In this tutorial we present the results of recent research about the cloud enablement of data streaming systems. We illustrate, based on both industrial as well as academic prototypes, new emerging uses cases and research trends. Specifically, we focus on novel approaches for (1) scalability and (2) fault tolerance in large scale distributed streaming systems. In general, new fault tolerance mechanisms strive to be more robust and at the same time introduce less overhead. Novel load balancing approaches focus on elastic scaling over hundreds of instances based on the data and query workload. Finally, we present open challenges for the next generation of cloud-based data stream processing engines. © 2014 Authors.
Cloud-based data stream processing / Thomas, Heinze; Aniello, Leonardo; Querzoni, Leonardo; Zbigniew, Jerzak. - (2014), pp. 238-245. (Intervento presentato al convegno 8th ACM International Conference on Distributed Event-Based Systems, DEBS 2014 tenutosi a Mumbai, India nel 26 May 2014 through 29 May 2014) [10.1145/2611286.2611309].
Cloud-based data stream processing
ANIELLO, LEONARDO;QUERZONI, Leonardo;
2014
Abstract
In this tutorial we present the results of recent research about the cloud enablement of data streaming systems. We illustrate, based on both industrial as well as academic prototypes, new emerging uses cases and research trends. Specifically, we focus on novel approaches for (1) scalability and (2) fault tolerance in large scale distributed streaming systems. In general, new fault tolerance mechanisms strive to be more robust and at the same time introduce less overhead. Novel load balancing approaches focus on elastic scaling over hundreds of instances based on the data and query workload. Finally, we present open challenges for the next generation of cloud-based data stream processing engines. © 2014 Authors.File | Dimensione | Formato | |
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