This is the source code of our (Roman Heinrich, Carsten Binnig, Harald Kornmayer and Manisha Luthra) published paper at ICDE 2024: "COSTREAM: Learned Cost Models for Operator Placement in Edge-Cloud Environments"
In this work, we present COSTREAM, a novel learned cost model for Distributed Stream Processing Systems that provides accurate predictions of the execution costs of a streaming query in an edge-cloud environment. The cost model can be used to find an initial placement of operators across heterogeneous hardware, which is particularly important in these environments. In our evaluation, we demonstrate that COSTREAM can produce highly accurate cost estimates for the initial operator placement and even generalize to unseen placements, queries, and hardware. When using COSTREAM to optimize the placements of streaming operators, a median speed-up of around 21× can be achieved compared to baselines.
COSTREAM consists out of three separate packages. Each of these have a separate README.
- COSTREAM Management: Code for setting up distributed clusters and the collection of training data
- COSTREAM Plan Generation: Code for generate and execute DSPS queries with Apache Storm v.2.4.0
- COSTREAM Learning: Code for learning and inference of COSTREAM