By Min Xie, Kim-Leng Poh, Yuan-Shun Dai

Computing platforms are of becoming value due to their large use in lots of parts together with these in safety-critical platforms. This ebook describes the fundamental versions and methods to the reliability research of such platforms. an in depth evaluation is supplied and types are labeled into differing kinds. a few Markov versions are prolonged to the research of a few particular computing structures comparable to mixed software program and undefined, imperfect debugging techniques, failure correlation, multi-state platforms, heterogeneous subsystems, and so forth. one of many goals of the presentation is that in response to the sound research and straightforwardness of the ways, using Markov types will be greater carried out within the computing process reliability.

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The intermittent failure can also lead the component into the permanent failure state. This scenario is presented by a Markov model as shown in Fig. 4. In Fig. 4, the states 0, 1 and 2 are operational, intermittent failure and permanent failure states, respectively. According to the model, the state 0 can make a transition to state 1 with a rate v and to state 2 with a rate From the Computing System Reliability 45 intermittent failure state 1, it can either go to operational state 0 with a rate or to permanent failure state 2 with a rate Let and be the probabilities of being in the states 0, 1 and 2, respectively.

A single hardware component may have a normal functioning state, a few degraded states and a failed state. This section analyzes the reliability performance of the single component, considering a single failure mode, double failure modes and multiple failure modes. 1. Case of a single failure mode Suppose that there are two states, and a single, irreversible transition between the two states as shown in Fig. 1. The two states are operational state and failed state denoted by state 1 and 2 respectively.

Here m(t) is called the mean value function of the NHPP. If N(t) represents the number of system failures, the function m(t) describes the expected 38 Basic Reliability Concepts cumulative number of failures in [0,t). Hence, m(t) is a very useful descriptive measure of the failure behavior. 2. Reliability measures and properties Given the mean value function m(t), the failure intensity function can be computed by Moreover, the reliability function at time is given by Generally, by using different functions m(t), different NHPP models can be obtained.