Functions-as-a-Service (FaaS) is currently being marketed as the silver bullet of abstractions for developing scalable applications in the cloud. Although very popular, current FaaS offerings offer poor support for the management of application state: managing and keeping it consistent at large scale is very challenging. As a result, the serverless model is inadequate for executing stateful, consistent, and latency-sensitive applications. To this end, a new breed of systems and programming models are currently in the making, broadly termed as Stateful Functions as a Service (SFaaS). Surprisingly, recent results in both academia and industry point to a common pattern: stateful functions can be modelled as dataflow graphs and possibly handed over for execution by what we call nowadays a “stream processor”. In this talk, I will analyse the requirements of scalable cloud applications and how those requirements affect the design choices of a “universal execution engine” for the Cloud. I then try to answer the question: can parallel dataflow engines be the answer in the quest for the universal cloud execution engine? I will conclude with a set of requirements and directions for the future dataflow engines and what we have been up to in my research group at TU Delft.

Asterios Katsifodimos is an Associate Professor at the Delft University of Technology. Before that, Asterios worked at the SAP Innovation Center (Berlin), and at the Technical University (TU) of Berlin. Asterios obtained his PhD from INRIA Saclay & University Paris 11.

Asterios’ research work spans the areas of parallel (streaming-) data processing & Cloud computing, optimization of ML-systems, and data integration. His research on fault tolerance, aggregation methods and benchmarking has influenced the design of open-source stream processing engines, while his research group develops and maintains the dataset discovery system Valentine. Asterios has received the ACM SIGMOD Research Highlights Award in 2016, as well as the best paper award at EDBT 2019 and ACM DEBS 2021. He is the instructor of the online MOOC “Taming Massive Data Streams” and is invited regularly to give talks at industry and research venues. Asterios serves as an associate editor or a program committee member in the data management conferences such as VLDB, ICDE, SIGMOD and EDBT.