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Quantifying Uncertainty for Preemptive Resource Provisioning in the Cloud

Marin Aranitasi, Benjamin Byholm, Mats Neovius, Quantifying Uncertainty for Preemptive Resource Provisioning in the Cloud. In: A Min Tjoa, Roland R. Wagner (Eds.), 28th International Workshop on Database and Expert Systems Applications (DEXA), 127–131, IEEE Computer Society, 2017.

http://dx.doi.org/10.1109/DEXA.2017.42

Abstract:

To satisfy quality of service requirements in a cost-efficient manner, cloud service providers would benefit from providing a means for quantifying the level of operational uncertainty within their systems. This uncertainty arises due to the dynamic nature of the cloud. Since tasks requiring various amounts of resources may enter and leave the system at any time, systems plagued by high volatility are challenging in preemptive resource provisioning. In this paper, we present a general method based on Dempster-Shafer theory that enables quantifying the level of operational uncertainty in an entire cloud system or parts thereof. In addition to the standard quality metrics, we propose monitoring of system calls to capture historical behavior of virtual machines as an input to the general method. Knowing the level of operational uncertainty enables greater accuracy in online resource provisioning by quantifying the volatility of the deployed system.

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BibTeX entry:

@INPROCEEDINGS{inpArByNe17a,
  title = {Quantifying Uncertainty for Preemptive Resource Provisioning in the Cloud},
  booktitle = {28th International Workshop on Database and Expert Systems Applications (DEXA)},
  author = {Aranitasi, Marin and Byholm, Benjamin and Neovius, Mats},
  editor = {Tjoa, A Min and Wagner, Roland R.},
  publisher = {IEEE Computer Society},
  pages = {127–131},
  year = {2017},
  keywords = {Cloud uncertainty, Resource provisioning, System calls},
  ISSN = {1529-4188},
}

Belongs to TUCS Research Unit(s): Distributed Systems Laboratory (DS Lab), Software Engineering Laboratory (SE Lab)

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