Conference paper Open Access
SheerMP: Optimized Streaming Analytics-as-a-Service over Multi-site Multi-platform Settings
George Stamatakis;
Antonios Kontaxakis;
Alkis Simitsis;
Nikos Giatrakos;
Antonios Deligiannakis
Analytics are in the core of many emerging applications and can greatly benefit from the abundance of data and the progress in the processing capabilities of modern hardware. Still, new challenges arise with the extreme complexity of deciding how to execute analytics workflows given the plethora of choices of various cloud providers, the fragmented nature of diverse Big Data technologies, and the difficult task of resource provisioning to dynamically satisfy the demands of running streaming analytics over time. In this paper, we demonstrate a prototype system that optimizes streaming analytics workflows across Big Data platforms and computer clusters. Our system is the first that (i) considers a multi-user setup, (ii) examines the availability of multiple (potentially, geo-dispersed) compute choices, and (iii) provides a holistic framework covering a wide variety of practical optimization and adaptive resource allocation scenarios over a variety of streaming Big Data platforms
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edbt2022.pdf
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- Publication date:
- March 29, 2022
- DOI:
-
- Keyword(s):
- Big Data Optimization Data Streams Resource Allocation Analytics-as-a-Service Software Architectures Apache Flink Apache Spark Apache Kafka
- Grants:
-
European Commission:
- INFORE - Interactive Extreme-Scale Analytics and Forecasting (825070)
- Meeting:
- 25th International Conference on Extending Database Technology (EDBT), Online, 29 March - 01 April 2022
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International