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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. 

The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT research. We examine AIoT literature related to sensing, computing, and networking & communication, which form the three key components of AIoT. In addition to advancements in these areas, we review domain-specific AIoT systems that are designed for various important application domains. We have also created an accompanying GitHub repository, where we compile the papers included in this survey: https://github.com/AIoT-MLSys-Lab/AIoT-Survey. This repository will be actively maintained and updated with new research as it becomes available. As both IoT and AI become increasingly critical to our society, we believe that AIoT is emerging as an essential research field at the intersection of IoT and modern AI. It is our hope that this survey will serve as a valuable resource for those engaged in AIoT research and act as a catalyst for future explorations to bridge gaps and drive advancements in this exciting field. 

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.