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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. 

Log-Structured Merge-tree-based Key-Value Stores (LSM-KVS) are widely used to support modern, high-performance, data-intensive applications. In recent years, with the trend of deploying and optimizing LSM-KVS from monolith to Disaggregated Storage (DS) setups, the confidentiality of LSM-KVS persistent data (e.g., WAL and SST files) is vulnerable to unauthorized access from insiders and external attackers and must be protected using encryption. Existing solutions lack a high-performance design for encryption in LSM-KVS, often focus on in-memory data protection with overheads of 3.4-32.5x, and lack the scalability and flexibility considerations required in DS deployments. This paper proposes two novel designs to address the challenges of providing robust security for persistent components of LSM-KVS while maintaining high performance in both monolith and DS deployments - a simple and effective instance-level design suitable for monolithic LSM-KVS deployments, andSHIELD,a design that embeds encryption into LSM-KVS components for minimal overhead in both monolithic and DS deployment. We achieve our objective through three contributions: (1) A fine-grained integration of encryption into LSM-KVS write path to minimize performance overhead from exposure-limiting practices like using unique encryption keys per file and regularly re-encrypting using new encryption keys during compaction, (2) Mitigating performance degradation caused by recurring encryption of Write-Ahead Log (WAL) writes by using a buffering solution and (3) Extending confidentiality guarantees to DS by designing a metadata-enabled encryption-key-sharing mechanism and a secure local cache for high scalability and flexibility. We implement both designs on RocksDB, evaluating them in monolithic and DS setups while showcasing an overhead of 0-32% for the instance-level design and 0-36% for SHIELD. 

Data deduplication relies on a chunk index to identify the redundancy of incoming chunks. As backup data scales, it is impractical to maintain the entire chunk index in memory. Consequently, an index lookup needs to search the portion of the on-storage index, causing a dramatic regression of index lookup throughput. Existing studies propose to search a subset of the whole index (partial index) to limit the storage I/Os and guarantee a high index lookup throughput. However, several core factors of designing partial indexing are not fully exploited. In this paper, we first comprehensively investigate the trade-offs of using different meta-groups, sampling methods, and meta-group selection policies for a partial index. We then propose a Collaborative Partial Index (CPI) which takes advantage of two meta-groups including recipe-segment and container-catalog to achieve more efficient and effective unique chunk identification. CPI further introduces a hook-entry sharing technology and a two-stage eviction policy to reduce memory usage without hurting the deduplication ratio. According to evaluation, with the same constraints of memory usage and storage I/O, CPI achieves a 1.21x-2.17x higher deduplication ratio than the state-of-the-art partial indexing schemes. Alternatively, CPI achieves 1.8X-4.98x higher index lookup throughput than others when the same deduplication ratio is achieved. Compared with full indexing, CPI's maximum deduplication ratio is only 4.07% lower but its throughput is 37.1x - 122.2x of that of full indexing depending on different storage I/O constraints in our evaluation cases. 

This paper aims to design and implement a radio device capable of detecting a person’s handwriting through a wall. Although there is extensive research on radio frequency (RF) based human activity recognition, this task is particularly challenging due to the through-wall requirement and the tiny-scale handwriting movements. To address these challenges, we present RadSee—a 6 GHz frequency modulated continuous wave (FMCW) radar system designed for detecting handwriting content behind a wall. RadSee is realized through a joint hardware and software design. On the hardware side, RadSee features a 6 GHz FMCW radar device equipped with two custom-designed, high-gain patch antennas. These two antennas provide a sufficient link power budget, allowing RadSee to “see” through most walls with a small transmission power. On the software side, RadSee extracts effective phase features corresponding to the writer’s hand movements and employs a bidirectional LSTM (BiLSTM) model with an attention mechanism to classify handwriting letters. As a result, RadSee can detect millimeter-level handwriting movements and recognize most letters based on their unique phase patterns. Additionally, it is resilient to interference from other moving objects and in-band radio devices. We have built a prototype of RadSee and evaluated its performance in various scenarios. Extensive experimental results demonstrate that RadSee achieves 75% letter recognition accuracy when victims write 62 random letters, and 87% word recognition accuracy when they write articles.