Flexible and Scalable Storage Management for Data-Intensive Stream Processing
Data Stream Management Systems (DSMS) operate under strict performance requirements. Key to meeting such requirements is to efficiently handle time-critical tasks such as managing internal states of continuous query operators, traffic on the queues between operators, as well as providing storage support for shared computation and archived data. This paper introduces a general purpose storage management framework for DSMSs that performs these tasks based on a clean, loosely-coupled, and flexible system design that also facilitates performance optimization. An important contribution of the framework is that, in analogy to buffer management techniques in relational database systems, it uses information about the access patterns of streaming applications to tune and customize the performance of the storage manager.