A Parametric I/O Model for Modern Storage Devices

Fast network quality analysis in the telecom industry is an important method used to provide quality service. SK Telecom, based in South Korea, built a Hadoop-based analytical system consisting of a hundred nodes, each of which only contains hard disk drives (HDDs). Because the analysis process is a set of parallel I/O intensive jobs, adding solid state drives (SSDs) with appropriate settings is the most cost-efficient way to improve the performance, as shown in previous studies. Therefore, we decided to configure SSDs as a write-through cache instead of increasing the number of HDDs. To improve the cost-per-performance of the SSD cache, we introduced a selective I/O bypass (SIB) method, redirecting the automatically calculated number of read I/O requests from the SSD cache to idle HDDs when the SSDs are I/O over-saturated, which means the disk utilization is greater than 100 percent. To precisely calculate the disk utilization, we also introduced a combinational approach for SSDs because the current method used for HDDs cannot be applied to SSDs because of their internal parallelism. In our experiments, the proposed approach achieved a maximum 2x faster performance than other approaches.

Nowadays solid state drives (SSDs) are gaining popularity and are replacing magnetic hard disk drives (HDDs) in enterprise storage systems. As a result, extracting the maximum performance from SSDs is becoming crucial to deal with the increasing storage volume and performance needs. Active disks were introduced as a way to offload data-processing tasks from the host into disks freeing system resources and achieving better performance. In this work, we present an active SSD architecture called Self-Sorting SSD that targets to offload sorting operations which are commonly used in data-intensive and database environments and that require heavy data transfer. Processing sorting operations directly on the SSD reduces data transfer from/to the storage devices, increasing system performance and the lifetime of SSDs. Experiments on a real SSD platform reveal that our proposed architecture outperforms traditional external merge sort by up to 60.75%, reduces energy consumption by up to 58.86%, and eliminates all the data transfer overhead to compute sorted results.

Dynamic adaptive streaming over HTTP (DASH) technique, the most popular streaming method, requires a large number of hard disk drives (HDDs) to store multiple bitrate versions of many videos, consuming significant energy. A solid-state drive (SSD) can be used to cache popular videos, thus reducing HDD energy consumption by allowing I/O requests to be handled by an SSD, but this requires effective HDD power management due to limited SSD bandwidth. We propose a new SSD cache management scheme to minimize the energy consumption of a video storage system with heterogeneous HDDs. We first present a technique that caches files with the aim of saving more HDD energy as a result of I/O processing on an SSD. Based on this, we propose a new HDD power management algorithm with the goal of increasing the number of HDDs operated in low-power mode while reflecting the heterogeneous HDD power characteristics. For this purpose, it assigns a separate parameter value to each I/O task based on the ratio of HDD energy to bandwidth and greedily selects the I/O tasks handled by the SSD within limits on its bandwidth. Simulation results show that our scheme consumes between 12% and 25% less power than alternative schemes under the same HDD configuration.

When originally introduced, flash based solid state drives (SSD) exhibited a very high random read throughput with low sub-millisecond latencies. However, in addition to their steep prices, SSDs suffered from slow write rates and reliability concerns related to cell wear. For these reasons, they were relegated to a niche status in the consumer and personal computer market. Since then, several architectural enhancements have been introduced that led to a substantial increase in random write operations as well as a reasonable improvement in reliability. From a purely performance point of view, these high I/O rates and improved reliability make the SSDs an ideal choice for enterprise On-Line Transaction Processing (OLTP) applications. However, from a price/performance point of view, the case for SSDs may not be clear. Enterprise class SSD Price/GB, continues to be at least 10x higher than conventional magnetic hard disk drives (HDD) despite considerable drop in Flash chip prices. We show that a complete replacement of traditional HDDs with SSDs is not cost effective. Further, we demonstrate that the most cost efficient use of SSDs for OLTP workloads is as an intermediate persistent cache that sits between conventional HDDs and memory, thus forming a three-level memory hierarchy. We also discuss two implementations of such cache: hardware or software. For the software approach, we discuss our implementation of such a cache in an inhouse database system. We also describe off-the shelf hardware solutions. We will develop a Total Cost of Ownership (TCO) model for All-SSD and All-HDD configurations. We will also come up with a modified OLTP benchmark that can scale IO density to validate this model. We will also show how such SSD cache implementations could increase the performance of OLTP applications while reducing the overall system cost.

