Fault tolerance in big data storage and processing systems: A review on challenges and solutions

被引:14
|
作者
Saadoon, Muntadher [1 ]
Ab Hamid, Siti Hafizah [1 ]
Sofian, Hazrina [1 ]
Altarturi, Hamza H. M. [1 ]
Azizul, Zati Hakim [1 ]
Nasuha, Nur [1 ]
机构
[1] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Software Engn, Kuala Lumpur 50603, Malaysia
关键词
Fault tolerance; Fault detection; Fault recovery; Big data storage; Big data processing; FAILURE RECOVERY; MAPREDUCE; RELIABILITY; AVAILABILITY; REPLICATION; NETWORKS; HADOOP;
D O I
10.1016/j.asej.2021.06.024
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Big data systems are sufficiently stable to store and process a massive volume of rapidly changing data. However, big data systems are composed of large-scale hardware resources that make their subspecies easily fail. Fault tolerance is the main property of such systems because it maintains availability, reliability, and constant performance during faults. Achieving an efficient fault tolerance solution in a big data system is challenging because fault tolerance must meet some constraints related to the system performance and resource consumption. This study aims to provide a consistent understanding of fault tolerance in big data systems and highlights common challenges that hinder the improvement in fault tolerance efficiency. The fault tolerance solutions applied by previous studies intended to address the identified challenges are reviewed. The paper also presents a perceptive discussion of the findings derived from previous studies and proposes a list of future directions to address the fault tolerance challenges. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University.
引用
收藏
页数:13
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