Classified enhancement model for big data storage reliability based on Boolean satisfiability problem
Disk reliability is a serious problem in the big data foundation environment. Although the reliability of disk drives has greatly improved over the past few years, they are still the most vulnerable core components in the server. If they fail, the result can be catastrophic: it can take some days to...
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| Vydáno v: | Cluster computing Ročník 23; číslo 2; s. 483 - 492 |
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01.06.2020
Springer Nature B.V |
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| ISSN: | 1386-7857, 1573-7543 |
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| Abstract | Disk reliability is a serious problem in the big data foundation environment. Although the reliability of disk drives has greatly improved over the past few years, they are still the most vulnerable core components in the server. If they fail, the result can be catastrophic: it can take some days to recover data, sometimes data lost forever. These are unacceptable for some important data. XOR parity is a typical method to generate reliability syndrome, thus improving the reliability of the data. In practice, we find that the data is still likely to be lost. In most storage systems reliability improvements are achieved through the allocation of additional disks in Redundant Arrays of Independent Disks (RAID), which will increase the hardware costs, thus it will be very difficult for cost-constrained environments. Therefore, how to improve the data integrity without raising the hardware cost has aroused much interest of big data researchers. This challenge is when creating non-traditional RAID geometries, care must be taken to respect data dependence relationships to ensure that the new RAID strategy improves reliability, which is a NP-hard problem. In this paper, we present an approach for characterizing these challenges using high-dimension variants of the n-queens problem that enables performable solutions via the SAT solver MiniSAT, and use the greedy algorithm to analyze the queen’s attack domain, as a basis for reliability syndrome generation. A large number of experiments show that the approach proposed in this paper is feasible in software-defined data centers and the performance of the algorithm can meet the current requirements of the big data environment. |
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| AbstractList | Disk reliability is a serious problem in the big data foundation environment. Although the reliability of disk drives has greatly improved over the past few years, they are still the most vulnerable core components in the server. If they fail, the result can be catastrophic: it can take some days to recover data, sometimes data lost forever. These are unacceptable for some important data. XOR parity is a typical method to generate reliability syndrome, thus improving the reliability of the data. In practice, we find that the data is still likely to be lost. In most storage systems reliability improvements are achieved through the allocation of additional disks in Redundant Arrays of Independent Disks (RAID), which will increase the hardware costs, thus it will be very difficult for cost-constrained environments. Therefore, how to improve the data integrity without raising the hardware cost has aroused much interest of big data researchers. This challenge is when creating non-traditional RAID geometries, care must be taken to respect data dependence relationships to ensure that the new RAID strategy improves reliability, which is a NP-hard problem. In this paper, we present an approach for characterizing these challenges using high-dimension variants of the n-queens problem that enables performable solutions via the SAT solver MiniSAT, and use the greedy algorithm to analyze the queen’s attack domain, as a basis for reliability syndrome generation. A large number of experiments show that the approach proposed in this paper is feasible in software-defined data centers and the performance of the algorithm can meet the current requirements of the big data environment. |
| Author | Khan, Latifur Huang, Hong Zhou, Shaohua |
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Eng.201620161435903401400.68068 Rozier, E.W.D., Zhou, P., Divine, D.: Building intelligence for software defined data centers: modeling usage patterns. In: International Systems & Storage Conference (2013) ZhaoHXuYXiangLScaling up of E-Msr codes based distributed storage systems with fixed number of redundancy nodesInt. J. Distrib. Parallel Syst.201235110.5121/ijdps.2012.3501 Huang, C., Li, J., Chen, M.: On optimizing XOR-based codes for fault-tolerant storage applications. In: Information Theory Workshop, 2007. ITW’07. IEEE. IEEE, pp. 218–223 (2007) Bayram, U., Divine, D., Zhou, P., et al.: Improving reliability with dynamic syndrome allocation in intelligent software defined data centers. In: Proceedings of the 2015 45th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, pp. 219–230 (2015) Gong, W., Zhou, X.: A survey of SAT solver. In: AIP Conference Proceedings, vol. 1836, No. 1, p. 020059. AIP Publishing (2017) McCartyCPQueen squaresAm. 