Abstract

International audience; Stream Processing deals with the efficient, real-time processing of continuous streams of data. Stream Processing engines ease the development and deployment of such applications which are commonly pipelines of operators to be traversed by each data item. Due to the varying velocity of the streams, autoscaling is needed to dynamically adapt the number of instances of each operator. With the advent of geographically-dispersed computing platforms such as Fog platforms, operators are dispersed accordingly, and autoscaling needs to be decentralized as well. In this paper, we propose an algorithm allowing for scaling decisions to be taken and enforced in a fully-decentralized way. In particular, in spite of scaling actions being triggered concurrently, each operator maintains a view of its neighbours in the graph so as no data message is lost. The protocol is detailed and its correctness discussed. Its performance is captured through early simulation experiments.


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1007/978-3-030-48340-1_4 under the license http://www.springer.com/tdm
https://hal.inria.fr/hal-02171172,
https://hal.inria.fr/hal-02171172/document,
https://link.springer.com/chapter/10.1007/978-3-030-48340-1_4,
https://academic.microsoft.com/#/detail/2961527251
https://hal.inria.fr/hal-02171172/document,
https://hal.inria.fr/hal-02171172/file/autodasp2019.pdf
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Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1007/978-3-030-48340-1_4
Licence: Other

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