  Streaming applications are becoming widespread across an extensive range of
business domains as an increasing number of sources continuously produce data
that need to be processed and analysed in real time. Modern businesses are
aggressively using streaming data to generate valuable knowledge that can be
used to automate processes, help decision-making, optimize resource usage, and
ultimately generate revenue for the organization. Despite their increased
adoption and tangible benefits, support for the automated deployment and
management of streaming applications is yet to emerge. Although a plethora of
stream management systems have flooded the open source community in recent
years, all of the existing frameworks demand a considerably challenging and
lengthy effort from human operators to manually and continuously tune their
configuration and deployment environment in order to reach and maintain the
desired performance goals. To address these challenges, this article proposes a
vision for creating Deep Reinforcement Learning (DRL)-based methods for
transforming stream processing engines into self-managed serverless solutions.
This will lead to an increase in productivity as engineers can focus on the
actual development process, an increase in application performance potentially
leading to reduced response times and more accurate and meaningful results, and
a considerable decrease in operational costs for organizations.
