ReCA: An Efficient Reconfigurable Cache Architecture for Storage Systems with Online Workload Characterization

Salkhordeh, Reza; Ebrahimi, Shahriar; Asadi, Hossein

doi:10.1109/tpds.2018.2796100

Cited by 25 publications

(27 citation statements)

References 37 publications

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“…SSD failures in Google and Facebook datacenters during six and four years of operation are studied in [26,35]. Meza et al observed similar "bathtube curve" in the failure trend of SSDs and an additional phase namely "early 5. In this work, we target the impact of power outage and high operating temperature on the reliability of I/O caches.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Reliability of SSD-Based I/O Caches in Enterprise Storage Systems

Ahmadian

Taheri

Asadi

2021

IEEE Trans. Emerg. Topics Comput.

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I/O caching techniques are widely employed in enterprise storage systems in order to enhance performance of I/O intensive applications in large-scale data centers. Due to higher performance compared to Hard Disk Drives (HDDs) and lower price and nonvolatility compared to Dynamic Random-Access Memories (DRAM), Flash-based Solid-State Drives (SSDs) are used as a main media in the caching layer of storage systems. Although SSDs are known as non-volatile devices but recent studies have reported large number of data failures due to power outage in SSDs. To overcome the reliability implications of SSD-based I/O caching schemes, RAID-1 (mirrored) configuration is commonly used to avoid data loss due to uncommitted write operations. Such configuration, however, may still experience data loss in the cache layer due to correlated failures in SSDs. To our knowledge, none of previous studies have investigated the reliability of SSD-based I/O caching schemes in enterprise storage systems.In this paper, we present a comprehensive analysis investigating the reliability of SSD-based I/O caching architectures used in enterprise storage systems under power failure and high-operating temperature. We explore variety of SSDs from top vendors and investigate the cache reliability in mirrored configuration. To this end, we first develop a physical fault injection and failure detection platform and then investigate the impact of workload dependent parameters on the reliability of I/O cache in the presence of two common failure types in data centers, power outage and high temperature faults. We implement an I/O cache scheme using an open-source I/O cache module in Linux operating system. The experimental results obtained by conducting more than twenty thousand of physical fault injections on the implemented I/O cache with different write policies reveal that the failure rate of the I/O cache is significantly affected by workload dependent parameters. Our results show that unlike workload requests access pattern, the other workload dependent parameters such as request size, Working Set Size (WSS), and sequence of the accesses have considerable impact on the I/O cache failure rate. We observe a significant growth in the failure rate in the workloads by decreasing the size of the requests (by more than 14X). Furthermore, we observe that in addition to writes, the read accesses to the I/O cache are subjected to failure in presence of sudden power outage (the failure mainly occurs during promoting data to the cache). In addition, we observe that I/O cache experiences no data failure upon high temperature faults.

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Section: Related Workmentioning

confidence: 99%

Evaluating Reliability of SSD-Based I/O Caches in Enterprise Storage Systems

Ahmadian

Taheri

Asadi

2021

IEEE Trans. Emerg. Topics Comput.

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“…Existing SSD-based I/O caching schemes such as Janus [17], Hystor [13], ReCA [3], KAML [26], DIDACache [27], and ALACC [28] [30] is the latest state-of-the-art load balancing schemes, which aims to balance the load of I/O requests between SSD and disk subsystem preventing the I/O cache to become performance bottleneck. This scheme is designed in a way that only works for WT and WO caches in which only write accesses are buffered in the I/O cache while they are propagated to the disk subsystem at the same time.…”

Section: Related Workmentioning

confidence: 99%

“…Increasing number of I/O intensive applications such as Online Transaction Processing (OLTP), High Performance Computing (HPC), web, and email applications arises the demand in data-centers for high-performance storage systems. The most common approach to improving the performance of storage systems is to employ Solid-State Drives (SSDs) [1] in the caching layer of the disk subsystems [2], [3], [4], [5], [6], which are mainly built upon low-performance and lowreliable Hard Disk Drives (HDD) [7], [8], [9] or mid-range SSDs (as shown in Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

LBICA: A Load Balancer for I/O Cache Architectures

Ahmadian

Salkhordeh

Asadi

2019

2019 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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In recent years, enterprise Solid-State Drives (SSDs) are used in the caching layer of high-performance servers to close the growing performance gap between processing units and storage subsystem. SSD-based I/O caching is typically not effective in workloads with burst accesses in which the caching layer itself becomes the performance bottleneck because of the large number of accesses. Existing I/O cache architectures mainly focus on maximizing the cache hit ratio while they neglect the average queue time of accesses. Previous studies suggested bypassing the cache when burst accesses are identified. These schemes, however, are not applicable to a general cache configuration and also result in significant performance degradation on burst accesses.In this paper, we propose a novel I/O cache load balancing scheme (LBICA) with adaptive write policy management to prevent the I/O cache from becoming performance bottleneck in burst accesses. Our proposal, unlike previous schemes, which disable the I/O cache or bypass the requests into the disk subsystem in burst accesses, selectively reduces the number of waiting accesses in the SSD queue and balances the load between the I/O cache and the disk subsystem while providing the maximum performance. The proposed scheme characterizes the workload based on the type of in-queue requests and assigns an effective cache write policy. We aim to bypass the accesses which 1) are served faster by the disk subsystem or 2) cannot be merged with other accesses in the I/O cache queue. Doing so, the selected requests are responded by the disk layer, preventing from overloading the I/O cache. Our evaluations on a physical system shows that LBICA reduces the load on the I/O cache by 48% and improves the performance of burst workloads by 30% compared to the latest state-of-the-art load balancing scheme.

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“…HDFS has also added support for caching input files internally [23]. NVRAM and SSDs have been used as the storage layer for distributed systems [13,27,34] as well as in shared storage systems (e.g., ReCA [44], Hermes [30]). Recently, the OctopusFS distributed file system [29] introduced fine-grained tiering in compute clusters via storing file replicas on the various storage media (e.g., memory, SSDs, HDDs) that are locally attached on the cluster nodes.…”

Section: Introductionmentioning

confidence: 99%

Automating distributed tiered storage management in cluster computing

Herodotou

Kakoulli

2019

Proc. VLDB Endow.

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Data-intensive platforms such as Hadoop and Spark are routinely used to process massive amounts of data residing on distributed file systems like HDFS. Increasing memory sizes and new hardware technologies (e.g., NVRAM, SSDs) have recently led to the introduction of storage tiering in such settings. However, users are now burdened with the additional complexity of managing the multiple storage tiers and the data residing on them while trying to optimize their workloads. In this paper, we develop a general framework for automatically moving data across the available storage tiers in distributed file systems. Moreover, we employ machine learning for tracking and predicting file access patterns, which we use to decide when and which data to move up or down the storage tiers for increasing system performance. Our approach uses incremental learning to dynamically refine the models with new file accesses, allowing them to naturally adjust and adapt to workload changes over time. Our extensive evaluation using realistic workloads derived from Facebook and CMU traces compares our approach with several other policies and showcases significant benefits in terms of both workload performance and cluster efficiency.

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ReCA: An Efficient Reconfigurable Cache Architecture for Storage Systems with Online Workload Characterization

Cited by 25 publications

References 37 publications

Evaluating Reliability of SSD-Based I/O Caches in Enterprise Storage Systems

Evaluating Reliability of SSD-Based I/O Caches in Enterprise Storage Systems

LBICA: A Load Balancer for I/O Cache Architectures

Automating distributed tiered storage management in cluster computing

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