different EDA jobs require
different machine configurations. Second, using observations from our
characterization, we propose a novel model based on Graph Convolutional
Networks to predict the total runtime of a given application on different
machine configurations. Our model achieves a prediction accuracy of 87%. Third,
we develop a new formulation for optimizing cloud deployments in order to
reduce deployment costs while meeting deadline constraints. We present a
pseudo-polynomial optimal solution using a multi-choice knapsack mapping that
reduces costs by 35.29%.
