Comparative analysis of different models of checkpointing and recovery

• Published in: IEEE transactions on software engineering Vol. 16; no. 8; pp. 807 - 821
• Main Authors: Nicola, V.F., van Spanje, J.M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-08-1990; Institute of Electrical and Electronics Engineers; IEEE Computer Society
• Subjects: Analytical models; Applied sciences; Audit trails; Availability; Checkpointing; Comparative analysis; Computer science; Computer science; control theory; systems; Database systems; Delay; Exact sciences and technology; Failure; Frequency; Information systems. Data bases; Load; Markov analysis; Mathematical models; Memory organisation. Data processing; Performance analysis; Queueing analysis; Random variables; Reliability; Response time; Simulation; Software; Software engineering; Stochastic processes; Techniques; Check; Strategy; Performance analysis; Markov model; System reliability; Modeling; Recovery; Database management system; Comparative study
• ISSN: 0098-5589; 1939-3520
• DOI: 10.1109/32.57620

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.< >