Log-structured-merge tree or LSM-tree is a technological underpinning in key-value (KV) stores to support a wide range of performance-critical applications. By conducting data re-organization in the background by virtue of compaction operations, the KV stores have the potential to swiftly service write requests with sequential batched disk writes and read requests for KV items constantly sorted by the compaction. Compaction demands high I/O bandwidth and CPU speed to facilitate quality service to user read/write requests. With the emergence of high-speed SSDs, CPUs are increasingly becoming a performance bottleneck. To mitigate the bottleneck limiting the KV-store’s performance and that of the applications supported by the store, we propose a system - gLSM - to leverage GPGPU to remarkably accelerate the compaction operations. gLSM fully utilizes the parallelism and computational capability inside GPGPUs to improve the compaction performance. We design a driver framework to parallelize compaction operations handled between a pair of CPU and GPGPU. We employ data independence and GPGPU-orient radix-sorting algorithm to concurrently conduct compaction. A key-value separation method is devised to slash the transfer of data volume from CPU-side memory to the GPGPU counterpart. The results reveal that gLSM improves the throughput and compaction bandwidth by up to a factor of 2.9 and 26.0, respectively, compared with the four state-of-the-art KV stores. gLSM also reduces the write latency by 73.3%. gLSM exhibits a performance improvement by up to 45% compared against its variant where there are no KV separation and collaboration sort modules.

LSM-tree-based key-value stores (KV stores) convert random-write requests to sequence-write ones to achieve high I/O performance. Meanwhile, compaction operations in KV stores update SSTables in forms of reorganizing low-level data components to high-level ones, thereby guaranteeing an orderly data layout in each component. Repeated writes caused by compaction (a.k.a. write amplification) impacts I/O bandwidth and overall system performance. Near-data processing (NDP) is one of the effective approaches to addressing this write-amplification issue. Most NDP-based techniques adopt synchronous parallel schemes to perform a compaction task on both the host and its NDP-enabled device. In synchronous parallel compaction schemes, the execution time of compaction is determined by a subsystem that has lower compaction performance coupled by under-utilized computing resources in a NDP framework. To solve this problem, we propose an asynchronous parallel scheme named PStore to improve the compaction performance in KV stores. In PStore, we designed a multi-tasks queue and three priority-based scheduling methods. PStore elects proper compaction tasks to be offloaded in host- and device-side compaction modules. Our proposed cross-leveled compaction mechanism mitigates write amplification induced by asynchronous compaction. PStore featured with the asynchronous compaction mechanism fully utilizes computing resources in both host- and device-side subsystems. Compared with the two popular synchronous compaction modes based on KV stores (TStore and LevelDB), our PStore immensely improves the throughput by up to a factor of 14 and 10.52 with an average of a factor of 2.09 and 1.73, respectively.

LSM-tree-based key-value stores (KV stores) render high-performance read/write services to data-intensive applications. KV stores employ an SSTable-based Coarse-Grained Compaction (CGC) mechanism, which involves a huge amount of data that do not need to be updated, thereby bringing a high write amplification (WA) and long tail latency. To address this issue, we propose a Fine-Grained Compaction (FGC) mechanism anchored on a Log-Structured patched-Merge tree (LSpM-tree) - a new data organization that averts rewriting irrelevant data into disks amid compaction. A cluster, the basic unit in FGC, encloses several patches and a redirection table, where each patch has an array of KV regions. We devise three compaction modes powered by the LSpM-tree, and we implement a high-performance key-value store, named FGKV. The extensive experiments show that FGKV improves the random-write throughput by up to 121%, 36.8%, 38.6%, and 15.2% compared with LevelDB, RocksDB, LDC, and ALDC, respectively. FGKV lowers the WA of the alternative KV stores by up to 50%. FGKV boosts read performance by up to 122%, 51.4%, 96.6%, and 368%, respectively, and FGKV curbs the 99th percentile latency of LevelDB, RocksDB, LDC, and ALDC by up to 78.2%, 77.6%, 78.3%, and 73.1% under YCSB A, respectively. Moreover, FGKV is readily extended to the other KV stores

Remote control malwares enable cyber attackers to achieve command and control over victim hosts, which are widely employed in ransomware attacks and espionage operations, jeopardizing personal privacy and state security. To effectively detect such malicious traffics holds high practical value. However, prior works have not adequately addressed the task due to challenges of encrypted traffics with misleading contents, incomplete sessions, and limited labels. To overcome these limitations, in this paper, we propose TrafCL, a contrastive learning framework for robust encrypted malicious traffic detection. In TrafCL, we first generate incomplete variants for the input session by Session Augmentation, then extract explicit session features with excluding misleading traffic contents by Triple-aspect Session Feature Extraction, and obtain session representations by Co-attention Session Encoder which fuses triple-aspect session features with capturing their interdependence. After that, we use a projection head to obtain final representations. TrafCL is pre-trained using unlabeled data to learn close representations for complete sessions and their incomplete variants, then fine-tuned on labeled data to detect encrypted malicious traffics. Experiment results show that TrafCL outperforms the best baseline by 11.35% and 6.71% in F1-scores on two datasets respectively.

NAND Flash-based solid-state drives (SSDs) have gained widespread usage in data storage thanks to their exceptional performance and low power consumption. The computational capability of SSDs has been elevated to tackle complex algorithms. Inside an SSD, a DRAM cache for frequently accessed requests reduces response time and write amplification (WA), thereby improving SSD performance and lifetime. Existing caching schemes, based on temporal locality, overlook its variations, which potentially reduces cache hit rates. Some caching schemes bolster performance via flash-aware techniques but at the expense of the cache hit rate. To address these issues, we propose a random forest machine learning Classifier-empowered Cache scheme named CCache, where I/O requests are classified into critical, intermediate, and non-critical ones according to their access status. After designing a machine learning model to predict these three types of requests, we implement a trie-level linked list to manage the cache placement and replacement. CCache safeguards critical requests for cache service to the greatest extent, while granting the highest priority to evicting request accessed by non-critical requests. CCache – considering chip state when processing non-critical requests – is implemented in an SSD simulator (SSDSim). CCache outperforms the alternative caching schemes, including LRU, CFLRU, LCR, NCache, ML_WP, and CCache_ANN, in terms of response time, WA, erase count, and hit ratio. The performance discrepancy between CCache and the OPT scheme is marginal. For example, CCache reduces the response time of the competitors by up to 41.9% with an average of 16.1%. CCache slashes erase counts by a maximum of 67.4%, with an average of 21.3%. The performance gap between CCache and and OPT is merely 2.0%-3.0%.