Hybrid-RAID: Tri-dimensional Optimization Framework for SSD-HDD Heterogeneous Storage Systems

The exponential growth of data-intensive applications has exacerbated the limitations of homogeneous storage systems, necessitating hybrid architectures that synergistically integrate Solid State Drives (SSDs) and Hard Disk Drives (HDDs).However, existing solutions often prioritize optimization along a single dimension, neglecting the intricate trade-offs between performance, reliability, and cost.To address this challenge, this paper proposes Hybrid-Redundant Array of Independent Disks (RAID), a novel tridimensional optimization framework specifically designed for heterogeneous SSD-HDD storage systems.Our framework synthesizes five canonical and modern RAID architectures (including RAID-x and HPDA, High-Performance Data Analytics) into a unified, workload-aware decision engine.It intelligently selects the optimal configuration by evaluating each workload's characteristics against a novel Performance-Reliability-Cost cube model.Extensive evaluations using the Storage simulator and diverse case studies demonstrate that Hybrid-RAID achieves significant improvements over traditional singletier RAID.Specifically, it reduces the 95th-percentile latency by 22%, doubles the mean-time-to-data-loss (MTTDL), and lowers the five-year total cost of ownership (TCO) by 18%.Hybrid-RAID thus provides a robust, adaptive, and cost-effective blueprint for building efficient storage systems in the era of big data.