As the flash-based solid-state drives(SSDs) gradually replace the mechanical hard disk drives(HDDs) as the mainstream storage, unlike the HDDs, SSDs have rich internal parallelism, which makes it have the excellent characteristics that HDDs do not have. External mergesort, as the classical algorithm of external sorting adopted in many systems and algorithms, has an important impact on the overall performance. Therefore, it is of great significance to optimize and improve the efficiency of external mergesort algorithm. The research work on optimizing raw external mergesort algorithm on SSDs is relatively few. Thus, aiming at the external mergesort problem, based on the characteristics of SSDs, this paper proposes the SortDecision algorithm which can calculate its optimal execution scheme, including merging way, read buffer size, and write buffer size which determine the execution process of external mergesort. Exploiting the above optimal execution scheme, external mergesort can obtain better efficiency. In the SortDecision algorithm, external mergesort problem on SSDs is formalized and transformed into a piecewise convex optimization problem. Then, the optimal external mergesort scheme is obtained by enumerating the solutions of each subconvex problem. The experimental results show that the external mergesort proceeds guided by SortDecision algorithm can achieve a speedup of 1 $\sim $ 6.7 compared to the traditional external mergesort algorithm in the case of limited memory provided. The richer the internal parallelism resources inside SSDs, the better the effect of SortDecision’s acceleration.

The performance gap between processor and storage device has continuously increased during the past few decades. The gap is further exacerbated recently because applications are becoming more data-intensive in both industry and academia. Traditional storage devices, such as hard disk drives (HDD), fail to keep up with the paces of this growth. A known solution is to use solid state drives (SSD) as fast storage. Due to high cost of SSD, data and supercomputing centers usually adopt a hybrid storage system, which consists of a combination of HDD and SSD I/O servers. However, hybrid I/O and storage systems have increased the complexity, making SSD often underutilized. The configuration and utilization of HDD/SSD hybrid systems is a lasting phenomenon. In this study, we propose a high performance hybrid parallel I/O and storage simulator, HPIS3. As a co-design tool, HPIS3 is capable of simulating a variety of parallel storage systems, especially under hybrid scenarios. The experimental results show that the lowest error rate is 2%, and the average is 11.98%.

<u></u>The ideas and sophisticated methods governing the storage and retrieval of digital information are explored in depth in this chapter as they pertain to the complex world of hard disc data organization. The path starts with a deep dive into hard disc drives (HDDs), exposing their inner workings, disc geometry, and storage basics. The research explores FAT32, NTFS, and exFAT, among others, to better understand their inner workings. The trade-offs involved in maximizing storage efficiency and performance are unpacked, including those involving sequential versus random access, fragmentation, and clustering schemes. Disk partitioning is also investigated in detail; topics covered include partitioning fundamentals, partition kinds, and the significance of the Master Boot Record (MBR) and GUID Partition Table (GPT). These frameworks provide the groundwork for learning how data is organized on storage devices, taking into account things like available space, compatibility, and redundancy. The last leg of the journey is an examination of developing tendencies, with a focus on the revolutionary effects of Solid State Drives (SSDs) and cloud storage. The trend toward quicker, more reliable, and scalable storage solutions is highlighted by comparisons between SSDs and conventional HDDs, insights into data organization on SSDs, and the incorporation of cloud storage and remote data organization. This section summarizes an exploration of the ever-changing world of data storage, with a focus on why it is crucial to be aware of current concepts and trends in data organizing on hard drives. These discoveries aid in the development of storage structures that meet the ever-changing needs of modern computing, allowing for the preservation of data accessibility, security, and efficiency in the face of shifting paradigms.

