details for the publication
@inproceedings {Qin16,
author={Cui Qin},
title={Impact-minimizing Runtime Adaptation in Cloud-based Data Stream Processing},
booktitle={Proceedings of the PhD Symposium at the 5th European Conference on Service-Oriented and Cloud Computing (ESOCC '16)},
year={2016},
pages={32-39},
url={http://wcms.itz.uni-halle.de/download.php?down=43099\&elem=2988261},
abstract={Recently, cloud-based data stream processing has been rapidly emerged. In particular, it has become competitive in high scalability for processing huge amounts of data. During such processing, the actual characteristics of data streams may vary, e.g., in terms of volume or velocity. To provide a steady quality of the analysis results, runtime adaptation of the data processing is desirable. While several techniques for changing data stream processing at runtime do exist, one specific challenge is to minimize the impact of runtime adaptation on the data processing, in particular for real-time data analytics. In this research work, we aim at performing a runtime adaptation in cloud-based data stream processing, namely, dynamically switching alternative distributed algorithms which has similar functionality but operate at different characteristics. The goal of this work is to provide a generic approach which can automatically determine the algorithm switch with quality guarantees on the data processing. To minimize the impact on the data streams as well as optimize resource usage during adaptation, we combine stream re-routing with buffering and stream synchronization along with a support of dynamic deployment of alternative stream processing algorithms into the cloud.}
}
