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.

The development of KV-SSDs allows simplifying the I/O stack compared to the traditional block-based SSDs. We propose a novel Key-Value-based Storage infrastructure for Parallel Computing(KV-SiPC)-a framework for multi-thread OpenMP applications to use NVMe-based KV-SSDs. We design a new capability to execute workloads with multiple parallel data threads along with traditional parallel compute threads, that allow us to improve the overall throughput of applications, utilizing the maximum possible storage bandwidth. We implement our KV-SiPC infrastructure in a real system by extending various processing layers (e.g., program, OS, and device layers) and evaluate the performance of KV-SiPC by using block-based NVMe SSDs in the traditional I/O stack as a baseline for comparisons. The experimental results show that KV-SiPC can better utilize the available device bandwidth and significantly increases application I/O throughput.

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.

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.

Docker containers are becoming the mainstay for deploying applications in cloud platforms, having many desirable features like ease of deployment, developer friendliness and lightweight virtualization. Meanwhile, solid state disks (SSDs) have witnessed tremendous performance boost through recent innovations in industry such as Non-Volatile Memory Express (NVMe) standards. However, the performance of containerized applications on these high speed contemporary SSDs has not yet been investigated. In this paper, we present a characterization of the performance impact among a wide variety of the available storage options for deploying Docker containers and provide the configuration options to best utilize the high performance SSDs.

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.

Flash memory is widely used for its fast random I/O access performance in a gamut of enterprise storage applications. However, due to the limited endurance and asymmetric write performance of flash memory, minimizing writes to a flash device is critical for both performance and endurance. Previous studies have focused on flash memory as a candidate for primary storage devices; little is known about its behavior as a Solid State Cache (SSC) device. In this paper, we propose HEC, a High Endurance Cache that aims to improve overall device endurance via reduced media writes and erases while maximizing cache hit rate performance. We analyze the added write pressures that cache workloads place on flash devices and propose optimizations at both the cache and flash management layers to improve endurance while maintaining or increasing cache hit rate. We demonstrate the individual and cumulative contributions of cache admission policy, cache eviction policy, flash garbage collection policy, and flash device configuration on a) hit rate, b) overall writes, and c) erases as seen by the SSC device. Through our improved cache and flash optimizations, 83% of the analyzed workload ensembles achieved increased or maintained hit rate with write reductions up to 20x, and erase count reductions up to 6x.

Storage Class Memory (SCM) has become increasingly popular in enterprise systems as well as embedded and mobile systems. However, replacing hard drives with SCMs in current storage systems often forces either major changes in file systems or suboptimal performance, because the current block-based interface does not deliver enough information to the device to allow it to optimize data management for specific device characteristics such as the out-of-place update. To alleviate this problem and fully utilize different characteristics of SCMs, we propose the use of an object-based model that provides the hardware and firmware the ability to optimize performance for the underlying implementation, and allows drop-in replacement for devices based on new types of SCM. We discuss the design of object-based SCMs and implement an object-based flash memory prototype. By analyzing different design choices for several subsystems, such as data placement policies and index structures, we show that our object-based model provides comparable performance to other flash file systems while enabling advanced features such as object-level reliability.

Flash memory has become increasingly popular in today's storage systems. However, replacing hard drives with flash memory in current systems often either requires major file system changes or causes performance degradation due to the limitations of block-based interface and out-of-place updates required by flash. To alleviate this problem, we propose an object-based model for flash memory that gives the hardware and firmware the ability to optimize performance for the underlying implementation. Based on this model, we propose two new data placement policies that exploit richer information from an object-based interface. Using simulation, we show that cleaning overhead can be reduced by up to 9% by separating data and metadata. Segregating the access time from metadata can further reduce the cleaning overhead by up to 23%.