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One of the key challenges in computer networks is network security. For securing the network, various solutions have been proposed, including network security protocols and firewalls. In the case of so-called packet-filtering firewalls, policy rules are implemented to monitor changes to the network and preserve the required security level. Due to the dramatic increase of devices, however, and herewith the rapid increase of the size of the policy rules, firewall policy anomalies occur more frequently. This requires careful implementation of the policy rules to ensure cost-efficient solutions for anomaly detection to support network security. In this study, we present an anomaly detection framework for detecting intrafirewall policy anomaly rules. The framework supports the simulation of packets through the firewall ruleset for validating and enhancing the security level of the network. The framework is validated using four different types of firewall policy anomalies. Experimental results demonstrate that the framework is effective and efficient in detecting firewall policy anomalies. [ABSTRACT FROM AUTHOR]

Mutation testing is basically a fault-based software testing technique, which has been proposed to measure the fault detection effectiveness of a test suite using programs with simulated faults (namely mutants). However, mutation testing is time consuming and computationally expensive because of the normal use of a large amount of mutants. Thus, reducing the mutants is of great significance. To address this problem, various mutant reduction techniques have been proposed. Among them, the identification of redundant mutants aims at removing mutants whose test results can be inferred by other mutants. This article proposes a redundant mutant identification technique based on selective symbolic execution called ReMuSSE for weak mutation testing. Redundant mutants could be revealed by identifying those with similar program execution state changes within a program block involving mutated statements. An empirical study was conducted using 13 C programs from different application domains with varying sizes. The empirical results showed that ReMuSSE could identify up to 31.4% redundant mutants and consequentially save up to 35.2% time cost of weak mutation testing. The results demonstrated that ReMuSSE could effectively identify redundant mutants and thus could significantly improve the efficiency of weak mutation testing. [ABSTRACT FROM AUTHOR]

As an important component of space electronic systems, static random-access memory based field programmable gate arrays (FPGAs) are inevitably affected by single-event upsets, including single-cell upset (SCU) and multiple-cell upset (MCU), caused by space radiation. In particular, as the technology scales down, the probability of MCU increases significantly; however, the analysis of MCU remains an open challenge. Based on the common cause failure theory and continuous-time Markov chain, this article proposes a new hybrid model to quantify the coexistence effect of MCU and SCU on mitigation design modes with different combinations of strategies, such as triple modular redundancy, partition, and scrubbing. Synchronization and interleaving modeling technologies are used to construct the concurrent behavior of an FPGA system under different mitigation strategies and MCU problems. In addition, we introduce a partition factor to quantitatively explain the phenomena and laws of MCUs acting on adjacent partitions. The proposed method is demonstrated using TMR with scrubbing and partition strategies, which verify that the existence of MCU reduces the system reliability and availability, and the scrubbing and partition strategy are effective against MCU issues. More importantly, the limitations of mitigation strategies in different occurrence probabilities of MCU can be found by the proposed method. In summary, the analysis and discussion presented in this article can provide useful insights for relevant designers to select and optimize different design patterns of a system operating in a dynamic and complex radiation environment. [ABSTRACT FROM AUTHOR]

Cross-project defect prediction (CPDP) refers to identifying defect-prone software modules in one project (target) using historical data collected from other projects (source), which can help developers find bugs and prioritize their testing efforts. Recently, CPDP has attracted great research interest. However, the source and target data usually exist redundancy and nonlinearity characteristics. Besides, most CPDP methods do not exploit source label information to uncover the underlying knowledge for label propagation. These factors usually lead to unsatisfactory CPDP performance. To address the above limitations, we propose a landmark selection-based kernelized discriminant subspace alignment (LSKDSA) approach for CPDP. LSKDSA not only reduces the discrepancy of the data distributions between the source and target projects, but also characterizes the complex data structures and increases the probability of linear separability of the data. Moreover, LSKDSA encodes label information of the source data into domain adaptation learning process and makes itself with good discriminant ability. Extensive experiments on 13 public projects from three benchmark datasets demonstrate that LSKDSA performs better than a range of competing CPDP methods. The improvement is 3.44%-11.23% in g-measure, 5.75%-11.76% in AUC, and 9.34%-33.63% in MCC, respectively. [ABSTRACT FROM AUTHOR]

Optimal reliability design (ORD) problem is challenging and fundamental to the study of system reliability. For a system with n components/stages where each of them can be set in m possible reliability levels, state-of-the-art linear reformulation models of ORD problem require O(nm) binary variables, O(mn) continuous variables together with either O(mn) inequality constraints or O(nm) equality constraints. Using the special property of prime factorization and adopting the logarithmic expression technique, in this article, we propose a novel linear reformulation model of the ORD problem requiring O(nm) binary variables, O(mn/n!) continuous variables, and very few linear constraints. This theoretic reduction in variables and constraints can lead to significant savings in computational efforts. Our numerical experiments further confirm the drastic reduction in computational time for solving ORD problems in large size. [ABSTRACT FROM AUTHOR]

This article presents a new two-stage stochastic model for the simultaneous optimization of component test plan and redundancy allocation. The optimal number of tests is determined for each component of the system in the first stage, and the system redundancy configuration is optimized in the second stage, after the realization of the system working condition. Scenario analysis is used for modeling variations in working conditions. A multiobjective reliability problem is formulated for series–parallel systems, and the allocation of redundancy is optimized by a genetic algorithm. The results of the feedback control system demonstrate the performance of the proposed model. [ABSTRACT FROM AUTHOR]

