Reducing Latency in Large-Scale Data Systems Through Intelligent Memory Tiering and Offloading Mechanisms

The exponential growth of data volumes, driven by IoT, AI, and real-time analytics, has placed unprecedented demands on large-scale data systems. A critical bottleneck in these systems is memory access latency, which directly impacts application performance and user experience. Traditional homogenous memory architectures, primarily reliant on Dynamic Random-Access Memory (DRAM), are increasingly insufficient due to cost, power, and density constraints. This paper proposes a novel framework for intelligent memory tiering and offloading that leverages a heterogeneous memory hierarchy, integrating DRAM with emerging non-volatile memory (NVM) technologies and leveraging fast storage. Our approach utilizes a lightweight machine learning-based profiler to dynamically classify data access patterns (hot, warm, cold) and an intelligent data placement engine that migrates data between memory tiers (DRAM, NVM, NVMe SSD) to minimize access latency. We also introduce a proactive offloading mechanism that pre-emptively moves data likely to be accessed by batch or analytical workloads to a high-throughput storage layer, reducing contention on the primary memory bus. A simulation-based evaluation demonstrates that our proposed framework can reduce overall tail latency by up to 45% and improve system throughput by 30% compared to traditional LRU-based caching and uniform memory architectures, while maintaining a 20% reduction in total cost of ownership (TCO). This research provides a viable pathway for designing next-generation data systems capable of meeting the low-latency requirements of modern data-intensive applications.