APERIODIC OPTIMAL CHECKPOINT SEQUENCE UNDER STEADY-STATE SYSTEM AVAILABILITY CRITERION

2006 ◽  
Author(s):  
K. IWAMOTO ◽  
T. MARUO ◽  
H. OKAMURA ◽  
T. DOHI
Keyword(s):  
Steady State ◽  
State System ◽  
System Availability

scholarly journals Availability Analysis of Software Systems with Rejuvenation and Checkpointing

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 846
Author(s):  
Junjun Zheng ◽  
Hiroyuki Okamura ◽  
Tadashi Dohi
Keyword(s):  
Steady State ◽  
Human Error ◽  
System Modeling ◽  
Regenerative Process ◽  
State System ◽  
Software Systems ◽  
System Availability ◽  
Software Rejuvenation ◽  
Availability Analysis ◽  
Availability Model

In software reliability engineering, software-rejuvenation and -checkpointing techniques are widely used for enhancing system reliability and strengthening data protection. In this paper, a stochastic framework composed of a composite stochastic Petri reward net and its resulting non-Markovian availability model is presented to capture the dynamic behavior of an operational software system in which time-based software rejuvenation and checkpointing are both aperiodically conducted. In particular, apart from the software-aging problem that may cause the system to fail, human-error factors (i.e., a system operator’s misoperations) during checkpointing are also considered. To solve the stationary solution of the non-Markovian availability model, which is derived on the basis of the reachability graph of stochastic Petri reward nets and is actually not one of the trivial stochastic models such as the semi-Markov process and the Markov regenerative process, the phase-expansion approach is considered. In numerical experiments, we illustrate steady-state system availability and find optimal software-rejuvenation policies that maximize steady-state system availability. The effects of human-error factors on both steady-state system availability and the optimal software-rejuvenation trigger timing are also evaluated. Numerical results showed that human errors during checkpointing both decreased system availability and brought a significant effect on the optimal rejuvenation-trigger timing, so that it should not be overlooked during system modeling.

A model of a growing steady state system

1966 ◽  
Vol 10 (3) ◽  
pp. 387-398 ◽  
Author(s):  
J.N.R. Grainger ◽  
L. Bass
Keyword(s):  
Steady State ◽  
State System

scholarly journals Methodology for the Assessment of Imprecise Multi-State System Availability

Mathematics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 150
Author(s):  
Joanna Akrouche ◽  
Mohamed Sallak ◽  
Eric Châtelet ◽  
Fahed Abdallah ◽  
Hiba Hajj Chehade
Keyword(s):  
Markov Chains ◽  
Constraint Propagation ◽  
State System ◽  
Combined Approach ◽  
System Availability ◽  
Contraction Method ◽  
Numerical Examples ◽  
Complex Architectures ◽  
Backward Propagation ◽  
Interval Constraint

Most existing studies of a system’s availability in the presence of epistemic uncertainties assume that the system is binary. In this paper, a new methodology for the estimation of the availability of multi-state systems is developed, taking into consideration epistemic uncertainties. This paper formulates a combined approach, based on continuous Markov chains and interval contraction methods, to address the problem of computing the availability of multi-state systems with imprecise failure and repair rates. The interval constraint propagation method, which we refer to as the forward–backward propagation (FBP) contraction method, allows us to contract the probability intervals, keeping all the values that may be consistent with the set of constraints. This methodology is guaranteed, and several numerical examples of systems with complex architectures are studied.

scholarly journals Degrading systems availability analysis: analytical semi-Markov approach

2021 ◽  
Vol 23 (1) ◽  
pp. 195-208
Author(s):  
Varun Kumar ◽  
Girish Kumar ◽  
Rajesh Kumar Singh ◽  
Umang Soni
Keyword(s):  
Steady State ◽  
Degradation Mechanism ◽  
Continuous Process ◽  
Mechanical Systems ◽  
Embedded Markov Chain ◽  
Steady State Probability ◽  
Opportunistic Maintenance ◽  
System Availability ◽  
Availability Analysis ◽  
Complex Mechanical Systems

This paper deals with modeling and analysis of complex mechanical systems that deteriorate with age. As systems age, the questions on their availability and reliability start to surface. The system is believed to suffer from internal stochastic degradation mechanism that is described as a gradual and continuous process of performance deterioration. Therefore, it becomes difficult for maintenance engineer to model such system. Semi-Markov approach is proposed to analyze the degradation of complex mechanical systems. It involves constructing states corresponding to the system functionality status and constructing kernel matrix between the states. The construction of the transition matrix takes the failure rate and repair rate into account. Once the steady-state probability of the embedded Markov chain is computed, one can compute the steady-state solution and finally, the system availability. System models based on perfect repair without opportunistic and with opportunistic maintenance have been developed and the benefits of opportunistic maintenance are quantified in terms of increased system availability. The proposed methodology is demonstrated for a two-stage reciprocating air compressor with intercooler in between, system in series configuration.

