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CompoundFS: Compounding I/O operations in firmware file systems 12

CompoundFS: Compounding I/O operations in firmware file syst...

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12th usenix Workshop on hot topics in storage and file systems, hotstorage 2020, co-located withusenix ATC 2020

作者： Ren, Yujie Zhang, Jian Kannan, Sudarsun Rutgers University United States ShanghaiTech University China

We introduce CompoundFS, a firmware-level file system that combines multiple filesystem I/O operations into a single compound operation to reduce software overheads. the overheads include frequent interaction (e.g., system calls), data copy, and the VFS overheads between user-level application and the storage stack. Further, to exploit the compute capability of modern storage, CompoundFS also provides a capability to offload simple I/O data processing operations to the device-level CPUs, which further provides an opportunity to reduce interaction with the filesystem, move data, and free-up host CPU for other operations. Preliminary evaluation of CompoundFS against the state-of-the-art user-level, kernel-level, and firmware-level file systems using microbenchmarks and a real-world application shows up to 178% and 75% performance gains, respectively. © hotstorage 2020 - 12th usenix Workshop on hot topics in storage and file systems, co-located with usenix ATC 2020. All rights reserved.

关键词： Firmware

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Prefetching in hybrid main memory systems 12

Prefetching in hybrid main memory systems

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12th usenix Workshop on hot topics in storage and file systems, hotstorage 2020, co-located withusenix ATC 2020

作者： Subisha, V. Gohil, Varun Ujjainkar, Nisarg Awasthi, Manu Indian Institute of Technology Gandhinagar India Ashoka University India

the architecture of main memory has experienced a paradigm shift in recent years, with non volatile memory technologies (NVM) like Phase Change Memory (PCM) being incorporated into the hierarchy at the same level as DRAM. this transformation is being carried out by either splitting the memory address across two or more memory technologies, or using a faster technology with higher lifetimes, typically the DRAM, as a cache for the higher capacity, albeit slower main memory made up of a NVM. Design of such hybrid architectures remains an active area of research from the perspective of DRAM-as-a-cache design, since DRAM could quickly become the bottleneck, as cache lookups require multiple accesses for reading tag and data. In this paper, we augment the DRAM-as-a-cache model with a novel DRAM cache prefetcher that builds on state of the art Alloy Cache. the new DRAM cache architecture allows for prefetching data at both cacheline and page granularities from the NVM, and as a result, provides upto a maximum of 2 performance improvement over a state of the art baseline. © hotstorage 2020 - 12th usenix Workshop on hot topics in storage and file systems, co-located with usenix ATC 2020. All rights reserved.

关键词： Dynamic random access storage

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It's time to revisit LRU vs. FIFO 12

It's time to revisit LRU vs. FIFO

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12th usenix Workshop on hot topics in storage and file systems, hotstorage 2020, co-located withusenix ATC 2020

作者： Eytan, Ohad Harnik, Danny Ofer, Effi Friedman, Roy Kat, Ronen IBM Research Technion

We revisit the question of the effectiveness of the popular LRU cache eviction policy versus the FIFO heuristic which attempts to give an LRU like behavior. Several past works have considered this question and commonly stipulated that while FIFO is much easier to implement, the improved hit ratio of LRU outweighs this. We claim that two main trends call for a reevaluation: new caches such as front-ends to cloud storage have very large scales and this makes managing cache metadata in RAM no longer feasible;and new workloads have emerged that possess different characteristics. We model the overall cost of running LRU and FIFO in a very large scale cache and evaluate this cost using a number of publicly available traces. Our main evaluation workload is a new set of traces that we collected from a large public cloud object storage service and on this new trace FIFO exhibits better overall cost than LRU. We hope that these observations reignite the evaluation of cache eviction policies under new circumstances and that the new traces, that we intend to make public, serve as a testing ground for such work. © hotstorage 2020 - 12th usenix Workshop on hot topics in storage and file systems, co-located with usenix ATC 2020. All rights reserved.

关键词： Random access storage

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Neural trees: Using neural networks as an alternative to binary comparison in classical search trees 12

Neural trees: Using neural networks as an alternative to bin...

