METHODS FOR OPTIMIZING THE USE OF RANDOM-ACCESS MEMORY IN THE PROCESSES OF IMPROVING COMPUTER SYSTEM PERFORMANCE

Abstract

The paper examines the main methods for optimizing the use of random-access memory (RAM) in order to improve the overall performance and stability of computer systems, considering RAM as a critically important resource for ensuring high system speed and responsiveness. Memory optimization is analyzed at several complementary levels, including hardware, software, and operating-system layers, each of which contributes to efficient resource utilization.

At the hardware level, optimization techniques include increasing memory capacity and bandwidth, improving access latency, and actively managing multi-level CPU cache hierarchies. These approaches aim to reduce memory bottlenecks and improve data locality. At the software level, the paper considers the use of efficient algorithms and data structures, object pooling techniques, manual and automated memory management strategies, as well as optimization of loops, arrays, and memory allocation patterns to minimize fragmentation and excessive memory consumption.

A significant role in memory optimization is played by operating-system–level mechanisms. These include advanced memory management strategies based on Least Recently Used (LRU) policies, paging and swapping algorithms, tuning of paging file parameters, and the elimination or reduction of unnecessary background services that consume memory resources. The combined application of these techniques allows achieving a balanced trade-off between performance and resource availability.

Current trends in RAM optimization are analyzed and generalized, with particular attention to systems operating under dynamic workloads. A detailed approach to dynamic caching is proposed, which adapts to changing access patterns in real time. This approach is based on continuous monitoring of memory requests, trend analysis, and dynamic partitioning of cache space into hot, warm, and cold zones. Such adaptive cache management improves hit rates and reduces access latency under variable load conditions.

The paper demonstrates that effective RAM management is a multifaceted and complex task that requires the integration of hardware, software, and operating-system optimization methods. This is especially important for environments with limited resources, such as IoT and mobile systems, as well as for systems characterized by high computational dynamics. The proposed approaches contribute to improved performance, stability, and scalability of modern computing systems.