  This paper proposes a learned cost estimation model for Distributed Stream
Processing Systems (DSPS) with an aim to provide accurate cost predictions of
executing queries. A major premise of this work is that the proposed learned
model can generalize to the dynamics of streaming workloads out-of-the-box.
This means a model once trained can accurately predict performance metrics such
as latency and throughput even if the characteristics of the data and workload
or the deployment of operators to hardware changes at runtime. That way, the
model can be used to solve tasks such as optimizing the placement of operators
to minimize the end-to-end latency of a streaming query or maximize its
throughput even under varying conditions. Our evaluation on a well-known DSPS,
Apache Storm, shows that the model can predict accurately for unseen workloads
and queries while generalizing across real-world benchmarks.
