In the era of big data processing, more and more data centers in cloud storage are now replacing traditional HDDs with enterprise SSDs. Both developers and users of these SSDs require thorough benchmarking to evaluate and configure the variable parameters of emerging technologies.[2]and[3]are the recent development of the SSD industry, which assists in placing data on SSDs in a smart way to improve application performance and SSD endurance. The challenging part to use multi-stream SSDs is to assign stream IDs to incoming writes, such that each stream consists of data with a similar lifetime. The benefit of the stream management algorithms varies over different workloads. Thus, first, we propose a new framework, calledPatternI/Ogenerator (PatIO), to capture the enterprise storage behavior that is prevailing across various user workloads, virtualization setup, file systems, and volume managers for the database server applications on flash-based storage. Second, usingPatIO, we study what type of applications may be benefited by which stream assignment algorithm. Third, we design the framework to automatically tune the variable parameters of different stream identification algorithms of the multi-stream SSDs. Our evaluation shows 20 to 110 percent of the reward function increase, measuring the cumulative impact on application performance and SSD endurance.

The demand for high performance I/O in Storage-as-a-Service (SaaS) is increasing day by day. To address this demand, NAND Flash-based Solid-state Drives (SSDs) are commonly used in data centers as cache- or top-tiers in the storage rack ascribe to their superior performance compared to traditional hard disk drives (HDDs). Meanwhile, with the capital expenditure of SSDs declining and the storage capacity of SSDs increasing, all-flash data centers are evolving to serve cloud services better than SSD-HDD hybrid data centers. During this transition, the biggest challenge is how to reduce the Write Amplification Factor (WAF) as well as to improve the endurance of SSD since this device has a limited program/erase cycles. A specified case is that storing data with different lifetimes (i.e., I/O streams with similar temporal fetching patterns such as reaccess frequency) in one single SSD can cause high WAF, reduce the endurance, and downgrade the performance of SSDs. Motivated by this, multi-stream SSDs have been developed to enable data with a different lifetime to be stored in different SSD regions. The logic behind this is to reduce the internal movement of data—when garbage collection is triggered, there are high chances of having data blocks with either all the pages being invalid or valid. However, the limitation of this technology is that the system needs to manually assign the same streamID to data with a similar lifetime. Unfortunately, when data arrives, it is not known how important this data is and how long this data will stay unmodified. Moreover, according to our observation, with different definitions of a lifetime (i.e., different calculation formulas based on selected features previously exhibited by data, such as sequentiality, and frequency), streamID identification may have varying impacts on the final WAF of multi-stream SSDs. Thus, in this article, we first develop a portable and adaptable framework to study the impacts of different workload features and their combinations on write amplification. We then propose a feature-based stream identification approach, which automatically co-relates the measurable workload attributes (such as I/O size, I/O rate, and so on.) with high-level workload features (such as frequency, sequentiality, and so on.) and determines a right combination of workload features for assigning streamIDs . Finally, we develop an adaptable stream assignment technique to assign streamID for changing workloads dynamically. Our evaluation results show that our automation approach of stream detection and separation can effectively reduce the WAF by using appropriate features for stream assignment with minimal implementation overhead.

In the era of big data processing, more and more datacenters in cloud storages are now replacing traditional HDDs with enterprise SSDs. Both developers and users of these SSDs require thorough benchmarking to evaluate their performance impacts. I/O performance with synthetic workload or classic benchmark varies drastically from real I/O activities in the datacenter. Thus, we propose a new framework, called Pattern I/O generator (PatIO), to collectively capture the enterprise storage behavior that is prevailing across assorted user workloads and system configurations for different database server applications on flash-based storage. PatIO is designed to emulate the processing of real-world I/O activities easily with less time and resource requirements. Our methodology comprises three main steps: (1) dissect the overall I/O activities of various real workloads and identify the prevailing attributes in distinct visual I/O patterns; (2) construct a pattern warehouse as the collection of unique I/O patterns that are generated through various combinations of multiple I/O jobs; and (3) finally integrate different combinations of these synthetically generated I/O patterns to reproduce the comprehensive characteristics of various real workloads and system setup for the database server applications. To provide an easy-to-use experience, we develop a graphical user interface (GUI). We evaluate our framework by comparing I/O characteristics and I/O performance of generated workloads with those of real-world workloads for multiple database applications such as MySQL, Cassandra, and ForestDB.

