CheR: Cheating Resilience in the Cloud via Smart Resource Allocation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8352)


Cloud computing offers unprecedented ways to split and offload the workload of parallel algorithms to remote computing nodes. However, such remote parties can potentially misbehave, for instance by providing fake computation results in order to save resources. In turn, these erroneous partial results can affect the timeliness and correctness of the overall outcome of the algorithm. The widely successful cloud approach increases the economic feasibility of leveraging computational redundancy to enforce some degree of assurance about the results. However, naïve solutions that dumbly replicate the same computation over several sets of nodes are not cost-efficient.

In this paper, we provide several contributions as for the distribution of workload over (heterogeneous) cloud nodes. In particular, we first formalize the problem of computing a parallel function over a set of nodes; later, we introduce CheR (for Cheating Resilience), a novel approach based upon modelling the assignment of input elements to cloud nodes as a linear integer programming problem aimed at minimizing cost while being resilient against misbehaving nodes. Further, we describe the CheR approach in different scenarios and highlight the novelty with respect to other state-of-the-art solutions. Finally, we present and discuss some experimental results showing the viability and quality of our proposal.


Smart Resource Allocation Cloud Nodes Input Elements Outsourced Computation CHEERS Statement 
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.



This work has been partially supported by the TENACE PRIN Project (n.20103P34XC) funded by the Italian Ministry of Education, University and Research.

The research leading to these results has received funding from the EU Seventh Framework Programme (FP7/2007-2013) under grant n. 256980 (NESSoS), n. 257930 (Aniketos), and EIT ICT Labs activity 13083.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Mathematics and PhysicsUniversity Roma TreRomeItaly
  2. 2.Institute for Informatics and TelematicsNational Research Council of ItalyPisaItaly

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