Optimization of energy consumption in blockchain networks through adaptive machine learning algorithms and graph representations of transactions

Abstract

This article examines the problem of high energy consumption of modern blockchain networks and suggests an innovative approach to their optimization using adaptive machine learning algorithms and graph representations of transactions. The introduction describes the relevance of the research in the context of a constant increase in the amount of data and computing resources needed to maintain the operability of distributed systems. The main hypothesis is that the integration of graph analysis methods with machine learning algorithms can reduce power consumption by optimizing the allocation of computational tasks and reducing excessive re-calculation of transactions. The methodology section describes in detail the stages of development of the proposed model. At the first stage, data is collected and preprocessed from various blockchain ecosystems, after which a graph structure of transactions is formed, which makes it possible to identify key nodes and anomalies in network traffic. Then, an adaptive machine learning algorithm is applied that is able to dynamically analyze the resulting graphs and predict the energy consumption for performing operations. The algorithm adapts to changes in network load, ensuring a balance between computing resources and power consumption. Special attention is paid to tuning the model parameters using methods of cross-validation and hyperparameter optimization. The results section demonstrates the successful testing of the proposed approach on experimental data. The comparative analysis shows that the integration of graph representations of transactions with adaptive machine learning algorithms leads to a significant reduction in power consumption, reaching up to 30% compared with traditional network management methods. The results obtained highlight the potential of using hybrid methods to optimize the operation of blockchain systems, as well as open up new prospects for further research in the field of energy efficiency of distributed computing. The discussion focuses on the practical significance of the results, discusses the limitations of the methods used and directions for future research, including the introduction of new types of algorithms and the expansion of models to account for additional variables in distributed systems. Thus, the article makes a significant contribution to the development of a scientific approach to solving energy consumption problems in blockchain networks, offering innovative methodological solutions and practical recommendations for optimizing the operation of distributed computing systems.