Advances in memory technologies ( e . g ., HBM, DRAM, NVM) and interconnects ( e . g ., CXL) have significantly enhanced the flexibility of utilizing memory resources in modern computer systems. As this trend continues, memory resources are poised to become fully composable in the near future. This increasing flexibility also accelerates the demand for effective tiered memory systems capable of performing well across diverse scenarios and workloads. However, the effectiveness of existing tiered memory systems heavily depends on system configuration (e.g., the ratio of fast tier to capacity tier), memory access patterns, and page sizes (base vs. huge). Their reliance on simple heuristics and static thresholds for detecting page hotness, and limited consideration of page sizes, results in suboptimal (often pathological) page placement decisions. To build a robust and effective system, tiered memory management must holistically account for overall memory access patterns in conjunction with the tiering environment. We present Memtis , a tiered memory system that adopts informed decision-making for page placement and page size determination. Memtis leverages access distribution of allocated pages to optimally approximate the hot data set to the fast tier capacity. Moreover, Memtis dynamically determines the page size that allows applications to use huge pages while avoiding their drawbacks by detecting inefficient use of fast tier memory and splintering them if necessary. To further enhance its practicality across diverse scenarios, Memtis effectively supports the dynamic allocation of fast tier memory if there is a specific performance requirement, such as a target hit ratio. Our evaluation shows that Memtis outperforms existing tiered memory systems under various workloads and tiering configurations by up to 169.0%, showing its robustness.