ISSN : 1796-203X
Volume : 4    Issue : 8    Date : August 2009

Research on Running Time Behavior Analyzing and Trend Predicting of Modern Distributed Software
Junfeng Man, Zhicheng Wen, Changyun Li, and Xiangbing Wen
Page(s): 747-754
Full Text:
PDF (887 KB)

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.

Index Terms
modern distributed software systems, behavior trust, behavior analyzing, trend predicting, infinite hidden
Markov model