This article presents the first large-scale field study of NAND-based SSDs in enterprise storage systems (in contrast to drives in distributed data center storage systems). The study is based on a very comprehensive set of field data, covering 1.6 million SSDs of a major storage vendor (NetApp). The drives comprise three different manufacturers, 18 different models, 12 different capacities, and all

Storage systems are designed and optimized relying on wisdom derived from analysis studies of file-system and block-level workloads. However, while SSDs are becoming a dominant building block in many storage systems, their design continues to build on knowledge derived from analysis targeted at hard disk optimization. Though still valuable, it does not cover important aspects relevant for SSD performance

No abstract available.

In the era of data-intensive computing, large-scale applications, in both scientific and the BigData communities, demonstrate unique I/O requirements leading to a proliferation of different storage devices and software stacks, many of which have conflicting requirements. Further, new hardware technologies and system designs create a hierarchical composition that may be ideal for computational storage

The growing volume of data produced continuously in the Cloud and at the Edge poses significant challenges for large-scale AI applications to extract and learn useful information from the data in a timely and efficient way. The goal of this article is to explore the use of computational storage to address such challenges by distributed near-data processing. We describe Newport, a high-performance and

The rapidly increasing data in recent years requires the datacenter infrastructure to store and process data with extremely high throughput and low latency. Fortunately, persistent memory (PM) and RDMA technologies bring new opportunities towards this goal. Both of them are capable of delivering more than 10 GB/s of bandwidth and sub-microsecond latency. However, our past experiences and recent studies

Complex storage stacks providing data compression, indexing, and analytics help leverage the massive amounts of data generated today to derive insights. It is challenging to perform this computation, however, while fully utilizing the underlying storage media. This is because, while storage servers with large core counts are widely available, single-core performance and memory bandwidth per core grow

While the computing power of mobile devices has been quickly evolving in recent years, the growth of mobile storage capacity is, however, relatively slower. A common problem shared by budget-phone users is that they frequently run out of storage space. This article conducts a deep inspection of file usage of mobile applications and their potential implications on user experience. Our major findings

Erasure codes are being extensively deployed in practical storage systems to prevent data loss with low redundancy. However, these codes require excessive disk I/Os and network traffic for recovering unavailable data. Among all erasure codes, Minimum Storage Regenerating (MSR) codes can achieve optimal repair bandwidth under the minimum storage during recovery, but some open issues remain to be addressed

As semiconductor technology has advanced, many storage systems have begun to use non-volatile memories as storage media. The organization and architecture of storage controllers have become more complex to meet various design requirements in terms of performance, response time, quality of service (QoS), and so on. In addition, due to the evolution of memory technology and the emergence of new applications

Many file-system applications such as defragmentation tools, file-system checkers, or data recovery tools, operate at the storage layer. Today, developers of these file-system aware storage applications require detailed knowledge of the file-system format, which requires significant time to learn, often by trial and error, due to insufficient documentation or specification of the format. Furthermore

In this work, we propose B3-tree, a hybrid index for persistent memory that leverages the byte-addressability of the in-memory index and the page locality of B-trees. As in the byte-addressable in-memory index, B3-tree is updated by 8-byte store instructions. Also, as in disk-based index, B3-tree is failure-atomic since it makes every 8-byte store instruction transform a consistent index into another

The SSD has been playing a significantly important role in caching systems due to its high performance-to-cost ratio. Since the cache space is typically much smaller than that of the backend storage by one order of magnitude or even more, write density (defined as writes per unit time and space) of the SSD cache is therefore much more intensive than that of HDD storage, which brings about tremendous

Existing local file systems, designed to support a typical single-file access mode only, can lead to poor performance when accessing a batch of files, especially small files. This single-file mode essentially serializes accesses to batched files one by one, resulting in a large number of non-sequential, random, and often dependent I/Os between file data and metadata at the storage ends. Such access

No abstract available.

Flash-based key-value caching is becoming popular in data centers for providing high-speed key-value services. These systems adopt slab-based space management on flash and provide a low-cost solution for key-value caching. However, optimizing cache efficiency for flash-based key-value cache systems is highly challenging, due to the huge number of key-value items and the unique technical constraints

Regular file server upgrades are indispensable to improve performance, robustness, and power consumption. In upgrading file servers, it is crucial to quickly migrate file-sharing services between heterogeneous servers with little downtime while minimizing performance interference. We present a practical quick file server migration scheme based on the postcopy approach that defers file copy until after

Erasure codes in large-scale storage systems allow recovery of data from a failed node. A recently developed class of codes, locally repairable codes (LRCs), offers tradeoffs between storage overhead and repair cost. LRCs facilitate efficient recovery scenarios by adding parity blocks to the system. However, these additional blocks may eventually increase the number of blocks that must be reconstructed

For a decade, the Ceph distributed file system followed the conventional wisdom of building its storage backend on top of local file systems. This is a preferred choice for most distributed file systems today, because it allows them to benefit from the convenience and maturity of battle-tested code. Ceph’s experience, however, shows that this comes at a high price. First, developing a zero-overhead

File systems are too large to be bug free. Although handwritten test suites have been widely used to stress file systems, they can hardly keep up with the rapid increase in file system size and complexity, leading to new bugs being introduced. These bugs come in various flavors: buffer overflows to complicated semantic bugs. Although bug-specific checkers exist, they generally lack a way to explore

Data encryption and authentication are essential for secure non-volatile memory (NVM). However, the introduced security metadata needs to be atomically written back to NVM along with data, so as to provide crash consistency, which unfortunately incurs high overhead. To support fine-grained data protection and fast recovery for a secure NVM system without compromising the performance, we propose ShieldNVM