An online failure prediction system for private IaaS platforms

Authors:
Pedro Capelastegui;Alvaro Navas;Francisco Huertas;Rodrigo Garcia-Carmona;Juan Carlos Dueñas
Affiliations:
Universidad Politécnica de Madrid (UPM);Universidad Politécnica de Madrid (UPM);Universidad Politécnica de Madrid (UPM);Universidad Politécnica de Madrid (UPM);Universidad Politécnica de Madrid (UPM)
Venue:
Proceedings of the 2nd International Workshop on Dependability Issues in Cloud Computing
Year:
2013

Citing 2
Cited 0

A survey of online failure prediction methods

ACM Computing Surveys (CSUR)
Online failure prediction in cloud datacenters by real-time message pattern learning

CLOUDCOM '12 Proceedings of the 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom)

Quantified Score

Hi-index	0.00

Visualization

Abstract

The size and complexity of cloud environments make them prone to failures. The traditional approach to achieve a high dependability for these systems relies on constant monitoring. However, this method is purely reactive. A more proactive approach is provided by online failure prediction (OFP) techniques. In this paper, we describe a OFP system for private IaaS platforms, currently under development, that combines different types of data input, including monitoring information, event logs, and failure data. In addition, this system operates at both the physical and virtual planes of the cloud, taking into account the relationships between nodes and failure propagation mechanisms that are unique to cloud environments.