Abstract:
The 'software aging', is characterized by progressive
performance degradation due to the exhaustion of
operating system resources. Software rejuvenation
should be planned and initiated in the face of the actual
system behavior which requires measurement, analysis
and prediction of system resource usage. In this paper,
we present availability analysis and prediction for
software aging in virtualized environment. The difficulty
is that it can be due to two or more resources
simultaneously involved in the service failure. So, we
decide to evaluate the use of powerful Machine
Learning algorithms to predict the time to crash (TTC)
of a system which suffers from software aging
phenomena. We also present time dependent software
rejuvenation (TDSR) policy that can be applied with
predictable data in virtualized environment. The
behavior of the system is represented through a
Stochastic Petri Net (SPN) model. Numerical analysis of
the system availability is carried out the SHARPE tool
simulation.
