Accelerating the performance of distributed stream processing systems with in-network computing.

The performance of stream processing systems heavily relies on the ability to move data between stream processing operators efficiently. The softwarization of computer networks offers a huge potential for distributed systems to accelerate the performance of distributed stream processing operators by minimizing data movements and accelerating the execution of operators. Yet, using methods of in-network computing to accelerate middleware services like stream processing systems often conflict with the famous end-to-end principle. Therefore, in this talk, we will focus on abstractions that allow executing computations on heterogeneous resources of network elements and discuss how these abstractions can support stream processing systems. In particular, we highlight and introduce recent findings in distributed data stream processing, network function virtualization, and realtime packet streaming. We show how different paradigms and programming models support accelerating performance by better utilizing the capabilities of in-network computing elements. Moreover, we give an outlook on how future developments can change how distributed computing can be adaptively performed over networked infrastructures.