Formal methods and stochastic models for performance evaluation

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This comprehensive work delves into various optimization problems in service provisioning systems, exploring Markov chains and their implications on steady-state probabilities, particularly regarding reward-based measures and equilibrium distributions. It discusses compositionality for Markov reward chains with rapid transitions and presents closed-form absorption time bounds alongside a canonical representation of order 3 phase-type distributions. The text also covers process algebras and state machines, introducing SPAMR, an extension of PAMR with stochastic time, and highlights advancements in SPDL model checking through property-driven state space generation. Additionally, it evaluates finite state machines with stochastic time and timeouts, investigates peer-to-peer (P2P) systems, and assesses P2P search algorithms for trust path discovery. The work emphasizes building online performance models for grid middleware, particularly through a case study on the Globus Toolkit, and proposes a performance measuring framework for grid market middleware. It further explores queueing theory applications, including a fixed-point algorithm for closed queueing networks and automated architectural feedback generation from software performance analysis. The text also examines optimal dynamic server allocation in systems with on/off sources, workload characterization of benchmarks, and resource sharing in performance models. Lastly, it