Software State Monitoring Model Studies Based on Multivariate HPM
Hardware Performance Monitor counters (HPM) are an emerging analysis technology in the area of software performance analysis. This paper proposes a method of software state monitoring based on HPM from the perspective of software fault diagnosis. Compared with traditional methods, the method does not depend on test case and expected result, and it can detect abnormal behavior in realtime based on software performance data. By the use of Performance API (PAPI), the method can gather CPU performance data. These data are recorded in HPM and can reflect software state at the running time of software. With Hidden- Markov Model (HMM), the method can learn prior probability of software state and conditional probability of performance data readings in each interval. Finally, based on the above parameters, the method classifies the follow-up multivariate observations by Naive Bayesian classifier (NBC) so as to monitor software state in real-time. The experiment shows that based on predefined monitoring event set, our method can effectively identify abnormal behavior which may occur in the running time of software.
HPM PAPI Hidden Markov Model Naive Bayesian Classifier State Monitoring
Kefei Cheng Jun Feng Kewen Pan Mingguo Li
College of Computer Science, Chongqing University of Posts and Telecommunications Chongqing China
国际会议
重庆
英文
441-445
2011-08-20（万方平台首次上网日期，不代表论文的发表时间）