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Performance-Based Pricing in Multi-Core Geo-Distributed Cloud Computing

Drazen Lucanin, Ilia Pietri, Simon Holmbacka, Ivona Brandic, Johan Lilius, Rizos Sakellariou, Performance-Based Pricing in Multi-Core Geo-Distributed Cloud Computing. IEEE Transactions on Cloud Computing (99), 1–14, 2016.

http://dx.doi.org/10.1109/TCC.2016.2628368

Abstract:

New pricing policies are emerging where cloud providers charge resource provisioning based on the allocated CPU frequencies. As a result, resources are offered to users as combinations of different performance levels and prices which can be configured at runtime. With such new pricing schemes and the increasing energy costs in data centres, balancing energy savings with performance and revenue losses is a challenging problem for cloud providers. CPU frequency scaling can be used to reduce power dissipation, but also impacts VM performance and therefore revenue. In this paper, we firstly propose a non-linear power model that estimates power dissipation of a multi-core CPU physical machine (PM) and secondly a pricing model that adjusts the pricing based on the VM’s CPU-boundedness characteristics. Finally, we present a cloud controller that uses these models to allocate VMs and scale CPU frequencies of the PMs to achieve energy cost savings that exceed service revenue losses. We evaluate the proposed approach using simulations with realistic VM workloads, electricity price and temperature traces and estimate energy savings of up to 14:57%.

BibTeX entry:

@ARTICLE{jLuPiHoBrLiSa16a,
  title = {Performance-Based Pricing in Multi-Core Geo-Distributed Cloud Computing},
  author = {Lucanin, Drazen and Pietri, Ilia and Holmbacka, Simon and Brandic, Ivona and Lilius, Johan and Sakellariou, Rizos},
  journal = {IEEE Transactions on Cloud Computing},
  number = {99},
  publisher = {IEEE},
  pages = {1–14},
  year = {2016},
  ISSN = {2168-7161},
}

Belongs to TUCS Research Unit(s): Embedded Systems Laboratory (ESLAB)

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