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Tiered memory systems often rely on access frequency ("hotness") to guide data placement. However, hot data is not always performance-critical, limiting the effectiveness of hotness-based policies. We introduce amortized offcore latency (AOL), a novel metric that precisely captures the true performance impact of memory accesses by accounting for memory access latency and memory-level parallelism (MLP). Leveraging AOL, we present two powerful tiering mechanisms: SOAR, a profile-guided allocation policy that places objects based on their performance contribution, and ALTO, a lightweight page migration regulation policy to eliminate unnecessary migrations. SOAR and ALTO outperform four state-of-the-art tiering designs across a diverse set of workloads by up to 12.4\texttimes{}, while underperforming in a few cases by no more than 3\%. 

Many datasets in real life are complex and dynamic, that is, their key densities are varied over the whole key space and their key distributions change over time. It is challenging for an index structure to efficiently support all key operations for data management, in particular, search, insert, and scan, for such dynamic datasets. In this article, we present DyTIS (Dynamic dataset Targeted Index Structure), an index that targets dynamic datasets. DyTIS, although based on the structure of Extendible hashing, leverages the CDF of the key distribution of a dataset, and learns and adjusts its structure as the dataset grows. The key novelty behind DyTIS is to group keys by the natural key order and maintain keys in sorted order in each bucket to support scan operations within a hash index. We also define what we refer to as a dynamic dataset and propose a means to quantify its dynamic characteristics. Our experimental results show that DyTIS provides higher performance than the state-of-the-art learned index for the dynamic datasets considered. We also analyze the effects of the dynamic characteristics of datasets, including sequential datasets, as well as the effect of multiple threads on the performance of the indexes.