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<p>Different checkpointing strategies are combined with recovery models of different refinement levels in the database systems. The complexity of the resulting model increases with its accuracy in representing a realistic system. Three different analytic approaches are used depending on the complexity of the model: analytic, numerical and simulation. A Markovian queuing model is developed, resulting in a combined Poisson and load-dependent checkpointing strategy with stochastic recovery. A state-space analysis approach is used to derive semianalytic expressions for the performance variables in terms of a set of unknown boundary state probabilities. An efficient numerical algorithm for evaluating unknown probabilities is outlined. The validity of the numerical solution is checked against simulation results and shown to be of acceptable accuracy, particularly in the stable operating range. Simulations have shown that realistic load-dependent checkpointing results in performance close to the optimal deterministic checkpointing. Furthermore, the stochastic recovery model is an accurate representation of a realistic recovery.</p>
DBMS; checkpointing strategies; recovery models; refinement levels; realistic system; analytic approaches; simulation; Markovian queuing model; Poisson; load-dependent checkpointing strategy; stochastic recovery; state-space analysis approach; semianalytic expressions; performance variables; unknown boundary state probabilities; numerical algorithm; numerical solution; stable operating range; optimal deterministic checkpointing; computational complexity; database management systems; database theory; probability; queueing theory; system recovery.

V. Nicola and J. van Spanje, "Comparative Analysis of Different Models of Checkpointing and Recovery," in IEEE Transactions on Software Engineering, vol. 16, no. , pp. 807-821, 1990.
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