Rate-based query optimization for streaming information sources
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Rethinking cost and performance of database systems
ACM SIGMOD Record
An evaluation of alternative architectures for transaction processing in the cloud
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
S4: Distributed Stream Computing Platform
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Elastic Stream Computing with Clouds
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
A performance study on operator-based stream processing systems
IISWC '11 Proceedings of the 2011 IEEE International Symposium on Workload Characterization
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In recent time due to the availability of cloud-based data streaming systems like Yahoo! S4 or Twitter Storm and virtually unlimited resources using a public cloud infrastructure it is possible to run stream processing tasks with a new dimension of computational complexity. However, the required resources in terms of CPU, memory, and network bandwidth differ depending on the use case and applied data streaming system. For the user of such a system this is directly visible in the monetary cost he has to spent for the used resources. Therefore, he would like to maximize the ratio between gained performance and his monetary cost. In our demonstration we present an approach to measure and estimate the monetary cost for data streaming systems. We present a general scheme to model monetary cost for any combination of a cloud-based data streaming system and a major public cloud provider. Our model can be used as a starting point for optimizing the ratio between monetary cost and performance of streaming systems in general.