The demand for high speed 'Storage-as-a-Service' (SaaS) is increasing day-by-day. SSDs are commonly used in higher tiers of storage rack in data centers. Also, all flash data centers are evolving to better serve cloud services. Although SSDs guaranty better performance when compared to HDDs, but SSDs endurance is still a matter of concern. Storing data with different lifetime in an SSD can cause high write amplification and reduce the endurance and performance of SSDs. Recently, multi-stream SSDs have been developed to enable data with different lifetime to be stored in different SSD regions and thus reduce write amplification. To efficiently use this new multi-streaming technology, it is important to choose appropriate workload features to assign the same streamID to data with similar lifetime. However, we found that streamID identification using different features may have varying impacts on the final write amplification of multi-stream SSDs. Therefore, in this paper we develop a portable and adoptable framework to study the impacts of different workload features and their combinations on write amplification. We also introduce a new feature, named "coherency", to capture the friendship among write operations with respect to their update time. Finally, we propose a feature-based stream identification approach, which co-relates the measurable workload attributes (such as I/O size, I/O rate, etc.) with high level workload features (such as frequency, sequentiality etc.) and determines a good combination of workload features for assigning streamIDs. Our evaluation results show that our proposed approach can always reduce the Write Amplification Factor (WAF) by using appropriate features for stream assignment.

A Key-Value SSD (KV-SSD) is a new type of storage device that natively exposes a key-value interface. In this paper, we leverage KV-SSDs to develop new techniques to remove unnecessary layers of indirection traditionally imposed by block devices on distributed storage systems. Specifically, we extend the Crail distributed system [1] to leverage the KV-SSD's native key-value interface exposing it directly to clients through the NVMe-oF protocol. This architectural change simplifies key-value metadata management, as the metadata manager need only track key-value tuples rather than files comprised of blocks. These changes enable fewer RPCs and require less memory for metadata management, resulting in a performance improvement of up to 5x.

Storage disaggregation separates compute and storage to different nodes to allow for independent resource scaling and, thus, better hardware resource utilization. While disaggregation of hard-drives storage is a common practice, NVMe-SSD (i.e., PCIe-based SSD) disaggregation is considered more challenging. This is because SSDs are significantly faster than hard drives, so the latency overheads (due to both network and CPU processing) as well as the extra compute cycles needed for the offloading stack become much more pronounced. In this work, we characterize the overheads of NVMe-SSD disaggregation. We show that NVMe-over-Fabrics (NVMe-oF)—a recently released remote storage protocol specification—reduces the overheads of remote access to a bare minimum, thus greatly increasing the cost-efficiency of Flash disaggregation. Specifically, while recent work showed that SSD storage disaggregation via iSCSI degrades application-level throughput by 20%, we report on negligible performance degradation with NVMe-oF—both when using stress-tests as well as with a more-realistic KV-store workload.

The adoption of SSDs has become very prominent, but they still suffer from challenges to control write amplification. Traditional SSDs have single active append point where new data writes can be stored. Data of different lifetime stored together causes high write amplification. Recently, multi-stream SSDs are developed that allows multiple active append points. These multiple active append points can be used to store data of different lifetime in different locations within SSD. Such a data placement according to the lifetime of data would considerably reduce internal write amplification of SSD. For using multi-stream SSDs it is required to attach stream-id to each new incoming data writes. According to these stream-ids, the flash transition layer (FTL) of a multi-stream SSD then appends data to different erase blocks. Thus, multi-stream SSDs will help to reduce write amplification. But, to efficiently use this new multi-stream SSDs, it is important to properly identify streamids of data with respect to its lifetime. The lifetime of data is expected using different features that data exhibits like frequency, sequentiality etc. Stream-id identification using different features may have different impact on the final write amplification of multi-stream SSDs, depending on workload. Thus, it is required to quantify the impact of different data features that are used for stream-id identification on the resultant write amplification. Additionally, the combination of these data features may be used for stream-id identification, so it is also important to be able to study the impact of such different combinations. In order to address above challenges towards efficiently using multi-stream SSDs, here we propose a portable and adoptable framework to study the impact of stream-id identification using different workload data features and their combinations on write amplification of multi-stream SSDs. Our evaluation results show that use of appropriate features according to workload can considerably reduce the Write Amplification Factor (WAF) when compared to the legacy SSDs.

