Read-modify-writes (RMWs) are increasingly observed in practical key-value (KV) storage workloads to support fine-grained updates. To make RMWs efficient, one approach is to write deltas (i.e., changes to current values) to the log-structured merge-tree (LSM-tree), yet it increases the read overhead caused by retrieving and combining a chain of deltas. We propose a notion called key-delta (KD) separation to support efficient reads and RMWs in LSM-tree KV stores under RMW-intensive workloads. KD separation aims to store deltas in separate storage areas and group deltas into storage units called buckets, such that all deltas of a key are kept in the same bucket and can be all together accessed in subsequent reads. To this end, we build KDSep, a middleware layer that realizes KD separation and integrates KDSep into state-of-the-art LSM-tree KV stores (e.g., RocksDB and BlobDB). We show that KDSep achieves significant I/O throughput gains and read latency reduction under RMW-intensive workloads while preserving the efficiency in general workloads.

Privacy preservation is critical for neural network inference, which often involves collaborative execution of different parties to make predictions on sensitive data based on sensitive neural network models. However, the expensive cryptographic operations of privacy preservation also pose performance chal-lenges to neural network inference. We address this performance-security tension by designing PP-Stream, a distributed stream processing system for high-performance privacy-preserving neural network inference. PP-Stream adopts hybrid privacy-preserving mechanisms for linear and non-linear operations of neural network inference. It treats inference data as real-time data streams, and parallelizes the inference operations across multiple pipelined stages that are executed by multiple servers and threads. It also solves the load-balanced resource allocation across servers and threads as an optimization problem. We prototype PP-Stream and show via testbed experiments that it achieves low inference latencies on various neural network models.

Traditional disk failure prediction approaches struggle to scale with data growth, as they treat data as a whole collection to obtain the global data view for preprocessing and training. Existing distributed machine learning and stream mining systems are designed to scale data processing, particularly for training. However, scaling disk failure prediction faces challenges in the scalability of preprocessing, including additional data movements from data collection to training, data inflation during preprocessing, and multiple-to-multiple data allocation. To address these challenges, we present SCALEDFP, a general framework for scaling disk failure prediction via multi-source stream mining based on three techniques: near-data preprocessing, random downsampling, and training data allocation. SCALEDFP scales disk failure prediction with the number of data sources. It achieves significant throughput gains of preprocessing and training with comparable prediction accuracy against a state-of-the-art disk failure prediction approach that collects data in a centralized place.

Persistent key-value (KV) stores are mainly designed based on the Log-Structured Merge-tree (LSM-tree), yet they suffer from large read and write amplifications, especially when KV stores grow in size. Existing design optimizations for LSM-tree-based KV stores often make certain trade-offs and fail to simultaneously improve both the read and write performance on large KV stores without sacrificing scan performance. We design UniKV, which unifies the key design ideas of hash indexing and the LSM-tree in a single system. Specifically, UniKV leverages data locality to differentiate the indexing management of KV pairs. It also develops multiple techniques (e.g., merge with partial KV separation, dynamic range partitioning) to tackle the issues caused by unifying the indexing techniques, so as to simultaneously improve the performance in reads and writes. Furthermore, it proposes a parallel optimization scheme to manage partitions in parallel and develops multiple strategies to optimize the scan performance. Experiments show that UniKV significantly outperforms several state-of-the-art KV stores (e.g., LevelDB, RocksDB, PebblesDB and Titan) in overall throughput under read-write mixed workloads.

Persistent key-value (KV) stores are mainly designed based on the Log-Structured Merge-tree (LSM-tree), which suffer from large read and write amplifications, especially when KV stores grow in size. Existing design optimizations for LSM-tree-based KV stores often make certain trade-offs and fail to simultaneously improve both the read and write performance on large KV stores without sacrificing scan performance. We design UniKV, which unifies the key design ideas of hash indexing and the LSM-tree in a single system. Specifically, UniKV leverages data locality to differentiate the indexing management of KV pairs. It also develops multiple techniques to tackle the issues caused by unifying the indexing techniques, so as to simultaneously improve the performance in reads, writes, and scans. Experiments show that UniKV significantly outperforms several state-of-the-art KV stores (e.g., LevelDB, RocksDB, HyperLevelDB, and PebblesDB) in overall throughput under read-write mixed workloads.

With the advent of cellular network technologies, mobile Internet access becomes the norm in everyday life. In the meantime, the complaints made by subscribers about unsatisfactory cellular network access also become increasingly frequent. From a network operator's perspective, achieving accurate and timely cellular network diagnosis about the causes of the complaints is critical for both improving subscriber-perceived experience and maintaining network robustness. We present the Intelligent Customer Care Assistant (ICCA), a distributed fault classification system that exploits a data-driven approach to perform large-scale cellular network diagnosis. ICCA takes massive network data as input, and realizes both offline model training and online feature computation to distinguish between user and network faults in real time. ICCA is currently deployed in a metropolitan LTE network in China that is serving around 50 million subscribers. We show via evaluation that ICCA achieves high classification accuracy (85.3%) and fast query response time (less than 2.3 seconds). We also report our experiences learned from the deployment.

Fault tolerance is critical for distributed stream processing systems, yet achieving error-free fault tolerance often incurs substantial performance overhead. We present AF-Stream , a distributed stream processing system that addresses the trade-off between performance and accuracy in fault tolerance. AF-Stream builds on a notion called approximate fault tolerance , whose idea is to mitigate backup overhead by adaptively issuing backups, while ensuring that the errors upon failures are bounded with theoretical guarantees. Our AF-Stream design provides an extensible programming model for incorporating general streaming algorithms, and also exports only few threshold parameters for configuring approximation fault tolerance. Experiments on Amazon EC2 show that AF-Stream maintains high performance (compared to no fault tolerance) and high accuracy after multiple failures (compared to no failures) under various streaming algorithms.