Runtime Adaptation of Data Stream Processing Systems: The State of the Art

Data Stream Processing (DSP) has emerged over the years as the reference paradigm for the analysis of continuous and fast information flows, which have often to be processed with low-latency requirements to extract insights and knowledge from raw data. Dealing with unbounded data flows, DSP applications are typically long-running and, thus, likely experience varying workloads and working conditions over time. To keep a consistent service level in face of such variability, a lot of effort has been spent studying strategies for run-time adaptation of DSP systems and applications. In this survey, we review the most relevant approaches from the literature, presenting a taxonomy to characterize the state of the art along several key dimensions. Our analysis allows us to identify current research trends as well as open challenges that will motivate further investigations in this field.