DEVELOPMENT OF A SCALABLE DISTRIBUTED ARCHITECTURE FOR MASSIVELY MULTIPLAYER ONLINE SYSTEMS
DOI:
https://doi.org/10.35546/kntu2078-4481.2025.4.3.11Keywords:
MMO, distributed systems, scalability, load balancing, server architecture, machine learning.Abstract
In modern gaming and digital technology industries, there is an increasing demand for systems capable of maintaining stable performance under extremely high user concurrency. Massively multiplayer online (MMO) systems are complex software and hardware infrastructures that must guarantee not only rapid data exchange between clients but also consistency of shared states, scalability, fault tolerance, and minimal response latency. Traditional centralized architectures fail to meet these requirements because their performance is limited by the hardware resources of a single node, resulting in service degradation under peak loads. A dynamic balancing algorithm is proposed that combines local load metrics with global system state analysis. Using the Machine Learning Load Predictor (MLLP) module, the algorithm predicts future peak loads, allowing preemptive redistribution of computational resources between servers. The software implementation of the algorithm was performed in Python using multiprocessing and asynchronous message queue technologies. A comparative performance analysis showed that the developed architecture reduces average latency by 57% compared to conventional centralized MMO architectures and increases computational resource utilization efficiency by more than 230%. These results confirm the feasibility of applying distributed architectures in modern real-time gaming and simulation platforms. The practical significance of the research lies in the potential use of the proposed architecture for building high-load MMO servers, distributed simulations, and interactive educational or virtual environments. Future research will focus on improving state synchronization models, developing hybrid balancing strategies, and integrating self-learning systems for autonomous resource management.
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