Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement

Virtualization is one of the main technologies used for improving resource efficiency in datacenters; it allows the deployment of co-existing computing environments over the same hardware infrastructure. However, the co-existing of environments - along with management inefficiencies - often creates...

Full description

Saved in:
Bibliographic Details
Main Authors: Moreno, Ismael Solis, Yang, Renyu, Xu, Jie, Wo, Tianyu
Format: Conference Proceeding
Language:English
Subjects:
Cloud computing
Computational modeling
Energy consumption
Energy efficiency
Google
Interference
performance interference
Resource management
Servers
virtual machine placement
workload heterogeneity
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Virtualization is one of the main technologies used for improving resource efficiency in datacenters; it allows the deployment of co-existing computing environments over the same hardware infrastructure. However, the co-existing of environments - along with management inefficiencies - often creates scenarios of high-competition for resources between running workloads, leading to performance degradation. This phenomenon is known as Performance Interference, and introduces a non-negligible overhead that affects both a datacenter's Quality of Service and its energy-efficiency. This paper introduces a novel approach to workload allocation that improves energy-efficiency in Cloud datacenters by taking into account their workload heterogeneity. We analyze the impact of performance interference on energy-efficiency using workload characteristics identified from a real Cloud environment, and develop a model that implements various decision-making techniques intelligently to select the best workload host according to its internal interference level. Our experimental results show reductions in interference by 27.5% and increased energy-efficiency up to 15% in contrast to current mechanisms for workload allocation.
DOI:10.1109/ISADS.2013.6513411