Hot-spot prediction and alleviation in distributed stream processing applications

Many emerging distributed applications require the real-time processing of large amounts of data that are being updated continuously. Distributed stream processing systems offer a scalable and efficient means of in-network processing of such data streams. However, the large scale and the distributed nature of such systems, as well as the fluctuation of their load render it difficult to ensure that distributed stream processing applications meet their Quality of Service demands. We describe a decentralized framework for proactively predicting and alleviating hot-spots in distributed stream processing applications in real-time. We base our hot-spot prediction techniques on statistical forecasting methods, while for hot-spot alleviation we employ a non-disruptive component migration protocol. The experimental evaluation of our techniques, implemented in our Synergy distributed stream processing middleware over PlanetLab, using a real stream processing application operating on real streaming data, demonstrates high prediction accuracy and substantial performance benefits.