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12th usenix Workshop on hot topics in storage and file systems, hotstorage 2020, co-located withusenix ATC 2020

作者： Santry, Douglas NetApp

Binary comparison, the basis of the venerable B Tree, is perhaps the most successful operator for indexing data on secondary storage. We introduce a different technique, called Neural Trees, that is based on neural networks. Neural Trees increase the fan-out per byte of a search tree by up to 40% compared to B Trees. Increasing fan-out reduces memory demands and leads to increased cacheability while decreasing height and media accesses. A Neural Tree also permits search path layout policies that divorce a key's value from its physical location in a data structure. this is an advantage over the total ordering required by binary comparison, which totally determines the physical location of keys in a tree. Previous attempts to apply machine learning to indices are based on learning the data directly, which renders insertion too expensive to be supported. the Neural Tree is a hybrid scheme using a tree of small neural networks to learn search paths instead of the data directly. Neural Trees can efficiently handle a general read/write workload. We evaluate Neural Trees with weeks of traces from production storage and SPC1 workloads to demonstrate their viability. © hotstorage 2020 - 12th usenix Workshop on hot topics in storage and file systems, co-located with usenix ATC 2020. All rights reserved.

关键词： Digital storage

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SelectiveEC: Selective reconstruction in erasure-coded storage systems 12

SelectiveEC: Selective reconstruction in erasure-coded stora...

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12th usenix Workshop on hot topics in storage and file systems, hotstorage 2020, co-located withusenix ATC 2020

作者： Xu, Liangliang Lyu, Min Li, Qiliang Xie, Lingjiang Xu, Yinlong University of Science and Technology of China Anhui Province Key Laboratory of High Performance Computing USTC China

Erasure coding has been a commonly used approach to provide high reliability with low storage cost. But the skewed load in a recovery batch severely slows down the failure recovery process in storage systems. To this end, we propose a balanced scheduling module, SelectiveEC, which schedules reconstruction tasks out of order by dynamically selecting some stripes to be reconstructed into a batch and selecting source nodes and replacement nodes for each reconstruction task. So it achieves balanced network recovery traffic, computing resources and disk I/Os against single node failure in erasure-coded storage systems. Compared with conventional random reconstruction, SelectiveEC increases the parallelism of recovery process up to 106% and averagely bigger than 97% in our simulation. therefore, SelectiveEC not only speeds up recovery process, but also reduces the interference of failure recovery on the front-end applications. © hotstorage 2020 - 12th usenix Workshop on hot topics in storage and file systems, co-located with usenix ATC 2020. All rights reserved.

关键词： Recovery

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Rethinking WOM codes to enhance the lifetime in new SSD generations 12

Rethinking WOM codes to enhance the lifetime in new SSD gene...

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12th usenix Workshop on hot topics in storage and file systems, hotstorage 2020, co-located withusenix ATC 2020

作者： Jaffer, Shehbaz Mahdaviani, Kaveh Schroeder, Bianca University of Toronto Canada

New generations of Solid State Drives (SSDs) offer increased storage density with higher bits per cell, but an order of magnitude lower Program and Erase (P/E) cycles. this decreases the number of times one can rewrite on the SSD, and hence, the overall lifetime of the drive. One way of improving drive lifetime is by applying Write-Once Memory (WOM) codes which can rewrite on top of pre-existing data without erasing previous data. this increases the total logical data that can be written on the physical medium before an erase operation is required. Traditional WOM codes are not scalable and only offer up to 50% increase in total writable logical data between any two erase operations. In this paper we present a novel, simple and highly efficient family of WOM codes. Although our design is generic and could be applied to any N-Level cell drive, we focus on QLC drives to demonstrate and evaluate the proposed scheme and show that it can increase the total logical writable data before an erase in a range of 50-375% the physical medium capacity with varying storage overheads. Next, we argue that it is possible to further increase the total logical writable data between two erase operations by up to 500% with the help of a carefully chosen internal error-correcting code (ECC) already present in SSDs. © hotstorage 2020 - 12th usenix Workshop on hot topics in storage and file systems, co-located with usenix ATC 2020. All rights reserved.

关键词： file organization

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SplitKV: Splitting IO paths for different sized key-value items with advanced storage devices 12

SplitKV: Splitting IO paths for different sized key-value it...