The working conditions of multicomponent systems are usually dynamic and stochastic. Reliability evaluation of such systems is challenging, since the components are generally positively correlated. Based on the cumulative exposure principle, we model the effects of the dynamic environments on the component lifetimes by a common stochastic time scale, and exponential dispersion process is utilized to describe the stochastic time scale. Then, the component lifetimes are shown to be positively quadrant dependent, and the joint survival function of the component lifetimes is derived, which includes the results of  as special cases. In this article, we show that neglecting either the effects of dynamic environments or the correlation among component lifetimes would underestimate the reliability of series systems and overestimate the reliability of parallel systems. We also investigate the problem of parameter redundancy of the model, and give some suggestions for data analysis. Simulation studies show that the unified model is flexible and useful for suggesting an optimal model given observed data. [ABSTRACT FROM AUTHOR]

To adapt to the uncertain environment smartly and timely, cyber physical systems (CPSs) have to interact with the physical world in a decentralized but rigorous, organized way. Guaranteeing the timing reliability is key to achieve consensus on the order of distributed events, as well as dependable cooperative decision processing. Based on our hierarchically decentralized compositional self-adaptive framework, we propose a formal compositional reliability-contract-based solution to guarantee the timing reliability of event observation and decision processing in a large-scale, geographically distributed CPS. As the prophetic decision may not fit the local situation well because of the uncertainties, we propose a gradual contract optimization solution to refine the dependability, timeliness, and energy consumption. Following the seven proposed composition schemes, we employ the nondominated sorting genetic algorithm II (NSGA-II) algorithm to optimize arrangement of decision. Moreover, a topology-aware time reserving solution is applied to improve the resilience of processing time and to tolerance timing failures. Both simulation results and real-world testing are introduced to evaluate the efficacy of our proposal. We believe that the formal compositional contract will be a competitive CPS solution to analyze requirements and optimize the self-adaptation decision at runtime. [ABSTRACT FROM AUTHOR]

Probabilistic methods based on Markov models were developed in the last few years to determine the optimal number of spare transformers (regular spare units and mobile substation units) for a group of similar distribution power transformers. These methods accounted for a number of factors that affected the number of spare transformers. The two issues that would have an impact on the number of spare units are transformer redundancy and whether or not the station has a mobile unit substation capability. These two issues were not fully investigated in the earlier spare assessment work and need to be studied in more detail. This paper describes the recent reliability study performed at Hydro One to address the two issues. The study includes a review of three spare assessment methods, two existing and one new, and a detailed analysis of how each method handles the two issues. Examples are provided to illustrate and compare the various assessment methods and to draw some recommendations with regard to the use of these methods. [ABSTRACT FROM AUTHOR]

Recently there has been an increasing demand for more powerful processors for the next-generation space missions, such as communication and earth observation. The challenge is how to improve the reliability of the processor under the “single event effects” in orbit. We have previously proposed a new way of implementing any traditional software error detection and correction techniques at instruction level, capable of covering both the CPU and caches of “commercial off the shelf” processors. In this paper, a novel way of evaluation of the software protection is presented, based on a theoretical model and software injection experiments to predict the reliability of the whole processing architecture. The fault injection will evaluate the ability of the protection code to detect and recover errors in addition to the accuracy of the reliability models, by comparing the reliability of the theoretical predictions to the reliability of the injection experiments. Automatic compiler error detection and recovery improves the reliability of the system by reducing the error rate of “single event upsets.” In some benchmarks, the error rate was reduced to less than 1%. This research has been tested in two machines; Intel core i5-3470 and a Raspberry Pi 3. On the first processor, the overhead was less than 15%, and on the second one, the overhead was less than 17%. This research can also be ported to multiple high level languages, with the ability to cover multiple instructions and datatypes. [ABSTRACT FROM AUTHOR]

This paper studies the defense strategy for a parallel system subject to an unintentional impact and an intentional impact. Existing research assumes that system damage is caused by either natural disasters (unintentional impacts) or strategic attackers (intentional impacts). However, in practice, the defender may encounter diverse scenarios, where these two kinds of impacts can happen in a sequential order. Without knowing the precise occurrence sequence, the defender must allocate its limited resources against two successive impacts. In each contest, the defender can construct redundant elements and protect them from damage, to minimize the expected loss from system destructions. Illustrative examples of the optimal strategy are presented in three cases: first, where the unintentional impact comes first; second, where the intentional impact comes first; and third, where the two impacts come in an uncertain order. Supplemental protection is considered in the model extension, where the defender can allocate additional protection resources to those elements that have survived after the first impact. [ABSTRACT FROM AUTHOR]

This paper proposes a novel reliability-aware hard real-time task scheduling method for multicore systems along with a quantitative reliability model. The proposed method uses a heuristic clustered replication to maintain the desired reliability threshold with both minimum replication overhead and latency increase. It also minimizes intercore communication overhead of tasks. Both single and multiple soft errors are considered in this method. Simulation results show that the efficiency of our proposed approach improves with larger network-on-chip sizes, higher reliability thresholds, and higher number of tolerating errors. The proposed method achieves near optimal replica overhead (up to $\text{7.3}\%$ higher than optimal replica overhead) with up to $\text{2500}\%$ time complexity improvement compared to exhaustive exploration. Experimental results also show that the feasibility of the proposed method is higher than the conventional replication method up to $\text{9.3}\%$. All experiments are performed on both synthetic random task graphs and PARSEC real application benchmarks. Obtained task mapping solutions with communication volume reduction and near optimal replica overhead impose negligible latency increase (up to $\text{6.3}\%$) in comparison with the space exploration approach. [ABSTRACT FROM AUTHOR]

