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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Vydané v:Cluster computing Ročník 23; číslo 2; s. 483 - 492
Hlavní autori: Huang, Hong, Khan, Latifur, Zhou, Shaohua
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.06.2020
Springer Nature B.V
Predmet:
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
NP-hard
Big data
Boolean satisfiability problem
Data reliability
N-queens
ISSN:1386-7857, 1573-7543
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-019-02941-1