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Emerging persistent memory in commodity hardware allows byte-granular accesses to persistent state at memory speeds. However, to prevent inconsistent state in persistent memory due to unexpected system failures, different write-semantics are required compared to volatile memory. Transaction-based library solutions for persistent memory facilitate the atomic modification of persistent data in languages where memory is explicitly managed by the programmer, such as C/C++. For languages that provide extended capabilities like automatic memory management, a more native integration into the language is needed to maintain the high level of memory abstraction. It is shown in this paper how persistent software transactional memory (PSTM) can be tightly integrated into the runtime system of Haskell to atomically manage values of persistent transactional data types. PSTM has a clear interface and semantics extending that of software transactional memory (STM). Its integration with the language’s memory management retains features like garbage collection and allocation strategies, and is fully compatible with Haskell's lazy execution model. Our PSTM implementation demonstrates competitive performance with low level libraries and trivial portability of existing STM libraries to PSTM. The implementation allows further interesting use cases, such as persistent memoization and persistent Haskell expressions.

All-flash storage (AFS) systems have become an essential infrastructure component to support enterprise applications, where sub-millisecond latency and very high throughput are required. Nevertheless, the price per capacity ofsolid-state drives (SSDs) is relatively high, which has encouraged system architects to adoptdata reduction techniques, mainlydeduplication andcompression, in enterprise storage solutions. To provide higher reliability and performance, SSDs are typically grouped usingredundant array of independent disk (RAID) configurations. Data reduction on top of RAID arrays, however, adds I/O overheads and also complicates the I/O patterns redirected to the underlying backend SSDs, which invalidates the best-practice configurations used in AFS. Unfortunately, existing works on the performance of data reduction do not consider its interaction and I/O overheads with other enterprise storage components including SSD arrays and RAID controllers. In this paper, using a real setup with enterprise-grade components and based on the open-source data reduction module RedHat VDO, we reveal novel observations on the performance gap between the state-of-the-art and the optimal all-flash storage stack with integrated data reduction. We therefore explore the I/O patterns at the storage entry point and compare them with those at the disk subsystem. Our analysis shows a significant amount of I/O overheads for guaranteeing consistency and avoiding data loss through data journaling, frequent small-sized metadata updates, and duplicate content verification. We accompany these observations with cross-layer optimizations to enhance the performance of AFS, which range from deriving new optimal hardware RAID configurations up to introducing changes to the enterprise storage stack. By analyzing the characteristics of I/O types and their overheads, we propose three techniques: (a) application-aware lazy persistence, (b) a fast, read-only I/O cache for duplicate verification, and (c) disaggregation of block maps and data by offloading block maps to a very fast persistent memory device. By consolidating all proposed optimizations and implementing them in an enterprise AFS, we show 1.3× to 12.5× speedup over the baseline AFS with 90% data reduction, and from 7.8× up to 57× performance/cost improvement over an optimized AFS (with no data reduction) running applications ranging from 100% read-only to 100% write-only accesses.

Data reduction technologies have proven their effectiveness to decrease the ever-growing demands on storage system capacities, but also introduce new complexity in the system I/O stack that can easily invalidate well-known best practices. In this paper, we conduct an extensive set of experiments on an enterprise all-flash storage (AFS) system equipped with an open-source data reduction module, i.e., RedHat VDO, and reveal novel observations on the performance gap between the state-of-the-art and the optimal AFS stack with integrated data reduction. We then offer cross-layer optimizations to enhance the performance of AFS, which range from deriving new optimal hardware RAID configurations up to modifications of the enterprise storage stack tailored to the major bottlenecks observed. By implementing all proposed optimizations in an enterprise AFS, we show up to 12.5x speedup over the baseline AFS with integrated data reduction, and up to 57x performance/cost improvement over an optimized AFS (with no data reduction) for 100% write, high-reduction workload scenarios.