78 research outputs found
WLFC: Write Less in Flash-based Cache
Flash-based disk caches, for example Bcache and Flashcache, has gained
tremendous popularity in industry in the last decade because of its low energy
consumption, non-volatile nature and high I/O speed. But these cache systems
have a worse write performance than the read performance because of the
asymmetric I/O costs and the the internal GC mechanism. In addition to the
performance issues, since the NAND flash is a type of EEPROM device, the
lifespan is also limited by the Program/Erase (P/E) cycles. So how to improve
the performance and the lifespan of flash-based caches in write-intensive
scenarios has always been a hot issue. Benefiting from Open-Channel SSDs
(OCSSDs), we propose a write-friendly flash-based disk cache system, which is
called WLFC (Write Less in the Flash-based Cache). In WLFC, a strictly
sequential writing method is used to minimize the write amplification. A new
replacement algorithm for the write buffer is designed to minimize the erase
count caused by the evicting. And a new data layout strategy is designed to
minimize the metadata size persisted in SSDs. As a result, the Over-Provisioned
(OP) space is completely removed, the erase count of the flash is greatly
reduced, and the metadata size is 1/10 or less than that in BCache. Even with a
small amount of metadata, the data consistency after the crash is still
guaranteed. Compared with the existing mechanism, WLFC brings a 7%-80%
reduction in write latency, a 1.07*-4.5* increment in write throughput, and a
50%-88.9% reduction in erase count, with a moderate overhead in read
performance
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PebblesDB : building key-value stores using fragmented log-structured merge trees
Key-value stores such as LevelDB and RocksDB offer excellent write throughput, but suffer high write amplification. The write amplification problem is due to the Log-Structured Merge Trees data structure that underlies these key-value stores. To remedy this problem, this thesis presents a novel data structure that is inspired by Skip Lists, termed Fragmented Log- Structured Merge Trees (FLSM). FLSM introduces the notion of guards to organize logs (sstables or files containing the data on storage), and avoids rewriting data in the same level. Theoretically, we show how FLSM can address the problem of write amplification. We build PebblesDB, a high-performance key-value store, by modifying HyperLevelDB to use the FLSM data structure. We evaluate PebblesDB using micro-benchmarks and show that for write-intensive workloads, PebblesDB reduces write amplification by 2.4-3x compared to RocksDB, while increasing write throughput by 6.7x. We evaluate PebblesDB extensively under a variety of benchmarks, workload patterns, and environmental factors and analyze how it performs in different scenarios. We modify two widely-used NoSQL stores, MongoDB and HyperDex, to use PebblesDB as their underlying storage engine. Evaluating these applications using the YCSB benchmark shows that throughput is increased by 18-105% when using PebblesDB (compared to their default storage engines) while write IO is decreased by 35-55%.Computer Science
Assise: Performance and Availability via NVM Colocation in a Distributed File System
The adoption of very low latency persistent memory modules (PMMs) upends the
long-established model of disaggregated file system access. Instead, by
colocating computation and PMM storage, we can provide applications much higher
I/O performance, sub-second application failover, and strong consistency. To
demonstrate this, we built the Assise distributed file system, based on a
persistent, replicated coherence protocol for managing a set of
server-colocated PMMs as a fast, crash-recoverable cache between applications
and slower disaggregated storage, such as SSDs. Unlike disaggregated file
systems, Assise maximizes locality for all file IO by carrying out IO on
colocated PMM whenever possible and minimizes coherence overhead by maintaining
consistency at IO operation granularity, rather than at fixed block sizes.
We compare Assise to Ceph/Bluestore, NFS, and Octopus on a cluster with Intel
Optane DC PMMs and SSDs for common cloud applications and benchmarks, such as
LevelDB, Postfix, and FileBench. We find that Assise improves write latency up
to 22x, throughput up to 56x, fail-over time up to 103x, and scales up to 6x
better than its counterparts, while providing stronger consistency semantics.
Assise promises to beat the MinuteSort world record by 1.5x
An In-Depth Investigation of Performance Characteristics of Hyperledger Fabric
Private permissioned blockchains, such as Hyperledger Fabric, are widely
deployed across the industry to facilitate cross-organizational processes and
promise improved performance compared to their public counterparts. However,
the lack of empirical and theoretical results prevent precise prediction of the
real-world performance. We address this gap by conducting an in-depth
performance analysis of Hyperledger Fabric. The paper presents a detailed
compilation of various performance characteristics using an enhanced version of
the Distributed Ledger Performance Scan. Researchers and practitioners alike
can use the results as guidelines to better configure and implement their
blockchains and utilize the DLPS framework to conduct their measurements
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