Quantifying Temporal and Spatial Correlation of Failure Events for Proactive Management

Authors:
Song Fu;Cheng-Zhong Xu
Affiliations:
Wayne State University, Detroit, MI 48202, USA;Wayne State University, Detroit, MI 48202, USA
Venue:
SRDS '07 Proceedings of the 26th IEEE International Symposium on Reliable Distributed Systems
Year:
2007

Citing 0
Cited 11

Exploring event correlation for failure prediction in coalitions of clusters

Proceedings of the 2007 ACM/IEEE conference on Supercomputing
An analysis of clustered failures on large supercomputing systems

Journal of Parallel and Distributed Computing
Failure-Aware Construction and Reconfiguration of Distributed Virtual Machines for High Availability Computing

CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
A survey of online failure prediction methods

ACM Computing Surveys (CSUR)
Failure-aware resource management for high-availability computing clusters with distributed virtual machines

Journal of Parallel and Distributed Computing
Towards pro-active adaptation with confidence: augmenting service monitoring with online testing

Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems
Quantifying event correlations for proactive failure management in networked computing systems

Journal of Parallel and Distributed Computing
Error log processing for accurate failure prediction

WASL'08 Proceedings of the First USENIX conference on Analysis of system logs
Online event correlations analysis in system logs of large-scale cluster systems

NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing
Randomized load balancing strategies with churn resilience in peer-to-peer networks

Journal of Network and Computer Applications
Dependable Grid Workflow Scheduling Based on Resource Availability

Journal of Grid Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Networked computing systems continue to grow in scale and in the complexity of their components and interactions. Component failures become norms instead of exceptions in these environments. Moreover, failure events exhibit strong correlations in time and space domain. In this paper, we develop a spherical covariance model with an adjustable timescale parameter to quantify the temporal correlation and a stochastic model to characterize spatial correlation. The models are further extended to take into account the information of application allocation to discover more correlations among failure instances. We cluster failure events based on their correlations and predict their future occurrences. Experimental results on a production coalition system, the Wayne State Grid, show the offline and online predictions by our predicting system can forecast 72.7% to 85.3% of the failure occurrences and capture failure correlations in cluster coalition environment.