Practical Verification of MapReduce Computation Integrity via Partial Re-execution

TL;DR

This paper introduces V-MR, a practical framework for verifying the integrity of outsourced MapReduce computations in untrusted clouds through partial re-execution, reducing verification overhead while ensuring correctness.

Contribution

The paper presents V-MR, a novel, practical verification framework that efficiently detects malicious behavior in MapReduce outsourcing using partial re-execution and program analysis.

Findings

V-MR effectively detects computation violations.

V-MR reduces verification overhead with partial re-execution.

Prototype experiments show small overhead in practice.

Abstract

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.