# Performance Evaluation, Optimization and Dynamic Decision in Blockchain Systems: A Recent Overview

arXiv: https://arxiv.org/abs/2211.15907 · HTML: https://mlsanity.com/paper/2211.15907/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 48 of its 151 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 30 of 48)

- [Random Walks: A Review of Algorithms and Applications](https://mlsanity.com/paper/2008.03639/) (2020)
- [Optimal Selfish Mining Strategies in Bitcoin](https://mlsanity.com/paper/1507.06183/) (2015)
- [Blockchain Systems, Technologies and Applications: A Methodology Perspective](https://mlsanity.com/paper/2105.03572/) (2021)
- [Performance Analysis of the Raft Consensus Algorithm for Private Blockchains](https://mlsanity.com/paper/1808.01081/) (2018)
- [Flash: Efficient Dynamic Routing for Offchain Networks](https://mlsanity.com/paper/1902.05260/) (2019)
- [An Overview for Markov Decision Processes in Queues and Networks](https://mlsanity.com/paper/1907.10243/) (2019)
- [Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning](https://mlsanity.com/paper/1908.07467/) (2019)
- [When Blockchain Meets AI: Optimal Mining Strategy Achieved By Machine Learning](https://mlsanity.com/paper/1911.12942/) (2019)
- [Throughput Optimal Routing in Blockchain Based Payment Systems](https://mlsanity.com/paper/2001.05299/) (2019)
- [Pooling is not Favorable: Decentralize Mining Power of PoW Blockchain Using Age-of-Work](https://mlsanity.com/paper/2104.01918/) (2021)
- [Bitcoin Blockchain Dynamics: the Selfish-Mine Strategy in the Presence of Propagation Delay](https://mlsanity.com/paper/1505.05343/) (2015)
- [Effect of Bitcoin fee on transaction-confirmation process](https://mlsanity.com/paper/1604.00103/) (2016)
- [On profitability of selfish mining](https://mlsanity.com/paper/1805.08281/) (2018)
- [A Deep Dive into Blockchain Selfish Mining](https://mlsanity.com/paper/1811.08263/) (2018)
- [Selfish Mining in Ethereum](https://mlsanity.com/paper/1901.04620/) (2019)
- [Competing (Semi)-Selfish Miners in Bitcoin](https://mlsanity.com/paper/1906.04502/) (2019)
- [Machine Learning in/for Blockchain: Future and Challenges](https://mlsanity.com/paper/1909.06189/) (2019)
- [A New Theoretical Framework of Pyramid Markov Processes for Blockchain Selfish Mining](https://mlsanity.com/paper/2007.01459/) (2020)
- [Efficient MDP Analysis for Selfish-Mining in Blockchains](https://mlsanity.com/paper/2007.05614/) (2020)
- [OptChain: Optimal Transactions Placement for Scalable Blockchain Sharding](https://mlsanity.com/paper/2007.08596/) (2020)
- [Blockchain Framework for Artificial Intelligence Computation](https://mlsanity.com/paper/2202.11264/) (2022)
- [Bitcoin's Security Model Revisited](https://mlsanity.com/paper/1605.09193/) (2016)
- [Optimizing Governed Blockchains for Financial Process Authentications](https://mlsanity.com/paper/1612.00407/) (2016)
- [On profitability of stubborn mining](https://mlsanity.com/paper/1808.01041/) (2018)
- [Predicting the confirmation time of Bitcoin transactions](https://mlsanity.com/paper/1809.10596/) (2018)
- [On Profitability of Trailing Mining](https://mlsanity.com/paper/1811.09322/) (2018)
- [Fission: A Provably Fast, Scalable, and Secure Permissionless Blockchain](https://mlsanity.com/paper/1812.05032/) (2018)
- [Profitable Double-Spending Attacks](https://mlsanity.com/paper/1903.01711/) (2019)
- [Weighted Voting on the Blockchain: Improving Consensus in Proof of Stake Protocols](https://mlsanity.com/paper/1903.04213/) (2019)
- [Markov Processes in Blockchain Systems](https://mlsanity.com/paper/1904.03598/) (2019)

## Abstract (arXiv)

With rapid development of blockchain technology as well as integration of various application areas, performance evaluation, performance optimization, and dynamic decision in blockchain systems are playing an increasingly important role in developing new blockchain technology. This paper provides a recent systematic overview of this class of research, and especially, developing mathematical modeling and basic theory of blockchain systems. Important examples include (a) performance evaluation: Markov processes, queuing theory, Markov reward processes, random walks, fluid and diffusion approximations, and martingale theory; (b) performance optimization: Linear programming, nonlinear programming, integer programming, and multi-objective programming; (c) optimal control and dynamic decision: Markov decision processes, and stochastic optimal control; and (d) artificial intelligence: Machine learning, deep reinforcement learning, and federated learning. So far, a little research has focused on these research lines. We believe that the basic theory with mathematical methods, algorithms and simulations of blockchain systems discussed in this paper will strongly support future development and continuous innovation of blockchain technology.