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1. RAFI: Risk-Aware Failure Identification to Improve the RAS in Erasure-coded Data Centers

   Fang, J; Wan, S; He, X (July 2018, The USENIX Annual Technical Conference (ATC))

   Data reliability and availability, and serviceability (RAS) of erasure-coded data centers are highly affected by data repair induced by node failures. Compared to the recovery phase of the data repair, which is widely studied and well optimized, the failure identification phase of the data repair is less investigated. Moreover, in a traditional failure identification scheme, all chunks share the same identification time threshold, thus losing opportunities to further improve the RAS. To solve this problem, we propose RAFI, a novel risk-aware failure identification scheme. In RAFI, chunk failures in stripes experiencing different numbers of failed chunks are identified using different time thresholds. For those chunks in a high risk stripe (a stripe with many failed chunks), a shorter identification time is adopted, thus improving the overall data reliability and availability. For those chunks in a low risk stripe (one with only a few failed chunks), a longer identification time is adopted, thus reducing the repair network traffic. Therefore, the RAS can be improved simultaneously. We use both simulations and prototyping implementation to evaluate RAFI. Results collected from extensive simulations demonstrate the effectiveness and efficiency of RAFI on improving the RAS. We implement a prototype on HDFS to verify the correctness and evaluate the computational cost of RAFI. 

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2. Modeling Common Cause Failures in Systems with Triple Modular Redundancy and Repair

   https://doi.org/10.1109/RAMS48030.2020.9153662  

   Cannon, Matthew J.; Keller, Andrew M.; Perez-Celis, Andres; Wirthlin, Michael J. (January 2020, 2020 Annual Reliability and Maintainability Symposium (RAMS))

   Triple modular redundancy (TMR) is commonly employed to increase the reliability and mean time to failure (MTTF) of a system. This improvement can be shown by using a continuous time Markov chain. However, typical Markov chain models do not model common cause failures (CCF), which is a singular event that simultaneously causes failure in multiple redundant modules. This paper introduces a new Markov chain to model CCF in TMR with repair systems. This new model is compared to the idealized models of TMR with repair without CCF. The fundamental limitations that CCF imposes on the system are shown and discussed. In a motivating example, it is seen that CCF imposes a limitation of 51× on the reliability improvement in a system with TMR and repair compared to a simplex system, (i.e., without TMR). A case study is also presented where the likelihood of CCF is reduced by a factor of 18× using various mitigation techniques. Reducing the CCF compounds the reliability improvement of TMR with repair and leads to a overall system reliability improvement of 10,000× compared to the simplex system as supported by the proposed model. 

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3. Reliability Analysis of a Decentralized Microgrid Control Architecture

   https://doi.org/10.1109/TSG.2018.2843527  

   Bani-Ahmed, Abedalsalam; Rashidi, Mohammad; Nasiri, Adel; Hosseini, Hossein (June 2018, IEEE Transactions on Smart Grid)

   Reliability enhancement of microgrids is challenged by environmental and operational failures. Centrally controlled microgrids are susceptible to failures at high probability due to a single-point-of-failure, e.g. the central controller. True decentralization of microgrid architecture entails elimination of the central controller, attaining a parallel configuration for the system. In this paper, decentralized microgrid control architecture is proposed as a solution for reliability degradation over the time, and analyzes the reliability aspects of centralized and decentralized control architectures for microgrids. Degree of importance of a single controller in centralized and decentralized architectures is determined and validated by Markov Chain Models (MCM). Results confirm that higher reliability is achieved when true decentralization of control architecture is adopted. Challenges of implementing a true decentralized control architecture are discussed. Hardware-In-the-Loop simulation results for microgrid controller failure scenarios for both architectures are presented and discussed. 

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4. Static and Dynamic Load-Triggered Cascading Failure Mitigation for Storage Area Networks

   https://doi.org/10.33889/IJMEMS.2024.9.4.036  

   Lyu, Guixiang; Xing, Liudong; Zhao, Guilin (August 2024, International Journal of Mathematical, Engineering and Management Sciences)

   Storage area networks (SANs) are a widely used and dependable solution for data storage. Nevertheless, the occurrence of cascading failures caused by overloading has emerged as a significant risk to the reliability of SANs, impeding the delivery of the desired quality of service to users. This paper makes contributions by proposing both static and dynamic load-triggered redistribution strategies to alleviate the cascading failure risk during the mission time. Two types of node selection rules, respectively based on the load level and node reliability, are studied and compared. Based on the SAN component reliability evaluation using the accelerated failure-time model under the power law, the SAN reliability is evaluated using binary decision diagrams. A detailed case study of a mesh SAN is conducted to compare the performance of different cascading failure mitigation schemes using criteria of SAN reliability improvement ratio and resulting SAN reliability after the mitigation. 

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5. Forecasting Repair and Maintenance Services of Medical Devices Using Support Vector Machine

   https://doi.org/10.1115/MSEC2021-63966  

   Liao, Hao-yu; Cade, Willie; Behdad, Sara (June 2021, the ASME International Manufacturing Science and Engineering Conference (MSEC))

   Accurate prediction of product failures and the need for repair services become critical for various reasons, including understanding the warranty performance of manufacturers, defining cost-efficient repair strategies, and compliance with safety standards. The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large dataset of over 530,000 repairs and maintenance of medical devices has been investigated by employing the Support Vector Machine (SVM) tool. SVM with four kernel functions is used to forecast the timing of the next failure or repair request in the system for two different products and two different failure types, namely random failure and physical damage. A frequency analysis is also conducted to explore the product quality level based on product failure and the time to repair it. Besides, the best probability distributions are fitted for the number of failures, the time between failures, and the time to repair. The results reveal the value of data analytics and machine learning tools in analyzing post-market product performance and the cost of repair and maintenance operations. 

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