Algorithmic and mathematical methods for optimizing data storage and processing in modern IT infrastructure

DOI: 10.31673/2412-9070.2026.318105

Abstract

The rapid growth of data volumes in modern information systems, driven by the active development of digital services, cloud technologies, the Internet of Things, and big data platforms, significantly complicates data storage and processing processes. Efficient utilization of memory resources, reduction of data access latency, and ensuring high performance, fault tolerance, and scalability have become key challenges for corporate, distributed, and cloud-based IT infrastructures. Under these conditions, algorithmic and mathematical optimization methods become particularly relevant, as they enable the formalization of data management processes, reduction of overhead costs, and improvement of the overall efficiency of information systems of various scales.
This paper presents a survey of modern algorithmic and mathematical methods for optimizing data storage and processing applied in database management systems, distributed computing environments, big data platforms, and cloud infrastructures. The main classes of optimization problems related to data placement and migration, indexing, caching, replication, load balancing, and resource management are considered. Mathematical models used to formalize these problems are analyzed, including mathematical programming methods, graph-based, stochastic, and probabilistic models.
Special attention is paid to algorithmic approaches for solving optimization problems, including exact, heuristic, and metaheuristic methods, as well as machine learning and reinforcement learning algorithms. A classification of existing approaches is proposed, and their advantages and limitations are identified in terms of accuracy, computational complexity, adaptability, and practical software implementation. Prospects for further development of optimization methods are outlined in the context of increasing IT infrastructure complexity, growing data volumes, and the adoption of intelligent data management systems.

Keywords: data storage optimization, algorithms, mathematical models, IT infrastructure, data-bases, cloud computing, big data.