Flash-based Solid State Drive (SSD) has been productively shipped and deployed in large scale storage systems. However, a single flash-based SSD cannot satisfy the capacity, performance and reliability requirements of the modern storage systems that support increasingly demanding data-intensive computing applications. Applying RAID schemes to SSDs to meet these requirements, while a logical and viable solution, faces many challenges. In this article, we propose a Hybrid Parity-based Disk Array architecture (short for HPDA), which combines a group of SSDs and two hard disk drives (HDDs) to improve the performance and reliability of SSD-based storage systems. In HPDA, the SSDs (data disks) and part of one HDD (parity disk) compose a RAID4 disk array. Meanwhile, a second HDD and the free space of the parity disk are mirrored to form a RAID1-style write buffer that temporarily absorbs the small write requests and acts as a surrogate set during recovery when a disk fails. The write data is reclaimed to the data disks during the lightly loaded or idle periods of the system. Reliability analysis shows that the reliability of HPDA, in terms of MTTDL (Mean Time To Data Loss), is better than that of either pure HDD-based or SSD-based disk array. Our prototype implementation of HPDA and the performance evaluations show that HPDA significantly outperforms either HDD-based or SSD-based disk array.

This paper explores the topic of Solid-State Drives (SSDs) and NAND flash memory. Information of this paper is mainly obtained from authoritative academic websites and papers related to SSDs and flash memory. Memory cells are fundamental components in various electronic devices. In the field of data storage, SSDs represent the latest generation of storage devices, offering significantly better performance and functionality than traditional hard disk drives (HDDs). NAND flash memory chips are crucial component of SSDs, particularly 3D NAND, which exhibits exceptional performance and holds significant potential for future advancements. This paper will commence by providing an overview of SSDs and HDDs, followed by a comparison between NAND flash and NOR flash, and ultimately analysing the future of NAND flash memory technology employed in SSDs, with a focus on elucidating the advantages of 2D NAND over 3D NAND. Additionally, this paper explains a few challenges that NAND technology faces currently, as well as some possible solutions.

Recently, solid state drives (SSDs) are replacing hard disk drives (HDDs) in datacenter storage systems in order to reduce power consumption and improve I/O performance. Additionally, in order to mitigate the performance bottleneck at I/O interface between host and SSD, the PCIe-leveraging NVMe SSD is emerging for datacenter SSDs. The NVMe interface supports the I/O virtualization mechanism called single root I/O virtualization (SR-IOV), which is a device self-virtualization technique for supporting direct paths from virtual machines (VMs) to I/O devices. Multiple virtual machines can share an SR-IOV-supporting physical device without intervention of virtual machine monitor. SR-IOV-supporting SSD should provide a device-level scheduler which can schedule the requests from multiple VMs considering performance isolation and fairness. In this paper, we propose a workload-aware budget compensation scheduling algorithm for the device-level request scheduler. To guarantee the performance isolation, the device-level scheduler estimates the contribution on the garbage collection (GC) cost of each virtual machine in the SSD device. Based on the estimated GC contributions, the budget of each VM is compensated for performance isolation. We experimented the effects of the proposed technique with an SSD simulator. The experiments showed that the scheduler can guarantee the performance isolation when multiple VMs share an NVMe SSD with different workloads.

Many high performance computing (HPC) applications are highly data intensive. Current HPC storage systems still use hard disk drives (HDDs) as their dominant storage devices, which suffer from disk head thrashing when accessing random data. New storage devices such as solid state drives (SSDs), which can handle random data access much more efficiently, have been widely deployed as the buffer to HDDs in many production HPC systems. Burst buffer has also been proposed to manage the SSD buffering of bursty write requests. Although burst buffer can improve I/O performance in many cases, we find that it has some limitations such as requiring large SSD capacity and harmonious overlapping between computation phase and data flushing stage.

