Performance Evaluation, Optimization and Dynamic Decision in Blockchain Systems: A Recent Overview
Quan-Lin Li, Yan-Xia Chang, Qing Wang

TL;DR
This paper systematically reviews recent research on performance evaluation, optimization, and dynamic decision-making in blockchain systems, emphasizing mathematical models, algorithms, and AI techniques to support future blockchain development.
Contribution
It provides a comprehensive overview of mathematical modeling and theoretical foundations for blockchain performance analysis and optimization, highlighting underexplored research areas.
Findings
Various mathematical models are used for performance evaluation.
Optimization techniques like linear and nonlinear programming are applied.
AI methods such as machine learning and reinforcement learning are emerging in blockchain research.
Abstract
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…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Supply Chain and Inventory Management
MethodsDiffusion