A three-dimensional (3-D) integration technology involving the use of a through-silicon-via (TSV) offers advantages such as low power consumption, small form factor, and large bandwidth. However, owing to the incompleteness of the 3-D manufacturing process, TSVs may exhibit some inherent defects; hence, switching and shifting repairing methods have been proposed. These methods repair the TSV faults by rerouting the signals of the faulty TSVs to the other signal TSVs or redundant TSVs using a simple repair algorithm. However, if one TSV exhibits a defect during its manufacturing process, the probability of multiple defects occurring in the TSVs neighboring the FTSV increases, i.e., the TSV defects tend to be clustered. Therefore, recently proposed repair solutions, such as ring/router-based repair architectures, have focused on clustered TSV faults. However, the implementation of these existing repair solutions for clustered faults involves an extremely high hardware overhead. This study proposes a TSV redundancy architecture to repair clustered TSV faults with a high repair rate and low hardware overhead. The proposed architecture divides the TSVs into several groups and connects the TSVs of each group using a 2:1 multiplexer chain. Simulation results show that the proposed architecture exhibits a repair rate of 98.52% for uniformly distributed faults and 69.86% for highly clustered faults. These repair rates are higher than those of other TSV redundancy architectures and the difference in the repair rate becomes even greater if the faults are more clustered. Moreover, the approach yields a 58.55% reduced area as compared to that of the router-based redundancy architecture, which also targets the repair of clustered faults. [ABSTRACT FROM AUTHOR]

Redundant systems, which usually consist of a number of same/similar components (or modules), have been used in various critical infrastructures to ensure the system's normal function. Usually, system reliability can be improved with the adoption of additional components or redundancies. Typically, two types are mainly included, i.e., majority voters and standby redundancies (referred to as SPARE gates for simplicity). Nevertheless, with the increment of redundancies, the consumed cost or space requirement also grows. This study considers a tradeoff between cost and reliability in order to pursue the cost-effective optimal design. The relationships between the total cost and corresponding parameters are discussed thoroughly. Besides, in order to determine the cost-effective design at the expense of a unit of cost, a revised evaluation standard is proposed (referred to as ${\rm{R}\_\rm{per}\_\rm{Cost}}$). In this paper, we perform a cost-effective analysis of majority voters with different implementations; and we also perform the analyses of SPARE gates (here, a warm spare gate and a cold spare gate are mainly focused). For deriving corresponding total cost, algorithms are presented to predict the necessary failure time of components. In this line, cost-effective analyses of several case studies are performed. [ABSTRACT FROM AUTHOR]

Built-in redundancy analysis (BIRA) is widely used for memory yield improvement. However, increases in fault occurrence probability inevitably lead to the use of various spare lines to achieve a high repair rate. Generally, it is difficult to apply conventional BIRAs for memories with various spare lines because they focus on a simple spare structure. Therefore, this study examines a BIRA that focuses on a various spare lines structure. The proposed BIRA achieves a high repair rate through the use of various spare lines. Although long analysis time is typically required due to the use of various spare lines, the proposed BIRA solves the problem through sequential spare line allocation. Additionally, it achieves hardware overhead reduction through a simple analyzer. These advantages of the proposed BIRA are demonstrated experimentally. [ABSTRACT FROM PUBLISHER]

Cyber-attacks in cyber-physical systems (CPS) are receiving much attention due to the pervasive use of communication in essential services. If cyber components in CPS are compromised by attackers, the ability to maintain stability of the physical system is lost, and performance disruptions may occur. Vulnerability analysis allows quantifying the impact of attacks based on the damage cost model. Yet, existing works partially account for uncertainties in cyber-attacks and may provide inadequate support to decision making. This paper proposes a framework for optimal defense resource allocation for minimizing unsupplied demand of CPS under uncertain cyber-attacks. The vulnerability model of cyber components is described by an attacker–defender two-stage min–max game. The unavailability of cyber components causes the loss of performance of the monitored physical components. Uncertainties in the most probable attack time and in the accuracy of its estimate by the defender are considered. Numerical studies identify the optimum strategies in terms of protection and redundancy allocation of cyber components, and demonstrate that the contest intensity largely affects the two-stage game. Furthermore, if uncertainties increase, the system damage costs also increase and the defensive resource allocation strategies converge to a constant one due to the defender's lack of information about the attack. [ABSTRACT FROM PUBLISHER]

Existing works on redundancy allocation problems have typically focused on active or cold standby redundancies or a mix of them; little research is dedicated to warm standby systems but with an assumption of allocating the same choice of components within each subsystem. Motivated by the fact that components with different costs and failure time distributions from different vendors can be available for the design of the same subsystem in practice, this paper advances the state-of-the-art by presenting a solution methodology to determine combined optimal design configuration and optimal operation of heterogeneous warm standby series-parallel systems. Particularly, based on a proposed numerical reliability evaluation algorithm, two combined optimization problems (component allocation and sequencing problem, and component distribution and sequencing problem) are formulated and solved. Necessity and significance of the proposed methodology are illustrated through examples. Efficiency of the methodology is also successfully demonstrated on large warm standby series-parallel systems containing 14 subsystems of different choices of components. [ABSTRACT FROM PUBLISHER]

Warm standby redundancy is a fault-tolerant technique balancing the low economical efficiency of hot standby and the long recovery time of cold standby. In this paper, motivated by practical engineering systems, a general demand-based warm standby system (DB-WSS) considering component degradation process is studied. A series of intermediate states exists between perfect functionality and complete failure because of degradation processes. A lot of existing analytical reliability assessment techniques are focused on conventional binary-state models or exponential state transition distributions for a system or its components. In this paper, a novel reliability evaluation approach based on the multistate decision diagram for DB-WSS is proposed. The proposed technique can handle arbitrary distributions of degradation processes for multistate components or systems. Moreover, considering the imperfect switch of the warm standby component, the start failure probability is taken into account in the warm standby system. Numerical studies are given to illustrate the proposed approach. [ABSTRACT FROM PUBLISHER]