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12th usenix Workshop on hot topics in storage and file systems, hotstorage 2020, co-located withusenix ATC 2020

作者： Han, Shukai Jiang, Dejun Xiong, Jin SKL Computer Architecture ICT CAS University of Chinese Academy of Science China

Modern advanced storage devices, such as modern NVMe SSD and non-volatile memory based persistent memory (PM), provide different access features when data size varies. Existing key-value stores adopt unified IO path for all key-value items, which cannot fully exploit the advantages of different advanced storage devices. In this paper, we propose to split IO paths for different sized key-value items. We let small key-value items be written directly to PM and then migrated to SSD. Meanwhile, large key-value items are directly written to SSD. We present and discuss design choices towards challenging issues of splitting IO paths. We build SplitKV, a key-value store prototype to show the benefits of splitting IO paths. the preliminary results show SplitKV outperforms RocksDB, KVell, and NoveLSM under small-large KV mixed workloads. © hotstorage 2020 - 12th usenix Workshop on hot topics in storage and file systems, co-located with usenix ATC 2020. All rights reserved.

关键词： file organization

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Say goodbye to off-heap caches! on-heap caches using memory-mapped I/O 12

Say goodbye to off-heap caches! on-heap caches using memory-...

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12th usenix Workshop on hot topics in storage and file systems, hotstorage 2020, co-located withusenix ATC 2020

作者： Kolokasis, Iacovos G. Papagiannis, Anastasios Pratikakis, Polyvios Bilas, Angelos Zakkak, Foivos Greece Red Hat Inc Department of Computer Science University of Crete Greece University of Manchester United Kingdom

Many analytics computations are dominated by iterative processing stages, executed until a convergence condition is met. To accelerate such workloads while keeping up with the exponential growth of data and the slow scaling of DRAM capacity, Spark employs off-memory caching of intermediate results. However, off-heap caching requires the serialization and deserialization (serdes) of data, which add significant overhead especially with growing datasets. this paper proposes TeraCache, an extension of the Spark data cache that avoids the need of serdes by keeping all cached data on-heap but off-memory, using memory-mapped I/O (mmio). To achieve this, TeraCache extends the original JVM heap with a managed heap that resides on a memory-mapped fast storage device and is exclusively used for cached data. Preliminary results show that the TeraCache prototype can speed up Machine Learning (ML) workloads that cache intermediate results by up to 37% compared to the state-of-the-art serdes approach. © hotstorage 2020 - 12th usenix Workshop on hot topics in storage and file systems, co-located with usenix ATC 2020. All rights reserved.

关键词： Dynamic random access storage

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StripeFinder: Erasure coding of small objects over key-value storage devices (an uphill battle) 12

StripeFinder: Erasure coding of small objects over key-value...

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12th usenix Workshop on hot topics in storage and file systems, hotstorage 2020, co-located withusenix ATC 2020

作者： Maheshwari, Umesh Chiku Research

Emerging key-value storage devices are promising because they rid the storage stack of the intervening block namespace and reduce IO amplification. However, pushing the key-value interface down to the device level creates a challenge: erasure coding must be performed over key-value namespaces. We expose a fundamental problem in employing parity-based erasure coding over key-value namespaces. Namely, the system must store a lot of per-stripe metadata that includes the keys of all objects in the stripe. Furthermore, this metadata must be find-able using the key of each object in the stripe. A state-of-the-art design, KVMD, does not quantify this metadata overhead [10]. We clarify that, when storing D data and P parity objects, KVMD stores DP metadata objects, each of which stores D+P object keys. this nullifies the benefit of parity coding over replication in object count. For small objects, it might also nullify the benefit in byte count;e.g., to protect 256 byte objects with 16 byte keys against two failures (P=2), KVMD would cause byte amplification of 2.8x (D=4) and 3.3x (D=8) vs. 3x with plain replication. We present an optimized version, StripeFinder, that significantly reduces metadata byte and object counts;e.g., to protect 256 byte objects against two failures (P=2), StripeFinder reduces byte amplification to 1.9x (D=4) and 1.6x (D=8). However, even StripeFinder does not provide enough savings for 100 byte objects to justify its complexity over replication. © hotstorage 2020 - 12th usenix Workshop on hot topics in storage and file systems, co-located with usenix ATC 2020. All rights reserved.

关键词： Metadata

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Could cloud storage be disrupted in the next decade? 20

Could cloud storage be disrupted in the next decade?

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proceedings of the 12th usenix conference on hot topics in storage and file systems

作者： Andromachi Chatzieleftheriou Ioan Stefanovici Dushyanth Narayanan Benn thomsen Antony Rowstron Microsoft Research

If you were asked today "What are the three dominant persistent storage technologies used in the cloud in 2020?", you would probably answer HDD, flash and tape. If you were asked this question in 2010 you would have probably answered HDD, flash and tape. Will this answer change when you are asked this question in 2030?.

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