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

TL;DR

This paper introduces a new energy-aware cloud management approach that dynamically adjusts VM placement and CPU frequencies based on performance and energy models, achieving up to 14.57% energy savings.

Contribution

It presents a novel non-linear power model and a performance-based pricing scheme for multi-core geo-distributed clouds, optimizing energy efficiency while maintaining revenue.

Findings

Energy savings of up to 14.57% achieved

Effective CPU frequency scaling based on VM characteristics

Dynamic VM placement improves energy efficiency

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 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%.