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Cache systems are widely used to speed up data retrieving. Modern HPC, data analytics, and AI/ML workloads generate vast, multi-dimensional datasets, and those data are accessed via complex queries. However, the probability of requesting the exact same data across different queries is low, leading to limited performance improvement when a traditional key-value cache is applied. In this paper, we present Mosaic-Cache, a proactive and general caching framework that enables applications with efficient partial overlapped data reuse through novel overlap-aware cache interfaces for fast content-level reuse. The core components include a metadata manager leveraging customizable indexing for fast overlap lookups, an adaptive fetch planner for dynamic cache-to-storage decisions, and an async merger to reduce cache fragmentation and redundancy. Evaluations on real-world HPC datasets show that Mosaic-Cache improves overall performance by up to 4.1× over traditional key-value-based cache while adding minimal overhead in worst-case scenarios. 

Optimizing LSM-based Key-Value Stores (LSM-KVS) for disaggregated storage is essential to achieve better resource utilization, performance, and flexibility. Most of the existing studies focus on offloading the compaction to the storage nodes to mitigate the performance penalties caused by heavy network traffic between computing and storage. However, several critical issues are not addressed including the strong dependency between offloaded compaction and LSM-KVS, resource load-balancing, compaction scheduling, and complex transient errors. To address the aforementioned issues and limitations, in this paper, we propose CaaS-LSM, a novel disaggregated LSM-KVS with a new idea of Compaction-as-a-Service. CaaS-LSM brings three key contributions. First, CaaS-LSM decouples the compaction from LSM-KVS and achieves stateless execution to ensure high flexibility and avoid coordination overhead with LSM-KVS. Second, CaaS-LSM introduces a performance- and resource-optimized control plane to guarantee better performance and resource utilization via an adaptive run-time scheduling and management strategy. Third, CaaS-LSM addresses different levels of transient and execution errors via sophisticated error-handling logic. We implement the prototype of CaaS-LSM based on RocksDB and evaluate it with different LSM-based distributed databases (Kvrocks and Nebula). In the storage disaggregated setup, CaaS-LSM achieves up to 8X throughput improvement and reduces the P99 latency up to 98% compared with the conventional LSM-KVS, and up to 61% of improvement compared with state-of-the-art LSM-KVS optimized for disaggregated storage.