Adaptive input admission and management for parallel stream processing

In this paper, we propose a framework for adaptive admission control and management of a large number of dynamic input streams in parallel stream processing engines. The framework takes as input any available information about input stream behaviors and the requirements of the query processing layer, and adaptively decides how to adjust the entry points of streams to the system. As the optimization decisions propagate early from input management layer to the query processing layer, the size of the cluster is minimized, the load balance is maintained, and latency bounds of queries are met in a more effective and timely manner. Declarative integration of external meta-data about data sources makes the system more robust and resource-efficient. Additionally, exploiting knowledge about queries moves data partitioning to the input management layer, where better load balance for query processing can be achieved. We implemented these techniques as a part of the Borealis stream processing system and conducted experiments showing the performance benefits of our framework.