Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of evaluating the performability density and distribution of degradable computer systems is considered. A generalized model of performability is considered, wherein the dynamics of configuration modes are modeled as a nonhomogeneous Markov process, and the performance rate in each configuration mode can be time dependent. The key to the development of a unifying mathematical framework is the introduction of two related performability processes: the forward performability process over the interval (0,t), and the performability-to-go process over the interval (t,T), where T is the mission time. Stochastic differential equations techniques show that the joint density of the forward performability and configuration states satisfies a linear, hyperbolic partial differential equation (PDE) with time-dependent coefficients that runs forward in time, while the performability-to-go process satisfies an adjoint PDE running reverse in time. A numerical method for solving the PDEs is presented and is illustrated with examples. >
['Krishna R. Pattipati', 'Yong Li', 'Henk A. P. Blom']
I am writing a research paper on a idea A unified framework for the performability evaluation of fault-tolerant computer systems
[{"from": "human", "value": "I am writing a research paper on a idea A unified framework for the performability evaluation of fault-tolerant computer systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of evaluating the performability density and distribution of degradable computer systems is considered. A generalized model of performability is considered, wherein the dynamics of configuration modes are modeled as a nonhomogeneous Markov process, and the performance rate in each configuration mode can be time dependent. The key to the development of a unifying mathematical framework is the introduction of two related performability processes: the forward performability process over the interval (0,t), and the performability-to-go process over the interval (t,T), where T is the mission time. Stochastic differential equations techniques show that the joint density of the forward performability and configuration states satisfies a linear, hyperbolic partial differential equation (PDE) with time-dependent coefficients that runs forward in time, while the performability-to-go process satisfies an adjoint PDE running reverse in time. A numerical method for solving the PDEs is presented and is illustrated with examples. >"}]