.png){kind=icon}
.png){kind=icon}
Return
Performance-Based Pricing in Multi-Core Geo-Distributed Cloud Computing
.png){kind=icon}
OA
AI
DOI：10.1109/TCC.2016.2628368.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 virtual machine (VM) performance and therefore revenue. In this paper, we first propose a non-linear power model that estimates power dissipation of a multi-core CPU physical machine (PM) and second 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 VM and scale CPU frequencies of the physical machine (PM) 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 percent.
Keywords:
Cloud computing
energy efficiency
geo-distributed clouds
electricity price
performance-based pricing
multi-core
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
5
Papers:
1.8K
Citations:
4.3K
Organization
View more organizations