By: Sandra Johnson, David Hyland-Wood, Anders L Madsen, Kerrie Mengersen
WWW: Anders L Madsen and Sandra Johnson

Created: 2021-06-30
Latest update: 2021-08-17

Probably Stateless: Modelling the Feasibility of Stateless Ethereum

The primary aim of Stateless Ethereum is to make Ethereum scale, by mitigating unbounded state growth. Stateless is actually a misnomer, since a stateless Ethereum client is not completely stateless, but passes the responsibility of provisioning and storing the Ethereum world state on to another participant of the network. Stateless Ethereum clients will receive blocks from miners that have been validated and each block will have a corresponding witness consisting of all the data required to execute the transactions contained in that block. Consequently more data packets will be passed around the network, and the effects that this may have on the network need to be assessed to ensure that the Ethereum ecosystem can continue operating securely and efficiently in this altered environment. This leads to the key question: Is Stateless Ethereum feasible? To answer this question, we propose a Bayesian Network model, a probabilistic graphical modelling approach, to capture the key factors and their interactions in Ethereum MainNet, the public Ethereum blockchain, focussing on the changes being introduced by stateless Ethereum to assess the expected health of the Ethereum ecosystem, using publicly available Ethereum 1.0 data, supplemented with other data sources, including model output and expert knowledge.

Below is a set of HUGIN widgets for interacting with the model:

Is Stateless Ethereum Feasible?

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Scenario 1: Block propagation time is low and witness creation time is low, 0-5. This should make the ecosystem appear more healthy.

Scenario 2 illustrates....

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Structure of the Bayesian Network Model

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Acknowledgements

The authors wish to thank Nick Addison, Meredith Baxter, Tim Beiko, Vanessa Bridge, Joseph Chow, Zac Cole, Robert Drost, A. Frederick Dudley, Ben Edgington, Danno Ferrin, Matt Garnett, Griffin Hotchkiss, Casian Lacatusu, Nicolas Liochon, Piper Merriam, Trent Mohay, Horacio Mijail Anton Quiles, Raghavendra Ramesh, Peter Robinson, Roberto Saltini, Lucas Saldanha, Zhenyang Shi, Adrian Sutton, Rai Sur, Chris Wessels and WonderNetwork for insightful discussions regarding the Ethereum ecosystem, Stateless Ethereum, the Ethereum 2.0 witness specification and for assisting with the quantification of the model by providing expert knowledge, data and model output.

References and Further Reading

[1] U. B. Kjærulff and A. L. Madsen, Bayesian Networks and Influence Diagrams, Second Edi. Springer, 2013.

[2] R.E. Neapolitan, Learning Bayesian Networks, Pearson Education, Inc., 2004

[3] F.V. Jensen, Bayesian Networks and Decision Graphs, Springer-Verlag, Inc., 2001

[4] K.B. Korb and A.E. Nicholson, Bayesian Artificial Intelligence, Chapman & Hall/CRC, Second Edition, 2011

[5] J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann Publishers, 1988

[6] D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009

[7] D. N. Barton et al., Bayesian networks in environmental and resource management, Integr. Environ. Assess. Manag., vol. 8, no. 3, 2012.

[8] S. Johnson et al., Modeling the viability of the free-ranging cheetah population in Namibia: an object-oriented Bayesian network approach, Ecosphere, vol. 4, no. 7, p. art90, Jul. 2013.

[9] J. Holt et al., Bayesian Networks to Compare Pest Control Interventions on Commodities Along Agricultural Production Chains, Risk Anal., vol. 38, no. 2, 2018.

[10] F. Taroni, Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science, 2nd Edition, 2nd edition. John Wiley & Sons, 2014.

[11] B. G. Marcot et al, Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation, Can. J. For. Res., 2007.

[12] B. G. Marcot and T. D. Penman, Advances in Bayesian network modelling: Integration of modelling technologies, Environmental Modelling and Software. 2019.

Contact information

For further details on the study and Bayesian network model: Sandra Johnson (sandra(dot)johnson(at)consensys(dot)net)

For further details on the use of Bayesian networks and web deployment of models contact: Anders L Madsen (alm(at)hugin(dot)com)

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