A new modeling approach is presented to optimally and simultaneously design the configuration of a multicomponent system and determine a maintenance plan with uncertain future stress exposure. Traditionally, analytical models for system design and maintenance planning are applied sequentially, but this new model provides an integrated approach to make decisions considering the lifecycle cost of the system. Specifically considering the influence of uncertain future usage stresses on component and system reliability, the integrated redundancy allocation and maintenance planning problem is formulated as a two-stage stochastic programming model with recourse. In this model, the system is exposed to uncertain usage scenarios with their associated probabilities of occurrence or likelihood. The decision variables for the first stage are the selection of component types and the number of components to be used in the system, and these variables are modeled before the uncertainty is revealed. The second-stage variables, involving a recourse function, are the preventive maintenance plan, which defines optimal maintenance times for planned replacement of components under distinct usage scenarios. Numerical examples and sensitivity analysis on series–parallel systems demonstrate applications of the proposed model and provide further insights. The comparisons of the proposed integrated approach to traditional sequential method show advantages of the proposed model in cost saving. [ABSTRACT FROM PUBLISHER]

Mutation analysis is a well known yet unfortunately costly method for measuring test suite quality. Researchers have proposed numerous mutation reduction strategies in order to reduce the high cost of mutation analysis, while preserving the representativeness of the original set of mutants. As mutation reduction is an area of active research, it is important to understand the limits of possible improvements. We theoretically and empirically investigate the limits of improvement in effectiveness from using mutation reduction strategies compared to random sampling. Using real-world open source programs as subjects, we find an absolute limit in improvement of effectiveness over random sampling—$13.078\%$. Given our findings with respect to absolute limits, one may ask: How effective are the extant mutation reduction strategies? We evaluate the effectiveness of multiple mutation reduction strategies in comparison to random sampling. We find that none of the mutation reduction strategies evaluated—many forms of operator selection, and stratified sampling (on operators or program elements)—produced an effectiveness advantage larger than $5\%$ in comparison with random sampling. Given the poor performance of mutation selection strategies—they may have a negligible advantage at best, and often perform worse than random sampling—we caution practicing testers against applying mutation reduction strategies without adequate justification. [ABSTRACT FROM PUBLISHER]

Technology scaling poses an increasing challenge to the reliability of digital circuits. Hardware redundancy solutions, such as triple modular redundancy (TMR), produce very high area overhead, so partial redundancy is often used to reduce the overheads. Approximate logic circuits provide a general framework for optimized mitigation of errors arising from a broad class of failure mechanisms, including transient, intermittent, and permanent failures. However, generating an optimal redundant logic circuit that is able to mask the faults with the highest probability while minimizing the area overheads is a challenging problem. In this study, we propose and compare two new approaches to generate approximate logic circuits to be used in a TMR schema. The probabilistic approach approximates a circuit in a greedy manner based on a probabilistic estimation of the error. The evolutionary approach can provide radically different solutions that are hard to reach by other methods. By combining these two approaches, the solution space can be explored in depth. Experimental results demonstrate that the evolutionary approach can produce better solutions, but the probabilistic approach is close. On the other hand, these approaches provide much better scalability than other existing partial redundancy techniques. [ABSTRACT FROM PUBLISHER]

Cold standby redundancy is a widely-applied design strategy to achieve high system reliability in numerous applications. To facilitate an effective system recovery in case of an online element failure occurring, the backup mechanism is typically implemented which enables a standby element to take over the mission task from a backup point instead of re-executing the entire mission task from the very beginning. However, the backup mechanism is not perfectly reliable in practice, and effect of its failure on the system reliability can be non-monotonic and is correlated to other system and element parameters. In this paper a new numerical approach is first proposed to evaluate reliability and expected mission completion time for 1-out-of-N: G cold standby systems subject to imperfect even backups. The system elements are non-repairable during the mission. Based on the proposed evaluation algorithm, effects of backup system reliability in connection with other system parameters including backup frequency, data backup and retrieval times, replacement failure probability, replacement time, and number of system elements are investigated through examples. Further, the optimal backup frequency and initiation sequencing problem is formulated and solved for heterogeneous cold standby systems, providing solutions that can maximize mission reliability or minimize expected mission completion time depending on design requirements. [ABSTRACT FROM PUBLISHER]

Fault tree analysis (FTA) is a method of analyzing and visualizing the causes of a fault using a fault tree diagram (FT diagram), which has a tree structure with logical steps. Design engineers developing a new product generally use FTA to analyze many fault events, calculate their probability, and include redundancy systems in the design process. Furthermore, FTA has been used to analyze problems with products and to prevent the occurrence of problems in the design phase. In particular, it is necessary for design engineers to analyze the events after a failure to determine the root causes of the failure of the redundancy systems. However, it is not easy for design engineers to produce an accurate FT diagram in the actual design process. We have developed a computer-aided knowledge management system for creating FT diagrams (FTAid) as part of a collaborative group (The University of Tokyo, National Institute of Advanced Industrial Science and Technology (AIST), and Jatco Ltd.). This system has been verified by the design engineers of Jatco Ltd. in actual product development. We report its effectiveness for predicting mechanical, electrical, and heat transfer failure, the verification of the system, and its validation in an actual design process. We conclude that the system can help design engineers to effectively and efficiently create FT diagrams in reliability engineering, although some existing ability in FTA and engineering is required. We also describe some outstanding issues regarding the improvement of FTAid, engineering education, and ensuring reliability. [ABSTRACT FROM AUTHOR]

The failure of any component within a series system results in the failure of the system. Incorporating active redundancy into a system generally improves its reliability and availability. This type of system does not belong to the category of coherent systems, and thus those results on coherent systems are not applicable. Hence, it is of great interest to develop statistical methodologies that perform well for inference on reliability of redundancy systems. In a life-test for multicomponent systems, component lifetimes may not be observable, but one may observe a set of components that failed along with the system. The data available in this form are called autopsy data, and, in this case, lifetime information on some of the components is missing. In this paper, we consider an equal load-sharing model and develop an expectation-maximization algorithm for estimating system reliability characteristics based on such an autopsy data. The load-sharing parameter indicates whether incorporating active redundancy improves system reliability or induces rapid failure of the system. The performance of the proposed methodology is then evaluated through Monte Carlo simulations and then illustrated with two numerical examples. [ABSTRACT FROM AUTHOR]

