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Distributed Complex Event Processing in Multiclouds

  • Vassilis StefanidisEmail author
  • Yiannis VerginadisEmail author
  • Ioannis PatiniotakisEmail author
  • Gregoris MentzasEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11116)

Abstract

The last few years, the generation of vast amounts of heterogeneous data with different velocity and veracity and the requirement to process them, has significantly challenged the computational capacity and efficiency of the modern infrastructural resources. The propagation of Big Data among different processing and storage architectures, has amplified the need for adequate and cost-efficient infrastructures to host them. An overabundance of cloud service offerings is currently available and is being rapidly adopted by small and medium enterprises based on its many benefits to traditional computing models. However, at the same time the Big Data computing requirements pose new research challenges that question the adoption of single cloud provider resources. Nowadays, we discuss the emerging data-intensive applications that necessitate the wide adoption of multicloud deployment models, in order to use all the advantages of cloud computing. A key tool for managing such multicloud applications and guarantying their quality of service, even in extreme scenarios of workload fluctuations, are adequate distributed monitoring mechanisms. In this work, we discuss a distributed complex event processing architecture that follows automatically the big data application deployment in order to efficiently monitor its health status and detect reconfiguration opportunities. This proposal is examined against an illustrative scenario and is preliminary evaluated for revealing its performance results.

Keywords

Distributed CEP Cloud monitoring Multiclouds Big data 

Notes

Acknowledgements

The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 731664. The authors would like to thank the partners of the MELODIC project (http://www.melodic.cloud/) for their valuable advices and comments.

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Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.Institute of Communications and Computer SystemsNational Technical University of AthensZografouGreece

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