Optimal software rejuvenation for tolerating soft failures
Performance Evaluation
Analysis of Preventive Maintenance in Transactions Based Software Systems
IEEE Transactions on Computers
The O.D. E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
SIAM Journal on Control and Optimization
Actor-Critic--Type Learning Algorithms for Markov Decision Processes
SIAM Journal on Control and Optimization
Fine grained software degradation models for optimal rejuvenation policies
Performance Evaluation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Learning Algorithms for Markov Decision Processes with Average Cost
SIAM Journal on Control and Optimization
Monitoring Smoothly Degrading Systems for Increased Dependability
Empirical Software Engineering
Software Rejuvenation: Analysis, Module and Applications
FTCS '95 Proceedings of the Twenty-Fifth International Symposium on Fault-Tolerant Computing
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Proactive management of software aging
IBM Journal of Research and Development
Optimizing preventive service of software products
IBM Journal of Research and Development
On-line adaptive algorithms in autonomic restart control
ATC'10 Proceedings of the 7th international conference on Autonomic and trusted computing
A survey of software aging and rejuvenation studies
ACM Journal on Emerging Technologies in Computing Systems (JETC) - Special Issue on Reliability and Device Degradation in Emerging Technologies and Special Issue on WoSAR 2011
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Software rejuvenation is a preventive and proactive maintenance solution that is particularly useful for counteracting the phenomenon of software aging. In this paper we consider an operational software system with multiple degradations and derive the optimal software rejuvenation policy minimizing the expected operation cost per unit time in the steady state, via the dynamic programing approach. Especially, we develop a reinforcement learning algorithm to estimate the optimal rejuvenation schedule adaptively and examine its asymptotic properties through a simulation experiment.