Enabling more than one function in a component provides a new cost-effective way to develop a highly reliable system. In this paper, we study the reliability of a one-shot system containing multifunctional components. Such systems have attracted increasing attention in many areas, such as ad hoc sensor networks. We derive the expressions for system reliability and reliability of each function, and formulate a redundancy allocation problem (RAP) with the objective of maximizing system reliability. Unlike constructing a system with single-functional components, the number of copies of a specific function to be included in each multifunctional component (i.e., functional redundancy) needs to be determined as part of the design. Moreover, a start-up strategy for turning on specific functions in these components must be decided prior to system operation. We develop a heuristic algorithm and include it in a two-stage genetic algorithm (GA) to solve the new RAP. We also apply a Tabu search (TS) method for solving such NP-hard problems. Our numerical studies illustrate that the two-stage GA and the TS method are quite effective in searching for high-quality solutions. [ABSTRACT FROM AUTHOR]

While the majority of existing works on modeling and optimizing standby systems focus on single-phased missions, only a few works consider standby systems with phased-mission requirements, and these works are only applicable to restricted cold-standby configurations with assumptions of negligible replacement times and fixed phase durations. This paper makes new theoretical contributions by suggesting a general model of heterogeneous 1-out-of-N: G warm standby phased-mission systems (PMS) with dynamic phase durations. The model takes into account diverse, phase-dependent performances and time-to-failure distributions of system elements. It also considers non-zero replacement times specific for activated elements, and for the mission phase when the replacement occurs. Both cold and hot standby PMSs are special cases of the proposed model. An algorithm for evaluating the mission reliability and expected completion time is first presented. The algorithm is applicable to any type of time-to-failure distribution for system elements. The heterogeneous standby PMS can demonstrate non-coherent behavior where the reduction of reliability of some elements can cause the increase of the entire mission reliability. Then it is demonstrated that the sequence of the element activation affects the mission reliability and its expected completion time. Hence the element sequence optimization problem is further formulated and solved for the heterogeneous warm standby PMS. Illustrative examples of mission reliability and expected completion time analysis and optimization are presented. [ABSTRACT FROM PUBLISHER]

A well-known principle in engineering is that redundancy at the component level is generally more reliability effective than that at the system level. Here, the redundancy simply means that components are connected in parallel. Motivated by this principle, in this paper, a more general problem of assembling K-out-of-N systems optimally is studied. We consider two assembled systems: one is by assembling a k-out-of-n system and a l-out-of-m system at the system level, and the other is by assembling them at a component level. With some appropriate assumptions on (n,k) and (m,l), some results on stochastic comparisons of the two assembly systems are derived. From this result, some useful principles for assembling K-out-of-N-systems optimally are proposed. Two numerical examples are given to illustrate our results as well. [ABSTRACT FROM PUBLISHER]

In this paper, we develop an analytical model based on stochastic Petri nets to capture the dynamics between adversary behavior and defense for cyber physical systems. We consider several types of failures including attrition failure, pervasion failure, and exfiltration failure which can happen to a cyber physical system. Using a modernized electrical grid as an example, we illustrate the parameterization process. Our results reveal optimal design conditions, including the intrusion detection interval, and the redundancy level, under which the modernized electrical grid's mean time to failure is maximized. Further, there exists a design tradeoff between exfiltration failure, attrition failure, and pervasion failure when using redundancy to improve the overall system reliability. [ABSTRACT FROM PUBLISHER]

Capacity Related Reliability (CRR) is an important performance index for flow networks. Most of the CRR evaluation techniques proposed in literature, which deal with subset cut enumeration from minimal cut sets, generate a large number of redundant subsets. The efforts in validating these subsets as cuts and then removing the redundant subset cuts makes these approaches computationally inefficient. To solve this problem, this paper proposes a new subset cut enumeration technique using a proposed decomposition method to avoid generating higher order redundant subset cuts of the network, where order refers to the number of links present in the cut. The proposed decomposition method decomposes a cut into new cuts if the cut has a subset in already enumerated valid subset cuts. Such newly generated cuts do not have any subset which is equal to or a superset of already enumerated valid subset cuts. Therefore, if the subsets of these newly generated cuts qualify as valid subset cuts, then none of them are equal to or a superset of any earlier valid subset cuts. The subset cut enumeration technique, along with the decomposition method, completely eliminates the chances of generating higher order redundant subset cuts. However, it leaves a small chance of generating redundant subset cuts as sometimes an already enumerated valid subset cut may become a superset of a valid subset cut, enumerated from a minimal cut treated later, making previously enumerated valid subset cut(s) redundant. [ABSTRACT FROM PUBLISHER]

To improve the system reliability while conserving the limited system resources, cold standby sparing is often used. In computing tasks, because active components fail randomly, and the standby component has to pick up the mission task whenever required, scheduled backups are often implemented to save the completed portions of the task. The backups can facilitate an effective system recovery where the standby component can take over the mission task from the last backup point instead of resuming the mission task from the very beginning. This paper considers a k-out-of-n cold standby system subject to scheduled backups, where k components are online and operating, with the remaining components waiting in the unpowered, cold standby mode. Whenever an online component fails, a cold standby component is activated to take over the mission task from the last backup point. The backup intervals are deterministic, but can be even or uneven. As the component may fail due to an imperfect switching from the standby state to the fully powered up state, the switching failure is also considered in the system model. A multi-valued decision diagram (MDD)-based analytical approach is proposed to evaluate the reliability of the considered system, and its complexity is analyzed. The proposed method is applicable to systems with non-identical components following arbitrary lifetime distributions. Examples are given to illustrate the MDD-based method. The correctness and efficiency of the proposed method are verified using Monte Carlo simulations. [ABSTRACT FROM PUBLISHER]