Storage disaggregation separates compute and storage to different nodes in order to allow for independent resource scaling and thus, better hardware resource utilization. While disaggregation of hard-drives storage is a common practice, NVMe-SSD (i.e., PCIe-based SSD) disaggregation is considered more challenging. This is because SSDs are significantly faster than hard drives, so the latency overheads (due to both network and CPU processing) as well as the extra compute cycles needed for the offloading stack become much more pronounced.

Multi-stream SSDs can isolate data with different life time to disparate erase blocks, thus reduce garbage collection overhead and improve overall SSD performance. Applications are responsible for management of these device-level steams such as stream open/close and data-to-stream mapping. This requires application changes, and the engineer deploying the solution needs to be able to individually identify the streams in their workload. Furthermore, when multiple applications are involved, such as in VM or containerized environments, stream management becomes more complex due to the limited number of streams a device can support, for example, allocating streams to applications or sharing streams across applications will cause additional overhead.

Our cloud-based IT world is founded on hyper-visors and containers. Containers are becoming an important cornerstone, which is increasingly used day-by-day. Among different available frameworks, docker has become one of the major adoptees to use containerized platform in data centers and enterprise servers, due to its ease of deploying and scaling. Further more, the performance benefits of a lightweight container platform can be leveraged even more with a fast back-end storage like high performance SSDs. However, increase in number of simultaneously operating docker containers may not guarantee an aggregated performance improvement due to saturation. Thus, understanding performance bottleneck in a multi-tenancy docker environment is critically important to maintain application level fairness and perform better resource management. In this paper, we characterize the performance of persistent storage option (through data volume) for I/O intensive, dockerized applications. Our work investigates the impact on performance with increasing number of simultaneous docker containers in different workload environments. We provide, first of its kind study of I/O intensive containerized applications operating with NVMe SSDs. We show that 1) a six times better application throughput can be obtained, just by wise selection of number of containerized instances compared to single instance; and 2) for multiple application containers running simultaneously, an application throughput may degrade upto 50% compared to a stand-alone applications throughput, if good choice of application and workload is not made. We then propose novel design guidelines for an optimal and fair operation of both homogeneous and heterogeneous environments mixed with different applications and workloads.

The storage subsystem has undergone tremendous innovation in order to keep up with the ever-increasing demand for throughput. NVMe based SSDs are the latest development in this domain, delivering unprecedented performance in terms of both latency and peak bandwidth. Given their superior performance, NVMe drives are expected to be particularly beneficial for I/O intensive applications in datacenter installations. In this paper we identify and analyze the different factors leading to the better performance of NVMe SSDs. Then, using databases as the prominent use-case, we show how these would translate into real-world benefits. We evaluate both a relational database (MySQL) and a NoSQL database (Cassandra) and demonstrate significant performance gains over best-in-class enterprise SATA SSDs: from 3.5x for TPC-C and up to 8.5x for Cassandra.

The storage subsystem has undergone tremendous innovation in order to keep up with the ever-increasing demand for throughput. Non Volatile Memory Express (NVMe) based solid state devices are the latest development in this domain, delivering unprecedented performance in terms of latency and peak bandwidth. NVMe drives are expected to be particularly beneficial for I/O intensive applications, with databases being one of the prominent use-cases.

In recent years, a number of benchmark suites have been created for the ``Big Data'' domain, and a number of such applications fit the client-server paradigm. A large volume of recent literature in characterizing ``Big Data'' applications have largely focused on two extremes of the characterization spectrum. On one hand, multiple studies have focused on client-side performance. These involve fine-tuning server-side parameters for an application to get the best client-side performance. On the other extreme, characterization focuses on picking one set of client-side parameters and then reporting the server microarchitectural statistics under those assumptions. While the two ends of the spectrum present interesting results, this paper argues that they are not enough, and in some cases, undesirable, to drive system-wide architectural decisions in datacenter design.