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SHIYF: A Secured and High-Integrity YARN Framework

College of Computer Science and Technology, Jilin University, Changchun 130012, China
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
Author to whom correspondence should be addressed.
Electronics 2019, 8(5), 548;
Received: 17 April 2019 / Revised: 7 May 2019 / Accepted: 11 May 2019 / Published: 15 May 2019
(This article belongs to the Special Issue Cloud Computing and Applications)
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Cloud computing is becoming a powerful parallel data processing method, and it can be adopted by many network service providers to build a service framework. Although cloud computing is able to efficiently process a large amount of data, it can be attacked easily due to its massively distributed cluster nodes. In this paper, we propose a secure and high-integrity YARN framework (SHIYF), which establishes a close relationship between speculative execution and the security of Yet Another Resource Negotiator (YARN, MapReduce 2.0). SHIYF computes and compares the MD5 hashes of the intermediate and final results in the MapReduce process by launching the speculative executions in a certain ratio, which is able to find actual and potentially malicious nodes in the Hadoop cluster. The prototype of SHIYF is implemented based on Hadoop 2.8.0. In this paper, theoretical derivations and experiments show that SHIYF not only guarantees the security and high integrity of the MapReduce process but also successfully locates the malicious nodes and the potential malicious ones in Hadoop, while increasing overhead slightly. Furthermore, the malicious node detection ratio is more than 87%.
Keywords: Cloud computing; Hadoop; MapReduce; YARN; speculative execution; security; integrity Cloud computing; Hadoop; MapReduce; YARN; speculative execution; security; integrity
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Deng, J.; Liu, Y.; Wang, J.; Li, S. SHIYF: A Secured and High-Integrity YARN Framework. Electronics 2019, 8, 548.

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