This paper studies a redundancy allocation problem (RAP) with cold standby strategy in non-repairable series-parallel systems. We assume that the components’ reliabilities are uncertain values in a budgeted uncertainty set, with unknown probability distributions. Because the system reliability is a nonlinear function of the components’ reliabilities, classical robust optimization approaches cannot be directly applied to construct the robust counterpart of this problem. Therefore, this paper for the first time proposes linear mixed integer programming (MIP) and binary equivalent models for the cold standby RAP; and by exploiting the problem structure, robust counterparts are developed to deal with budgeted uncertainty in this problem. Then, two exact solution methods are proposed: one of them solves a MIP model iteratively in a Benders' decomposition framework, and the other one solves a single binary linear model. The validity and the performance of the proposed approach are tested through a Monte Carlo simulation, and computational results. [ABSTRACT FROM AUTHOR]

A 3-dimensional Built-In Self-Repair (3D BISR) scheme is proposed for 3-dimensional (3D) memories. The proposed 3D BISR scheme consists of two phases: a parallel test-repair phase, and a serial test-repair phase. After all memory dice are simultaneously tested, only the faulty memory dice are serially tested and repaired using one Built-In Redundancy Analysis (BIRA) module. Thus, it is a faster test-repair with low area overhead. The proposed BIRA algorithm with a post-share redundancy scheme performs exhaustive searches for all combinations of spare rows and columns. Experimental results show that the proposed 3D BISR is up to two times faster than the 3D serial test-serial repair BISR when seven 2048 \times 2048 bit memory dice are stacked. The proposed 3D BISR requires 44.55% of the area in comparison to a 3D parallel test-parallel repair BISR for four stacked memory dice (one 128 K RAM, two 256 K RAMs, and 512 K RAM). The yield of 3D memories is the highest due to the exhaustive search BIRA algorithm with the post-share redundancy scheme as shown in various experimental results. [ABSTRACT FROM AUTHOR]

In standby systems, when an online working element fails, a replacement procedure is initiated to activate a standby unit which will take over the mission task to sustain the system function. Existing works on standby systems have mostly assumed that such replacement procedure takes a negligible or fixed amount of time. This assumption is not practical in many real-world systems, where the replacement procedure can take times that are random and different for different standby elements. This paper makes novel contributions by considering the effects of the random replacement times in analyzing and optimizing 1-out-of-N: G non-repairable warm standby systems. The system elements are not necessarily identical; different elements can have different time-to-failure distributions, different performance levels, and different replacement time distributions. The system is considered failed if the elements cannot complete the specified amount of work (mission task) within the maximum allowed mission time. A numerical algorithm is first proposed to simultaneously evaluate the mission reliability and expected mission completion time of the considered warm standby system. Influences of different mission and element parameters on the mission reliability and expected completion time are investigated. It is revealed that the considered warm standby systems exhibit non-coherent behavior as mission reliability may increase with the decrease of an element's reliability. Due to heterogeneity of the system elements, the order in which the elements are initiated and replaced can affect the mission reliability and actual completion time significantly. Therefore, based on the suggested numerical evaluation algorithm, we further formulate and solve the optimal element replacement sequencing problem for the considered warm standby system subject to random replacement times. Examples are given to demonstrate the considered problems and proposed methodology. [ABSTRACT FROM AUTHOR]

This paper addresses the coverage (including identification and isolation) of irrelevant components in systems with imperfect fault coverage (IFC). In fault-tolerant systems, a single not-covered component fault may thwart the automatic recovery mechanisms, and lead to a system or subsystem failure. The models that consider the effects of IFC are known as coverage models (CMs). In traditional CMs, except those considering functional dependency (a similar concept to relevancy but with different assumptions and semantics), coverage is typically limited to faulty components regardless of their relevancies. Consequently, an operational but irrelevant component will not be isolated, and may threaten the system by its future uncovered (not-covered) failures. Although the system is generally assumed to be coherent, which implies the relevancy of each component in the initial system state, the traditional CMs do not consider the fact that an initially relevant component could become irrelevant after the failures of other components. We propose the irrelevancy coverage model (ICM) to cover the irrelevant components in addition to the faulty components. In the ICM, a component will be isolated from the system whenever it becomes irrelevant (even it is not failed), such that its future not-covered failures will not affect the system anymore. By incorporating the coverage of irrelevant components, the ICM opens up a new cost-effective approach to improve system reliability without additional redundancy. [ABSTRACT FROM PUBLISHER]

This paper considers 1-out-of-N:G heterogeneous fault-tolerant systems that are designed with a mix of hot and cold standby redundancies to achieve the tradeoff between restoration and operation costs of standby elements. In such systems, the way in which the elements are distributed between hot and cold standby groups and the initiation sequence of all the cold standby elements can greatly affect the system reliability and mission cost. Therefore, it is significant to solve the optimal standby element distributing and sequencing problem (SE-DSP). The failure that occurs in a system element can propagate, causing the outage of other system elements, which complicates the solution to the SE-DSP problem. In this paper, we first propose a numerical method for evaluating the reliability and expected mission cost of 1-out-of-N:G systems with mixed hot and cold redundancy types and propagated failures. Two different failure propagation modes are considered: an element failure causing the outage of all the system elements, and an element failure causing the outage of only working or hot standby elements but not cold standby elements. A genetic algorithm is utilized as an optimization tool for solving the formulated SE-DSP problem, leading to a solution that can minimize the expected mission cost of the system while providing a desired level of the system reliability. Effects of the failure propagation probability on the system reliability, expected mission cost, as well as the optimization results are investigated. The suggested methodology can facilitate a reliability-cost tradeoff study of the considered systems, thus assisting in optimal decision making regarding the system's standby policy. Examples are provided for illustrating the considered problem as well as the proposed solution methodology. [ABSTRACT FROM PUBLISHER]

