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Enabling richer statistical MANET simulations through cluster computing

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Abstract

The wide-scale adoption of modern smart phones and other multi-radio mobile devices, has begun to provide pragmatic deployment environments for non-cellular mobile ad hoc network (MANET) services (i.e., for disaster recovery scenarios, peered mobile games, social networking applications, etc.). User perceptions of the quality of such MANET services will be driven, in part, by standard network-level quality of service (QoS) metrics such as delay, jitter, throughput, etc. Much of the existing MANET literature has explored these issues, as well as MANET routing protocol design, through single computer Monte Carlo simulations (e.g., via ns-2, ns-3, OMNeT++, or OpNet). Results are then reported as the averages of these Monte Carlo runs. As is well known from probability and statistics, such averaging is only meaningful when applied across statistically ergodic data (i.e., data drawn from the same underlying distribution). But, assessing the validity of this underlying ergodic assumption requires transitioning to more rigorous cluster-based MANET simulation frameworks. This work highlights the theoretical rationale for such ergodicity testing, the developments of a cluster-based framework, the STARs framework, to support such testing, and the results and insights obtained by using this framework to evaluate the popular DYMO and OLSR MANET routing protocols. This work also discusses why the insights ergodic testing provides are of interest to potential real-world MANET deployments.

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Notes

  1. It is also possible that subsets of k are ergodic with respect to each other but not across the whole set K of Monte Carlo runs (i.e., ergodic modes of behavior may arise implying a degree of sensitivity exists with respect to the initial conditions).

  2. This formalization of Birkhoff’s Ergodic Theorem is taken from [22].

  3. It should be noted that if P t,k (x)=P k (x) then all statistics that can be calculated on P t,k (x) will also be time independent.

  4. Assessing the true uncertainty in \(\hat {P}_{q,k}(x)\) requires that P t,k (x) be known, which is not. Hence, pragmatically the W k are set such that each W q,k for q>1 has at least 150 observed data samples.

  5. Other stricter distribution free goodness-of-fit tests, such as the Anderson-Darling test, could be used, but goodness-of-fits test such as Pearson’s χ 2-test, requiring analytical knowledge of the P t,k (x) cannot be used.

  6. The exact form of the CDFs is, of course, innately tied to the exact configuration scenario simulated in the Monte Carlo runs whereas the variations between the CDFs arise due to sensitivities to the initial conditions (i.e., each runs initial random seeds).

  7. The sheer number of configuration parameters precludes directly including these details within this work.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Victoria, P.O. Box 3055 STN CSC, Victoria, B.C., V8W 3P6, Canada

    Deepali Arora, Eamon Millman & Stephen W. Neville

Authors
  1. Deepali Arora
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  2. Eamon Millman
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  3. Stephen W. Neville
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Correspondence to Deepali Arora.

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Arora, D., Millman, E. & Neville, S.W. Enabling richer statistical MANET simulations through cluster computing. Cluster Comput 16, 989–1003 (2013). https://doi.org/10.1007/s10586-013-0247-x

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