High-Integrity MapReduce Computation in Cloud with Speculative Execution

  • Jing Xiao
  • Zhiwei Xiao
Part of the Communications in Computer and Information Science book series (CCIS, volume 164)


Cloud computing involves processing a huge amount of data using massively, distributed computing resources. However, the massive and distributed nature of cloud computing also make the integrity of computation upon easily be easily broken either by deliberate attacks or unconscious machine failures. In this paper, we propose to provide high-integrity feature to MapReduce computation using speculative execution. The key idea of our approach is selectively replicating MapReduce tasks on a random computation node, and comparing the hash of the execution results to determine if the integrity of the task is compromised. A preliminary prototype, called Nessaj, has been implemented on Hadoop MapReduce framework. Experimental results show that Nessaj can detect and recover from our randomly injected attacks in high probability. The performance overhead is also moderate.


Cloud Platform Statistical Machine Translation Intermediate Data Hadoop Distribute File System MapReduce Framework 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jing Xiao
    • 1
  • Zhiwei Xiao
    • 2
  1. 1.School of Information SecurityShanghai JiaoTong UniversityChina
  2. 2.Software SchoolFudan UniversityChina

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