Optimal component order can effectively improve the system reliability for the design of a warm standby sparing (WSP) system. The optimal component order in the 1-out-of-n WSP system is studied in this paper. The optimal component order strategies in terms of the expected system lifetime are proposed, and proved for several cases. Furthermore, the 1-out-of-n WSP system with exponential components is investigated from the viewpoint of the system reliability; it is proved that the components should be activated according to the descending order of their failure rates. These results are very useful and applicable in the design of 1-out-of-n WSP systems. [ABSTRACT FROM PUBLISHER]

Stochastic orders are useful to compare the lifetimes of two systems. We discuss both active redundancy as well as standby redundancy. We show that redundancy at the component level is superior to that at the system level with respect to different stochastic orders, for different types of systems. [ABSTRACT FROM AUTHOR]

In this paper, comparisons of allocation policies of components in two-parallel-series systems with two types of components are provided with respect to both hazard rate and reversed hazard rate orders. The main results indicate that the lifetime of these kinds of system is stochastically maximized by unbalancing the two classes of components as much as possible. We only assume that the two distributions implied in the model have proportional hazard rates. The same type of comparisons are also given for the dual model, the two-series-parallel systems, but assuming that the distributions implied in the model have proportional reversed hazard rates, and therefore the final conclusion is the opposite; that is, the reliability of the system improves as the similarity between the two parallel subsystems increases. [ABSTRACT FROM PUBLISHER]

We propose a robust optimization framework to deal with uncertain component reliabilities in redundancy allocation problems in series-parallel systems. The proposed models are based on linearized versions of standard mixed integer nonlinear programming (MINLP) formulations of these problems. We extend the linearized models to address uncertainty by assuming that the component reliabilities belong to a budgeted uncertainty set, and develop robust counterpart models. A key challenge is that, because the models involve nonlinear functions of the uncertain data, classical robust optimization approaches cannot apply directly to construct their robust optimization counterparts. We exploit problem structure to develop robust counterparts and exact solution methods, and present computational results demonstrating their performance. [ABSTRACT FROM PUBLISHER]

This paper considers the optimal cold standby element sequencing problem (SESP) for 1-out-of-n: G heterogeneous non-repairable cold-standby systems that accomplish multi-phase missions. Given a fixed set of element choices, the objective of the optimal system design is to select the initiation sequence of the system elements so as to minimize the expected mission cost while providing a desired level of system reliability. It is assumed that during different mission phases the elements are exposed to different stresses, which affects their time-to-failure distributions. The startup and exploitation costs of system elements are also phase dependent. We suggest an algorithm for evaluating the mission reliability and expected mission cost based on a discrete approximation of time-to-failure distributions of the system elements. A genetic algorithm is used as an optimization tool for solving the formulated SESP for multi-phase cold-standby systems. Examples are given to illustrate the considered problem and the proposed solution methodology. [ABSTRACT FROM PUBLISHER]

A hard disk drive (HDD) failure may cause serious data loss and catastrophic consequences. Online health monitoring provides information about the degradation trend of the HDD, and hence the early warning of failures, which gives us a chance to save the data. This paper developed an approach for HDD anomaly detection using Mahalanobis distance (MD). Critical parameters were selected using failure modes, mechanisms, and effects analysis (FMMEA), and the minimum redundancy maximum relevance (mRMR) method. A self-monitoring, analysis, and reporting technology (SMART) data set is used to evaluate the performance of the developed approach. The result shows that about 67% of the anomalies of failed drives can be detected with zero false alarm rate, and most of them can provide users with at least 20 hours during which to backup the data. [ABSTRACT FROM PUBLISHER]

Different techniques have been used to deal with defects and soft errors. Repair techniques are commonly used for defects, while error correction codes are used for soft errors. Recently, some proposals have been made to use error correction codes to deal with defects. In this paper, we analyze the impact on reliability of such approaches that use error correction codes, which in addition to soft errors can resolve defects, at the cost of reduced ability to correct soft errors. The results showed that low defect rates or small memory sizes are required to have a low impact on reliability. Additionally, a technique that can improve reliability is proposed and analyzed. The results show that our new approach can achieve a similar reliability in terms of time to failure as that of a defect free memory at the cost of a more complex decoding algorithm. [ABSTRACT FROM PUBLISHER]

We propose a robust deviation framework to deal with uncertain component reliabilities in the constrained redundancy optimization problem (CROP) in series-parallel reliability systems. The proposed model is based on a linearized binary version of standard nonlinear integer programming formulations of this problem. We extend the linearized model to address uncertainty by assuming that the component reliabilities belong to an interval uncertainty set, where only upper and lower bounds are known for each component reliability, and develop a Min-Max regret model to handle data uncertainty. A key challenge is that, because the deterministic model involves nonlinear functions of the uncertain data, classical robust deviation approaches cannot be applied directly to find robust solutions. We exploit problem structures to develop four exact solution methods, and present computational results demonstrating their performance. [ABSTRACT FROM AUTHOR]

