Title :
On Predictability of System Anomalies in Real World
Author :
Tan, Yongmin ; Gu, Xiaohui
Author_Institution :
Dept. of Comput. Sci., North Carolina State Univ., Raleigh, NC, USA
Abstract :
As computer systems become increasingly complex, system anomalies have become major concerns in system management. In this paper, we present a comprehensive measurement study to quantify the predictability of different system anomalies. Online anomaly prediction allows the system to foresee impending anomalies so as to take proper actions to mitigate anomaly impact. Our anomaly prediction approach combines feature value prediction with statistical classification methods. We conduct extensive measurement study to investigate anomalous behavior of three systems in the real world: PlanetLab, SMART hard drive data, and IBM System S. We observe that real world system anomalies do exhibit predictability, which can be predicted with high accuracy and significant lead time.
Keywords :
Internet; pattern classification; statistical analysis; systems analysis; IBM system S; PlanetLab; SMART hard drive data; computer system; feature value prediction; online anomaly prediction; real world; statistical classification method; system anomalies; system management; Accuracy; Bayesian methods; Markov processes; Mathematical model; Measurement; Monitoring; Predictive models;
Conference_Titel :
Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), 2010 IEEE International Symposium on
Conference_Location :
Miami Beach, FL
Print_ISBN :
978-1-4244-8181-1
DOI :
10.1109/MASCOTS.2010.22