Research on Running Time Behavior Analyzing and Trend Predicting of Modern Distributed Software

Abstract

Interactive behavior trust of modern distributed software systems (MDSS) should be “monitored” and “grasped” at running time. The paper investigates the relationships between behaviors and their effects at running time in MDSS, uses statistical machine learning tools to analyze the laws of behavior traces, and presents a novel behavior analyzing and trend predicting method. We use hierarchical Dirichlet process and infinite hidden Markov model to converge monitored interface data to determine unknown events, and learn behavior patterns from event sequences including unknown events in terms of semisupervised method. As determining unknown events and behavior patterns, Beam sampling has higher efficiency in sampling and inference compared with other method (e.g., Gibbs sampling). When behavior patterns reach a certain scale, MDSS can analyze and predict interactive behaviors in terms of unsupervised method. We adopt Viterbi algorithm of hidden Markov model to analyze optimal sequences of interactive events, which help to determine good and evil of current behaviors. MDSS can send early warning for hostile behaviors, actively predict subsequent trends for non-hostile behaviors. Simulation experiments testify that the novel method has unique predominance in software behavior analyzing and trend predicting.