We study reliability characteristics of the k-out-of-n warm standby system with identical components subject to exponential lifetime distributions. We derive state probabilities of the warm standby system in a form that is similar to the state probabilities of the active redundancy system. Subsequently, the system reliability is expressed in several forms that can provide new insights into the system reliability characteristics. We also show that all properties and computational procedures that are applicable for active redundancy are also applicable for the warm standby redundancy. As a result, it is shown that the system reliability can be evaluated using robust algorithms within \cal O(n-k+1) computational time. In addition, we provide closed-form expressions for the hazard rate, probability density function, and mean residual life function. We show that the time-to-failure distribution of the k-out-of-n warm standby system is equal to the beta exponential distribution. Subsequently, we derive closed-form expressions for the higher order moments of the system failure time. Further, we show that the reliability of the warm standby system can be calculated using well-established numerical procedures that are available for the beta distribution. We prove that the improvement in system reliability with an additional redundant component follows a negative binomial (Pólya) distribution, and it is log-concave in n. Similarly, we prove that the system reliability function is log-concave in n. Because the k-out-of-n system with active redundancy can be considered as a special case of the k-out-of-n warm standby system, we indirectly provide some new results for the active redundancy case as well. [ABSTRACT FROM AUTHOR]

Introducing redundancy at the device-level has been proposed as the most effective way to improve reliability. With the remarkable reliability of the complementary metal oxide semiconductor (CMOS) transistors the semiconductor industry was able to fabricate, the research on device-level redundancy has reduced. However, the increasing sensitivity to noise and variations (due to the massive scaling) of the CMOS transistors has led to a revival of interest in device-level redundancy schemes during the last decade. In this paper, we introduce a novel transistor sizing method that can be used to significantly reduce the probability of failure of CMOS gates due to threshold voltage variations. The method has almost no impact on the occupied area. For a given reliability target, the proposed sizing method provides very large scale integration (VLSI) designers with several transistor sizing options which allow them to optimize the trade-off between reliability and the traditional power-area-delay design parameters. The simulation results reported in this paper will show that the proposed transistor sizing method can improve the reliabilities of classical INV, NAND-2, and NOR-2 CMOS gates by factors of more than 10^5, 10, and 10^10 respectively, while the area is increased by less than 50%. [ABSTRACT FROM PUBLISHER]

To identify the critical components or sets of components in a system, various importance measures have been proposed with different probabilistic perspectives and applications. Many of these importance measures are actually related to each other in some ways. This paper summarizes the importance measures in reliability, and presents relations and comparisons among them, focusing on their interrelations to the B-importance, the dominant relations among them, the dual relations, their performances in typical systems, and their computations. The early versions of the importance measures are for binary coherent systems, while the recent research is not limited to this type of system. This paper investigates the extensions of importance measures in noncoherent systems, multistate systems, continuum systems, and repairable systems. [ABSTRACT FROM PUBLISHER]

Many importance measures have been proposed with respect to the diverse considerations of system performance, reflecting different probabilistic interpretations and potential applications. This paper studies importance measures in reliability, including their definitions, probabilistic interpretations, properties, computations, and comparability. It categorizes importance measures into the structure, reliability, and lifetime types based on the knowledge for determining them. It covers importance measures of individual components, and ones of pairs and groups of components. It also investigates importance measures in consecutive-k-out-of-n systems. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the integrated importance measure (IIM) of component states for maintenance processes. To describe the impact of each component state in maintenance processes, a maintenance cost function of multi-state systems is defined at first. Second, considering the probability distributions, transition rates of the component states, and system maintenance costs, the IIM of component states is described. The corresponding characteristics of the IIM of the component states are discussed in both series systems and parallel systems. Then the relationships between IIM and Griffith importance, Wu importance, mean absolute deviation, and multi-state redundancy importance measures are also discussed. At last, a numerical example is given to demonstrate the IIM of component states. The results show that IIM can be used to identify the most important component state for the maintenance decision. [ABSTRACT FROM AUTHOR]

In safety critical applications, it is becoming quite common to improve reliability through the use of quadruplexed, redundant subsystems organized with 2-out-of-4:G (2oo4:G) logic. As the subsystems involved will generally be complex, comprising many individual components, they are likely to fail at a time-dependent rate \lambda(t). The maintenance schedule here is assumed to require failed subsystems to be repaired and workable within a given time \tau of failure; and repairs are assumed to be minimal so that all functioning subsystems possess the same rate \lambda(t). Within this context, we consider the dependence of the system reliability measures on the allowed repair time \tau by providing solutions to two integro-differential-delay equations (IDDEs) which bound the exact solution above and below; these bounds may be tightened by iterating the IDDEs to higher order. Results for the stationary system are used to investigate the order required to provide sufficiently tight bounds for the general case. In addition, we consider examples in support of the conjecture of Solov'yev and Zaytsev (Engineering Cybernetics, 1975) that if \lambda(t)\tau is small, then the (asymptotic) instantaneous hazard function of the system hS(t), with a time-varying \lambda(t), will approach (\lambda(t)\tau\rightarrow 0) the limit hS0(\lambda(t),\tau), where hS0(\lambda,\tau) is the asymptotical hazard rate for the same system with constant failure rates. This method then allows for a simple analysis of the case of arbitrary time-varying \lambda(t) in terms of the much simpler stationary case. [ABSTRACT FROM PUBLISHER]

System reliability optimization models considering uncertainty are described, and new algorithms are proposed to minimize the coefficient of variation of the system reliability estimate. This is a realistic, important formulation because the reliability of most components is not known with certainty, and decision-makers are often risk averse, preferring a system with a slightly lower estimated reliability if it is a more certain measure. The redundancy allocation problem can be defined as the selection of the system configuration and the type of the components to optimize some objective function(s) while satisfying some system related constraints. In this paper, we propose algorithms to minimize the coefficient of variation of the system reliability estimate with respect to a minimum system reliability constraint, and some other system related constraints. Two algorithms are presented. For problems where component mixing is not allowed, an optimal algorithm is presented based on linear integer programming. For problems where mixing is allowed, a heuristic approach is presented based on a combined neighborhood search, and a linear integer programming approach. The heuristic starts with the solution of those problems where mixing components is not allowed, and searches the neighborhood of this solution to find better prospective solutions for the problems where mixing components is allowed. Then, a linear integer programming problem is solved to identify the recommended solution to the problem. [ABSTRACT FROM AUTHOR]