Abstract
While software rejuvenation is used to prevent severe software failures, existing researches generally choose the constant-value periodic policy through steady-state reliability optimization. Since the software reliability declines with the execution time, a steady policy either introduces extra overhead or could not guarantee the reliability constraints. So an adaptive mechanism is proposed to reconfigure the software rejuvenation in the runtime. The transient reliability analysis is used to choose an optimal rejuvenation policy which maintains the software reliability for a certain period of time. A dynamic time series is generated for the reconfiguration process and the optimal rejuvenation policy is re-calculated according to the reconfiguration intervals during the software execution. Experimental studies results show that as the execution time increases, the software reliability drops continuously and the optimal rejuvenation interval should be decreased to maintain the same reliability constraints. This mechanism guarantees the software reliability constraint by resetting the optimal rejuvenation policy dynamically according to a reconfiguration interval time series.
Highlights
The software is debugged and tested before deployment, unpredicted errors occurs at runtime due to the software aging, unnoticed faults and etc[1,2]
A lot of research has been conducted on this area, the optimal rejuvenation policy is often obtained through the steady state analysis and remains unchanged during the long-time software execution
During the specific execution time Δti, if no failures occur to the software, the new reconfiguration interval Δti+1 is calculated at ti through (8) and the procedure goes back to Step I with i=i+1, in which a new rejuvenation interval is optimized according to the new execution time ti+1
Summary
The software is debugged and tested before deployment, unpredicted errors occurs at runtime due to the software aging, unnoticed faults and etc[1,2]. A lot of research has been conducted on this area, the optimal rejuvenation policy is often obtained through the steady state analysis and remains unchanged during the long-time software execution. Dohi et al.[14] proposed to calculate optimal periodic software rejuvenation policies based on interval reliability criteria. They demonstrated the change of the rejuvenation interval according to the execution time increase, but did not explain how to update these policies dynamically. Based on the above researches, this paper aims to propose an adaptive mechanism to reconfigure the periodic software rejuvenation policy dynamically. Based on the Markov process model, the transient reliability of the software with periodic software rejuvenation policy is analyzed.
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