Daedalus: Self-Adaptive Horizontal Autoscaling for Resource Efficiency of Distributed Stream Processing Systems

Author:

Pfister Benjamin J. J.1ORCID,Scheinert Dominik1ORCID,Geldenhuys Morgan K.1ORCID,Kao Odej1ORCID

Affiliation:

1. Technische Universität Berlin, Berlin, Germany

Publisher

ACM

Reference24 articles.

1. Maycon Viana Bordin, Dalvan Griebler, Gabriele Mencagli, Cláudio Fernando Resin Geyer, and Luiz Gustavo Fernandes. 2020. DSPBench: A Suite of Benchmark Applications for Distributed Data Stream Processing Systems. IEEE Access , Vol. 8 (2020).

2. Minimizing cost by reducing scaling operations in distributed stream processing

3. Paris Carbone, Asterios Katsifodimos, Stephan Ewen, Volker Markl, Seif Haridi, and Kostas Tzoumas. 2015. Apache flink: Stream and batch processing in a single engine. Data Engineering Bulletin (2015).

4. Valeria Cardellini, Francesco Lo Presti, Matteo Nardelli, and Gabriele Russo Russo. 2022. Run-time Adaptation of Data Stream Processing Systems: The State of the Art. ACM Computing Surveys (CSUR) (2022).

5. Tao Chen Rami Bahsoon Shuo Wang and Xin Yao. 2018. To Adapt or Not to Adapt?: Technical Debt and Learning Driven Self-Adaptation for Managing Runtime Performance. In ICPE. ACM.

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