Determination of metabolic fluxes in a non-steady-state system

2007 ◽  
Vol 68 (16-18) ◽  
pp. 2313-2319 ◽  
Author(s):  
C.J. Baxter ◽  
J.L. Liu ◽  
A.R. Fernie ◽  
L.J. Sweetlove
Keyword(s):  
Steady State ◽  
State System ◽  
Metabolic Fluxes

Analysis of Two Phases Queue With Vacations and Breakdowns Under T-Policy

2019 ◽  
pp. 13-31
Author(s):  
Khalid Alnowibet ◽  
Lotfi Tadj
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
Performance Measures ◽  
Threshold Level ◽  
System Size ◽  
State System ◽  
Markov Chain Approach ◽  
Optimal Value ◽  
Two Phases ◽  
Random Breakdowns

The service system considered in this chapter is characterized by an unreliable server. Random breakdowns occur on the server and the repair may not be immediate. The authors assume the possibility that the server may take a vacation at the end of a given service completion. The server resumes operation according to T-policy to check if enough customers have arrived while he was away. The actual service of any arrival takes place in two consecutive phases. Both service phases are independent of each other. A Markov chain approach is used to obtain the steady state system size probabilities and different performance measures. The optimal value of the threshold level is obtained analytically.

A Study on the Life Time of the S3-State in the Filamentous Cyanobacterium Oscillatoria chalybea

1994 ◽  
Vol 49 (1-2) ◽  
pp. 108-114 ◽  
Author(s):  
G. H. Schmid ◽  
K. P. Bader ◽  
R. Schulder
Keyword(s):  
Steady State ◽  
Molecular Oxygen ◽  
Life Time ◽  
State System ◽  
Filamentous Cyanobacterium ◽  
Entire System ◽  
Higher Plant ◽  
Oscillatoria Chalybea ◽  
S States ◽  
Reduced State

In the filamentous cyanobacterium Oscillatoria chalybea deactivation of the S-states starting from steady-state conditions in which S0 = S1 = S2 = S3 = 25% reveals that S3 deactivates to a finite level of approx. 10%. This level is reached under normal conditions between 10-15 seconds. This quasi metastable S3 meets all requirements for S3 in that one flash eliminates this redox conditions to give S4 and therewith molecular oxygen. An analysis of the cyanobacterial S-state system in the 5-state Kok model shows that the S-state population in the dark adapted sample contains no contribution from S-1 or a more reduced condition which under normal conditions is the case for Chlorella or higher plant chloroplasts. Hence under standard conditions, the Oscillatoria condition is a pure Kok-4-condition in which S0 is the most reduced state. Under these conditions S2 seems to deactivate to S1 and S3 to S2 and to a smaller extent to S0. In the presence of the ADRY-reagent Ant-2-p (2-(3-chloro-4-trifluoromethyl)- anilino-3,5-dinitrothiophene) introduced by Renger (Biochim. Biophys. Acta 256,428,1972), which is supposed to specifically act on the S3-state (and thereby on S2), not only the deactivation kinetic of S3 (and S2) is accelerated (hence the life time of the S3-state is shortened), but also the level of metastable S3 becomes practically zero. An analysis of the deactivation pattern shows that the agent changes the mode of deactivation of the entire system. Thus, it is seen that after deactivation of a sample in presence of this agent the dark population of S-states contains the more reduced redox condition S-1 It looks as if in this condition S2 deactivates not only to S1 but also to an appreciable extent by two steps to S-1 Another agent ABDAC (alkyl-benzyl-dimethyl-ammoniumchloride) seems to lengthen the lifetime of the S2 and S3 condition in this cyanobacterium by apparently acting on the membrane condition.

scholarly journals Bayesian estimate of system availability for consecutive k-out-of-n:F system

2021 ◽  
Vol 24 (3) ◽  
pp. 1692
Author(s):  
Madhumitha J. ◽  
G. Vijayalakshmi
Keyword(s):  
Steady State ◽  
Sliding Window ◽  
Point Estimate ◽  
Significant Feature ◽  
Efficient Design ◽  
Squared Error Loss Function ◽  
System Availability ◽  
Squared Error ◽  
Distributed Components ◽  
Vacuum Systems

In the efficient design and functionality of complex systems, redundancy problems in systems play a key role. The consecutive-k-out-of-n:F structure, which has broad application in street light arrangements, vacuum systems in an accelerator, sliding window detection, relay stations for a communication system. Availability is one of the significant measures for a maintained device because availability accounts for the repair capability. A very significant feature is the steady-state availability of a repairable device. For the repairable consecutive k-out-of-n:F system with independent and identically distributed components, the Bayesian point estimate (B.P.E) of steady-state availability under squared error loss function (SELF) and confidence interval are obtained.

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