Topic: « Large Scale Emulation of Blockchain-based Distributed Systems »
Blockchains is becoming increasingly important as a core component of distributed systems. It provides the trust layer that allows direct interaction between peers. Therefore, blockchain could be used as a middleware to coordinate various components of distributed systems and implement various tasks such as payment, trust, reputation, provenance & traceability, etc.
However, the main characteristics of blockchain are still relatively unknown from a performance point of view, but we already know that scalability is the main issue to solve for a wider adoption. In fact , the technology is evolving rapidly to address the scalability issue with the introduction of various forms of consensus, such as Proof-of-Stake, side channeling, Lightning Network, sharding, cross-blockchain interaction, etc.
Because of this, new methodologies are required to experiment with blockchains in an environment that would allow for reproducibility, configurability and performance evaluation. The topic of this thesis is to propose a framework for experimenting with the various blockchain
protocols and middlewares, in particular by focusing on the Ethereum blockchain.
The use of virtualization and emulation techniques will be explored: the work on the Distem emulator, targeting other kinds of distributed systems, already showed that they are suitable to achieve experimentation at scale, with controlled heterogeneity of performance (CPU, network, I/O), or controlled fault injection & load imbalance.
We will build on and extend these techniques, including exploring automatic or assisted techniques to uncover performance or resilience issues. Experimentations performed to validate the framework will focus on the evaluation of Ethereum and the interaction with various forms of side chain structures to have a better scalability while ensuring the same level of security and transparency.
This PhD thesis, funded by a CIFRE grant, will be jointly supervised at both LORIA (Nancy) and iExec headquarters (Lyon). The PhD candidate will spend a majority of the time in Nancy, but is expected to spend about three months per year in Lyon.
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