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| References_xml | – reference: SathiamoorthyMAsterisMPapailiopoulosDXoring elephants: novel erasure codes for big dataProc. VLDB Endow.20136532533610.14778/2535573.2488339 – reference: Utard, G., Vernois, A.: Data durability in peer to peer storage systems. In: CCGrid 2004 IEEE International Symposium on Cluster Computing and the Grid, 2004. IEEE, pp. 90-97 (2004) – reference: BellJStevensBA survey of known results and research areas for n-queensDiscrete Math.20093091131247499710.1016/j.disc.2007.12.043 – reference: Bayram, U., Rozier, K.Y., Rozier, E.W.D.: Characterizing data dependence constraints for dynamic reliability using N-queens attack domains. In: Proceedings of the International Conference on Quantitative Evaluation of Systems. Springer, Cham, pp. 211–227 (2015) – reference: Corbett, P., English, B., Goel, A., et al.: Row-diagonal parity for double disk failure correction. In: Proceedings of the 3rd USENIX Conference on File and Storage Technologies. USENIX Association Berkeley, CA, USA, 1–14 (2004) – reference: Rozier, E.W.D., Zhou, P., Divine, D.: Building intelligence for software defined data centers: modeling usage patterns. In: International Systems & Storage Conference (2013) – reference: Bayram, U., Divine, D., Zhou, P., et al.: Improving reliability with dynamic syndrome allocation in intelligent software defined data centers. In: Proceedings of the 2015 45th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, pp. 219–230 (2015) – reference: GutirreznaranjoMAMartnezdelamorMAPrezhurtadoISolving the N-queens puzzle with P systemsRosa M Gutirrez Escudero2012199210 – reference: Huang, C., Li, J., Chen, M.: On optimizing XOR-based codes for fault-tolerant storage applications. In: Information Theory Workshop, 2007. ITW’07. IEEE. IEEE, pp. 218–223 (2007) – reference: Zhu, Y., Lee, P.P.C., Hu, Y., et al.: On the speedup of single-disk failure recovery in xor-coded storage systems: Theory and practice. In: Proceedings of the 2012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST). IEEE, pp. 1–12 (2012) – reference: ChenPMLeeEKGibsonGARAID: high-performance, reliable secondary storageACM Comput. Surv. (CSUR)199426214518510.1145/176979.176981 – reference: Schwarz, S.J.T., Long, D.D.E., Paris, J.F.: Reliability of disk arrays with double parity. In: Proceedings of the 2013 IEEE 19th Pacific Rim International Symposium on Dependable Computing (PRDC). IEEE, pp. 108–117 (2013) – reference: Rozier, E.W.D., Sanders, W.H.: A framework for efficient evaluation of the fault tolerance of deduplicated storage systems. In: Proceedings of the IEEE/IFIP International Conference on Dependable Systems & Networks. IEEE Computer Society (2012) – reference: http://minisat.se/ – reference: McCartyCPQueen squaresAm. Math. Monthly1978857578580153876710.1080/00029890.1978.11994646 – reference: Rozier, E.W., Rozier, K.Y.: SMT-driven intelligent storage for big data. In: Proceedings of the Ninth International Workshop on Constraints in Formal Verification (CFV 2015), Austin, Texas, USA (2015) – reference: SchroederBGibsonGADisk failures in the real world: what does an mttf of 1,000,000 hours mean to you?FAST200771116 – reference: TurnerVGantzJFReinselDThe digital universe of opportunities: rich data and the increasing value of the internet of thingsIDC Anal. Fut.201416110 – reference: WuXXuYYuenCA tag encoding scheme against pollution attack to linear network codingIEEE Trans. Parallel Distrib. Syst.2014251334210.1109/TPDS.2013.19 – reference: ZhaoHXuYXiangLScaling up of E-Msr codes based distributed storage systems with fixed number of redundancy nodesInt. J. Distrib. Parallel Syst.201235110.5121/ijdps.2012.3501 – reference: Li, T., Mehta, A., Yang, P.: Security Analysis of Email systems. In: Proceedings of the 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud). IEEE, pp. 91–96 (2017) – reference: Pris, J.F., Long, D.D.E., Litwin, W.: Three-dimensional redundancy codes for archival storage. In: Proceedings of the 2013 IEEE 21st International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), IEEE. pp. 328–332 (2013) – reference: Gong, W., Zhou, X.: A survey of SAT solver. In: AIP Conference Proceedings, vol. 1836, No. 1, p. 020059. AIP Publishing (2017) – reference: LiuXFanLWangLMultiobjective reliable cloud storage with its particle swarm optimization algorithmMath. Probl. Eng.201620161435903401400.68068 – reference: Rozier, E.W.D., Sanders, W.H., Zhou, P., et al.: Modeling the fault tolerance consequences of deduplication. In: Reliable Distributed Systems. 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| SubjectTerms | Artificial intelligence Big Data Boolean Catastrophic failure analysis Computer Communication Networks Computer Science Data integrity Data storage Disk drives Disks Failure Greedy algorithms Hardware Operating Systems Processor Architectures RAID Storage systems System reliability |
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| Title | Classified enhancement model for big data storage reliability based on Boolean satisfiability problem |
| URI | https://link.springer.com/article/10.1007/s10586-019-02941-1 https://www.proquest.com/docview/2918271569 |
| Volume | 23 |
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