论文标题

通过部分重新执行对MapReduce计算完整性的实际验证

Practical Verification of MapReduce Computation Integrity via Partial Re-execution

论文作者

Yoon, Eunjung, Liu, Peng

论文摘要

大数据处理通常被外包给功能强大但不受信任的云服务提供商,这些提供商为较弱的客户提供敏捷且可扩展的计算资源。但是,不信任的云服务不能确保数据和计算的完整性,而客户端无法控制外包计算或无法检查执行的正确性。尽管对可验证计算的兴趣日益增加和最近的进展,但由于高验证开销,现有技术对于大数据处理仍然不够实用。在本文中,我们提出了一种称为V-MR(可验证的MapReduce)的解决方案,该解决方案是一个框架,可通过部分重新执行在不信任的云中验证MapReduce计算的完整性。 V-MR实际上是有效和有效的,因为(1)它可以检测到MapReduce计算完整性的违规,并确定涉及产生不正确计算的恶意工人。 (2)使用程序分析,它可以通过仔细选择的输入数据和程序代码来减少验证的开销。 V-MR原型的实验结果表明,V-MR可以通过小型开销有效地验证MapReduce计算的完整性,以部分重新执行。

Big data processing is often outsourced to powerful, but untrusted cloud service providers that provide agile and scalable computing resources to weaker clients. However, untrusted cloud services do not ensure the integrity of data and computations while clients have no control over the outsourced computation or no means to check the correctness of the execution. Despite a growing interest and recent progress in verifiable computation, the existing techniques are still not practical enough for big data processing due to high verification overhead. In this paper, we present a solution called V-MR (Verifiable MapReduce), which is a framework that verifies the integrity of MapReduce computation outsourced in the untrusted cloud via partial re-execution. V-MR is practically effective and efficient in that (1) it can detect the violation of MapReduce computation integrity and identify the malicious workers involved in the that produced the incorrect computation. (2) it can reduce the overhead of verification via partial re-execution with carefully selected input data and program code using program analysis. The experiment results of a prototype of V-MR show that V-MR can verify the integrity of MapReduce computation effectively with small overhead for partial re-execution.

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