Application launch times can be reduced by using low-capacity solid-state drives (SSDs) in combination with the conventional magnetic hard disk drives (HDDs). We propose a storage tiering scheme for a logically integrated hybrid disk that eliminates the requirement to store a block-mapping table on the SSDs, as well as the need to access this table for every I/O: these characteristics distinguish our scheme from most other SSD caching and driver-level tiering schemes. A heuristic identifies the disk blocks accessed during application launches, which are then moved from HDDs to SSDs by updating file-system pointers. In experiments with eight widely used applications, launch times were reduced by an average of 56 %, and our scheme outperformed the Linux DM-Cache by 6.5% on average. Because it runs as a background process, the block migration overhead is negligible. We also propose edge-block first and shortest sequence and long seek first (SSLSF) strategies for using limited SSD space effectively. Small collections of blocks, which have to be dispatched to the device driver together, are preferentially moved to the SSD especially when those blocks incur long seek times on the HDD. This produces a large reduction in HDD seek time for a small outlay of space on the SSD. In a scenario in which only 10% of launch blocks can be migrated to the SSD, the SSLSF strategy achieves average launch times for Eclipse and LibreOffice, which are 5.8% less than those exhibited by the widely used strategy of migrating short sequences of blocks first.

When applications require high I/O performance, solid-state drives (SSDs) are often preferable because they perform better than traditional hard-disk drives (HDDs). Therefore, database system response time can be improved by moving frequently used data to SSDs. However, because capacity is both more limited and more expensive for SSDs than it is for HDDs, there is a need to determine which data are most appropriate to migrate from HDDs to SSDs. In this paper, we propose a priority-based data placement method for databases using SSDs by considering an integrated mechanism and a migration rule for performing migrations between HDDs and SSDs. The proposed method can provide database systems with high I/O performance by properly moving high-priority data to SSDs with fast access capabilities while maintaining the low-priority data on HDDs, which have lower costs. The results of experiments show that the proposed method can achieve the goal while requiring only small amounts of SSD capacity.

In this paper, we proposed web performance enhancement of the e-business system using the SSD (Solid State Drive). In the e-business system, transactions must be strictly ordered, should occur at a fixed point time, and not be lost. We approached to solve e-business problem by using software and hardware techniques such as SSD. X-internet has advantage of C/S and Web applications. Since X-internet technique enables to reduce the loading time of web pages, X-internet is useful to enhance performance of the e-business system. SSD is a storage device that uses DRAM or NAND Flash as primary storage media. Since the SSD stores and accesses data directly to memory chips, which results in storage speeds far greater than conventional magnetic storage devices (HDD). Therefore x-internet techniques can be used to solve confused browsing and poor UI, and SSD can be used to solve I/O bottleneck of the e-business system.

Non-volatile random access memory (NVRAM) is a promising approach to persistent data storage with outstanding advantages over traditional storage devices, such as hard disk drives (HDDs) and solid state drives (SSDs). Some of its biggest advantages are its DRAM-like read latency and microsecond-level write latency, which are several hundred times faster than those in the original block device. However, one of the issues with using NVRAM as a storage device is designing an indexing system for its data stores to fully utilize NVRAM characteristics. The state-of-the-art indexing systems of non-volatile key-value stores are usually based on B+-trees or their variants, which were originally designed for block-based storage devices with better sequential performance than random performance. The semantics of B+-tree require data being sorted into leaf nodes and inner nodes and frequent splitting and merging to keep balanced. However, all the sorting, splitting, and merging operations cause extra write to NVRAM, which decreases its performance. In this article, we propose NV-Skiplist, a skiplist-based indexing system for key-value stores on NVRAM that fully uses the features of both NVRAM and DRAM. NV-Skiplist constructs its bottom layer in non-volatile memory to maintain data persistence and support range scans. It builds its upper layers in DRAM to retain rapid index searching and prevent consistently large overhead. We also propose a multiranged variant of NV-Skiplist to increase its search performance and scalability. We evaluate the performance of NV-Skiplist and wB+-tree which is a state-of-art scheme on an NVRAM emulator on a server with an Intel Xeon E5-2620 v2 processor. The results show that our design outperforms the original tree-based, non-volatile key-value stores up to 48%.