Data analytics applications are increasingly leveraging serverless execution environments for their ease-of-use and pay-as-you-go billing. The structure of such applications is usually composed of multiple functions that are chained together to form a workflow. The current approach of exchanging intermediate (ephemeral) data between functions is through a remote storage (such as S3), which introduces significant performance overhead.

SONIC is a data-passing manager that optimizes application performance and cost, by transparently selecting the optimal data-passing method for each edge of a serverless workflow DAG and implementing communication-aware function placement.

It has the following three distinctive features:

1. It applies to the emerging class of serverless applications,
2. It dynamically selects the best data passing options balancing latency and cost, and
3. It does not need any modification to the applications.

We evaluate Sonic on Amazon EC2 with three analytics applications, popular in the serverless environment. SONIC provides lower latency (raw performance) and higher performance/$ across diverse conditions, compared to four baselines: SAND, vanilla OpenLambda, OpenLambda with Pocket, and AWS Lambda.