State Management in Distributed Systems Using the Example of Multi-Version Concurrency Control (MVCC)

Brandon Vrooman

Citation: Brandon Vrooman, "State Management in Distributed Systems Using the Example of Multi-Version Concurrency Control (MVCC)", Universal Library of Innovative Research and Studies, Volume 03, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This article analyzes state management in distributed systems based on Multi-Version Concurrency Control (MVCC). The relevance of this work is driven by the growth of high-load blockchain platforms and agentic AI systems, for which scalability and deterministic state consistency are critical requirements. The novelty of the research lies in the comprehensive examination of MVCC not as a local mechanism for transaction isolation, but as a central state management layer integrated with architectures for replication, consensus, inter-shard interaction, and disaggregated memory. The paper describes modern approaches to version garbage collection, lock-free data structures, hybrid blockchain-DBMS systems, cloned control protocols, and cross-shard transactions; their limitations and areas of applicability are studied. Particular attention is paid to identifying architectural patterns and recommendations for high-performance L2 blockchains and agentic AI platforms. To achieve the set objective, methods of comparative and systemic analysis of scientific publications are employed. The conclusion describes findings regarding the role of MVCC as a state management core and formulates directions for further research. The article will be useful to engineers and researchers involved in the design of large-scale distributed, blockchain, and AI systems.


Keywords: Distributed Systems, Multi-Version Concurrency Control, Blockchain, Cloned Concurrency Control, Hybrid Blockchain-DBMS, Agentic AI Platforms, Disaggregated Memory, State Management.

Download doi https://doi.org/10.70315/uloap.ulirs.2026.0301003