Non-volatile storage

Nanavati, Mihir; Schwarzkopf, Malte; Wires, Jake; Warfield, Andrew

doi:10.1145/2814342

Cited by 20 publications

(17 citation statements)

References 5 publications

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“…The high-speed, non-volatile storage devices like SSDs typically referred to as SCMs access data via electrical signals, as opposed to physical disk arm movement in the case of HDDs [3,9]. Data is organized in 4 kB pages, while a group of 128 or 256 pages is known as block in SCMs.…”

Section: Secondary Storage (Block Device) Characteristicsmentioning

confidence: 99%

“…Recently there has been great industrial and academic focus to utilize faster PCIe bus technology (also known as NVMe Express) as an interface for SSDs [3]. NVM Express is becoming the de-fact standard to interact with SCMs over PCI Express.…”

Section: Secondary Storage (Block Device) Characteristicsmentioning

confidence: 99%

“…Therefore SCMs increase the total cost of ownership (TCO). Unless majority of the data is uniformly "hot" it is highly inefficient to store the data in high-value SCMs as they are underutilized and do not justify the high investment [3,4,23].…”

Section: Secondary Storage (Block Device) Characteristicsmentioning

confidence: 99%

“…This causes contentions. In-order to meet their service level agreements (SLAs), cloud providers need to ensure performance isolation gaurantees for every application [3].…”

Section: Introductionmentioning

confidence: 99%

“…With recent developments in NVMe (non-volatile memory) devices such as solid state drives (SSDs), commonly known as storage class memories (SCM) [9], with supporting infrastructure, and, virtualization techniques, a hybrid approach of using heterogeneous tiers of storage together such as those having HDDs and SSDs coupled with workloadaware tiering to balance cost, performance and capacity have become increasingly popular [1,3]. In the second part, we propose a novel hybrid scheme BID-Hybrid to exploit SCM's (SSDs) superior random performance to further avoid contentions at disk based storage.…”

Section: Introductionmentioning

confidence: 99%

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Host managed contention avoidance storage solutions for Big Data

Mishra

Somani

2017

J Big Data

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The performance gap between compute and storage is fairly considerable. This results in a mismatch between the application needs from storage and what storage can deliver. The full potential of storage devices cannot be harnessed till all layers of I/O hierarchy function efficiently. Despite advanced optimizations applied across various layers along the odyssey of data access, the I/O stack still remains volatile. The problems associated due to the inefficiencies in data management get amplified in Big Data shared resource environments. The Linux OS (host) block layer is the most critical part of the I/O hierarchy, as it orchestrates the I/O requests from different applications to the underlying storage. Unfortunately, despite it's significance, the block layer, essentially the block I/O scheduler, hasn't evolved to meet the needs of Big Data. We have designed and developed two contention avoidance storage solutions, collectively known as "BID: Bulk I/O Dispatch" in the Linux block layer specifically to suit multitenant, multi-tasking shared Big Data environments. Hard disk drives (HDDs) form the backbone of data center storage. The data access time in HDDs is majorly governed by disk arm movements, which usually occurs when data is not accessed sequentially. Big Data applications exhibit evident sequentiality but due to the contentions amongst other I/O submitting applications, the I/O accesses get multiplexed which leads to higher disk arm movements. BID schemes aim to exploit the inherent I/O sequentiality of Big Data applications to improve the overall I/O completion time by reducing the avoidable disk arm movements. In the first part, we propose a dynamically adaptable block I/O scheduling scheme BID-HDD for disk based storage. BID-HDD tries to recreate the sequentiality in I/O access in order to provide performance isolation to each I/O submitting process. Through trace driven simulation based experiments with cloud emulating MapReduce benchmarks, we show the effectiveness of BID-HDD which results in 28-52% lesser time for all I/O requests than the best performing Linux disk schedulers. In the second part, we propose a hybrid scheme BID-Hybrid to exploit SCM's (SSDs) superior random performance to further avoid contentions at disk based storage. BID-Hybrid is able to efficiently offload non-bulky interruptions from HDD request queue to SSD queue using BID-HDD for disk request processing and multi-q FIFO architecture for SSD. This results in performance gain of 6-23% for MapReduce workloads when compared to BID-HDD and 33-54% over best performing Linux scheduling scheme. BID schemes as a whole is aimed to avoid contentions for disk based storage I/Os following system constraints without compromising SLAs.

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Section: Secondary Storage (Block Device) Characteristicsmentioning

confidence: 99%

Section: Secondary Storage (Block Device) Characteristicsmentioning

confidence: 99%

Section: Secondary Storage (Block Device) Characteristicsmentioning

confidence: 99%

“…This causes contentions. In-order to meet their service level agreements (SLAs), cloud providers need to ensure performance isolation gaurantees for every application [3].…”

Section: Introductionmentioning

confidence: 99%