Energy-Saving SSD Cache Management for Video Servers with Heterogeneous HDDs

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.

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.

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.

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.

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.

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.

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.

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.

Solid-State Drives (SSDs) play a crucial role in today's storage systems. They are appended into the Hard-Disk Drives (HDDs) storage systems to improve performance. They provide high IO rate and low latency, which makes them a perfect candidate for analytic-based workloads such as MapReduce. Defining an efficient SSD deployment strategies for MapReduce workloads is a challenging task: SSDs are costly and have limited capacity, the workloads have a big process data size, and the platform has a unique workflow nature. The goal of the work is to establish performance and cost relationship between SSD approaches and MapReduce workloads. In our setup, MapReduce workloads were executed with two SSD approaches of tiering and caching each with two setups: compress and uncompress. Our results showed that by using SSD as a tier, MapReduce workload performs better by up to 66% and increased SSD lifespan by around 20% when comparing with cache approach. We also observed that applying compression on the tier approach enhanced the lifespan by 60% but reduced lifespan of cache tier by 50%.

It is critical to reduce the electric energy consumption of information systems to realize green societies. Applications like database and web applications take usage of data in storages systems of servers. In this paper, we consider RAID storage systems which are composed of multiple drives like hard disk drives (HDDs) and solid state drives (SSDs). Types of RAID storage systems, RAID0, RAID10(1+0), and RAID5 are considered in this paper. The performance and reliability of RAID storage systems are so far studied by many researchers. The more number of storage drives are possibly in parallel accessed in the RAID storage systems, the more amount of electric energy is consumed while the higher reliability and availability are supported. The electric energy consumption of the RAID storage systems to read and write data is so far not discussed. In this paper, we measure the power consumption of RAID storage systems and time to read and write data in the storage systems in order to make a power consumption model of a storage system. We make clear how much energy each type of RAID storage system consumes to sequentially and randomly read and write data through experiment in this paper.

Solid State Drives (SSD) are integrated together with Hard Disk Drives (HDD) in Hybrid Storage Systems (HSS) for Cloud environment. When it comes to storing data, some placement strategies are used to find the best location (SSD or HDD). These strategies should minimize the cost of data placement while satisfying Service Level Objectives (SLO). This paper presents two Cost based Object Placement Strategies (COPS) for DBaaS objects in HSS: a Genetic based approach (G-COPS) and an ad-hoc Heuristic approach (H-COPS) based on incremental optimization. While G-COPS proved to be closer to the optimal solution in case of small instances, H-COPS showed a better scalability as it approached the exact solution even for large instances (by 10% in average). In addition, H-COPS showed small execution times (few seconds) even for large instances which makes it a good candidate to be used in runtime. Both H-COPS and G-COPS performed better than state-of-the-art solutions as they satisfied SLOs while reducing the overall cost by more than 40% for problems of small and large instances.

Solid State Drives (SSDs) which connect NAND-flash memory in parallel is going to replace Hard Disk Drives (HDDs). The physical trait of SSD is different from HDD and it uses Flash Translation Layer (FTL) to emulate HDD. Garbage Collection is the process of making an available sphere in SSD which has no function of an over-write. In the case of general policy of FTL, both of valid and invalid pages are randomly mixed, but if it could be separated, an effect of Garbage Collection can be improved. This paper presents a double hot/cold clustering scheme that separates the frequently overwritten region from the opposite. The performance evaluation result shows that the improvement is between 44.3% in maximum and 3.9% in minimum.Keywordsgarbage collectionhot/cold clusteringpage mappingflash translation layerNAND flash memory

Log-Structure Merge tree (LSM-tree) has been one of the mainstream indexes in key-value systems supporting a variety of write-intensive Internet applications in today’s data centers. However, the performance of LSM-tree is seriously hampered by constantly occurring compaction procedures, which incur significant write amplification and degrade the write throughput. To alleviate the performance degradation caused by compactions, we introduce a lightweight compaction tree (LWC-tree), a variant of LSM-tree index optimized for minimizing the write amplification and maximizing the system throughput. The lightweight compaction drastically decreases write amplification by appending data in a table and only merging the metadata that have much smaller size. Using our proposed LWC-tree, we have implemented three key-value LWC-stores on different storage mediums including Shingled Magnetic Recording (SMR) drives, Solid State Drives (SSD), and conventional Hard Disk Drives (HDDs). The LWC-store is particularly optimized for SMR drives, as it eliminates the multiplicative I/O amplification from both LSM-trees and SMR drives. Due to the lightweight compaction procedure, LWC-store reduces the write amplification by a factor of up to 5× compared to the popular LevelDB key-value store. Moreover, the random write throughput of the LWC-tree on SMR drives is significantly improved by up to 467% even compared with LevelDB on conventional HDDs. Furthermore, LWC-tree has wide applicability and delivers impressive performance improvement in various conditions, including different storage mediums (i.e., SMR, HDD, SSD) and various value sizes and access patterns (i.e., uniform and Zipfian).

The use of Solid State Drives (SSDs) has been increased in storage systems due to high performance and low power consumption. However, some inherent properties of SSDs result in different behavior for SSDs in comparison with Hard Disk Drives (HDDs). As an inherent property, the Bit Error Rate (BER) of SSDs increases, when the number of Program/Erase (P/E) cycles arises. This increment leads to two effects in an array of SSDs during its operation: (1) different BER on different SSDs of the system (SSD-variant BER), and (2) different BER in different time moments (time-variant BER). With respect to these two effects, the reliability evaluation of SSD-based RAIDs would be different from HDD-based RAlDs. This paper introduces a simulator called SOYA for evaluating the reliability of RAH) systems regarding different BER of SSDs. Both time-variant BER and SSD-variant BER have been considered in the evaluation process of this simulator. This simulator provides the ability to compare the reliability of SSD-based RAID systems considering arbitrary parameters in the evaluation such as disk failure rate, disk repair time, RAID-controller failure rate, RAID-controller repair time, and different levels of reliability. SOYA is the first SSD-based reliability simulator for SSD-bases RAID systems which can consider all the above-mentioned parameters together in its reliability evaluation.